1. A method of calibrating erroneous parameters in a network model of a power system, comprising: determining, by a calibration system, at least one recorded oscillatory mode in a recorded disturbance data associated with a power system; identifying, by the calibration system, one or more erroneous parameters in a network model of the power system, wherein the identification comprises: generating a state space model for the network model; computing one or more attributes associated with at least one computed oscillatory mode for the network model, based on the state space model by simulation of the recorded disturbance data; comparing the one or more attributes with the at least one recorded oscillatory mode in the recorded disturbance data, to identify deviation between the at least one computed oscillatory mode and the at least one recorded oscillatory mode; and identifying one or more states, contributing to the deviation, in the network model, when the deviation is greater than a predefined threshold value, wherein one or more parameters corresponding to each of the one or more states are identified to be the one or more erroneous parameters; and adjusting, by the calibration system, the one or more erroneous parameters based on the deviation, to calibrate the network model.

2. The method as claimed in claim 1, wherein the at least one recorded oscillatory modes are computed for the recorded disturbance data by: generating frequency spectrum for the recorded disturbance data; and dividing the frequency spectrum to the one or more recorded oscillatory modes based on at least one of frequency, damping, amplitude and phase associated with the computed oscillatory modes.

3. The method as claimed in claim 1, wherein the one or more attributes, associated with the at least one computed oscillatory mode, are computed from eigenvalues obtained from the state space model of the network model.

4. The method as claimed in claim 1, wherein the one or more attributes comprises frequency data and damping data associated with the at least one computed oscillatory mode.

5. The method as claimed in claim 1, wherein the one or more states contributing to the deviation are identified from plurality of states in the network model, based on impact factor and contribution factor associated with each of the plurality of states and the least one computed oscillatory modes.

6. A calibration system to calibrate erroneous parameters in a network model of a power system, said calibration system comprises: a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, cause the processor to: determine at least one recorded oscillatory mode in a recorded disturbance data associated with a power system; identify one or more erroneous parameters in a network model of the power system, wherein the identification comprises: generate a state space model for the network model; compute one or more attributes associated with at least one computed for the network model, based on the state space model by simulation of the recorded disturbance data; compare the one or more attributes with the at least one recorded oscillatory mode in the recorded disturbance data, to identify deviation between the at least one computed oscillatory mode and the at least one recorded oscillatory mode; and identify one or more states, contributing to the deviation, in the network model, when the deviation is greater than a predefined threshold value, wherein one or more parameters corresponding to each of the one or more states are identified to be the one or more erroneous parameters; and adjust the one or more erroneous parameters based on the deviation, to calibrate the network model.

7. The calibration system as claimed in claim 6, wherein the at least one recorded oscillatory modes are computed for the recorded disturbance data by: generating frequency spectrum for the recorded disturbance data; and dividing the frequency spectrum to the one or more recorded oscillatory modes based on at least one of frequency, damping, amplitude and phase associated with the computed oscillatory modes.

8. The calibration system as claimed in claim 6, wherein the one or more attributes, associated with the at least one computed oscillatory mode, are computed from eigenvalues obtained from the state space model of the network model.

9. The calibration system as claimed in claim 6, wherein the one or more attributes comprises frequency data and damping data associated with the at least one computed oscillatory mode.

10. The calibration system as claimed in claim 6, wherein the one or more states contributing to the deviation are identified from plurality of states in the network model, based on impact factor and contribution factor associated with each of the plurality of states and the least one computed oscillatory modes. Dated this 9th Day of May, 2019 Swetha G N IN/PA-2847 Of K & S Partners Agent for the Applicant , Description:TECHNICAL FIELD The present subject matter is related in general to power system, more particularly, but not exclusively to calibrate one or more erroneous parameters in a network model of a power system. BACKGROUND Electric power systems include a plurality of components forming a network, which facilitate the generation, transmission and distribution, of electric power. Power system operators are reliant on simulations to predict stability of the network. When threats are identified to the stability of the network, counter-measures are planned to ensure the threats are mitigated. Accuracy of simulations is dependent on mathematical parameters which capture the network response, which may be referred to as network model. If models used for simulations are not capable of capturing physical response of the network, simulations used to predict grid stability may be pessimistic. This may lead to under-utilization of assets. On the other hand, incorrect simulations may be optimistic, causing unforeseen instability, which may further lead to blackouts. Hence, it is very important for network models to be periodically validated and calibrated to ensure accuracy of simulation results. As generators play a major role in stability analysis, ensuring their model accuracy is essential for realistic simulations. When stability studies are considered, along with synchronous generator model, controllers such as Automatic Voltage Regulator (AVR), Power System Stabilizer (PSS), turbine governor, over-excitation limiter and so on, need to be modelled. From some of the recorded data of previous blackouts, it may be concluded that model inaccuracies led to incorrect prediction of system stability. Optimistic predictions may not capture impending instability leading to grid-wide incidents as operators could not predict the grid’s response. This has led to grid regulations which necessitate periodic validation of the generator model. Some of conventional techniques to validate may include performing staged tests at generation stations. However, on-site model validation techniques require the generators to be “out of service”. Hence, the staged tests cannot be carried out frequently, and are normally carried out during scheduled maintenance. With the growing popularity of Phasor Measurement Units (PMUs) in the power system, the recorded data may be used to validate models at regular intervals while the generation units are in service. In this case, mismatches are identified by comparing the recorded PMU data with the simulated disturbance. The network model may be calibrated using the mismatch to identify erroneous parameters. This will ensure better model performance in simulations. However, the conventional techniques require multiple recorded disturbances to identify the erroneous parameters in the network model. Multiple disturbances are needed to distinguish between the impacts of different parameters on the simulation and to calibrate the parameters. Some of conventional methods for calibration monitor the parameters of the network with disturbance data recorded from the network. When a parameter mismatch is detected, the tuning of the erroneous parameters is performed by minimizing the error between the simulation and the recorded data. In the conventional methods, to ensure that the calibration is accurate, large amount of disturbance data is required to validate all parameters and re-calibrate them, if required. This increases the number of disturbance data-sets required to identify and calibrate the model as the search space for the erroneous parameters is quite large. The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art. SUMMARY In an embodiment, the present disclosure relates to a method to calibrate erroneous parameters in a network model of a power system. For the calibration, at least one recorded oscillatory mode is determined in a recorded disturbance associated with a power system. Further, one or more erroneous parameters are identified in a network model of the power system. The one or more erroneous parameters are identified by generating a state space model for the network model and computing one or more attributes associated with at least one computed oscillatory mode for the network model, based on the state space model. The one or more attributes are compared with the at least one recorded oscillatory mode in the recorded disturbance data, to identify deviation between the at least one computed oscillatory mode and the at least one recorded oscillatory mode. When the deviation is greater than a predefined threshold value, one or more states contributing to the deviation are identified in the network model. One or more parameters corresponding to each of the one or more states are identified to be the one or more erroneous parameters. Upon identifying the one or more erroneous parameters, the one or more erroneous parameters are adjusted based on the deviation, to calibrate the network model. In an embodiment, the present disclosure relates to a calibration system to calibrate erroneous parameters in a network model of a power system. The calibration system includes a processor and a memory communicatively coupled to the processor. The memory stores processor-executable instructions, which on execution cause the processor to calibrate the one or more erroneous parameters. For the calibration, at least one recorded oscillatory mode is determined in a recorded disturbance data associated with a power system. Further, the one or more erroneous parameters is identified in a network model of the power system. The one or more erroneous parameters are identified by generating a state space model for the network model and computing one or more attributes associated with at least one computed oscillatory mode for the network model, based on the state space model. The one or more attributes are compared with the at least one recorded oscillatory mode in the recorded disturbance data, to identify deviation between the at least one computed oscillatory mode and the at least one recorded oscillatory mode. When the deviation is greater than a predefined threshold value, one or more states contributing to the deviation are identified in the network model. One or more parameters corresponding to each of the one or more states are identified to be the one or more erroneous parameters. Upon identifying the one or more erroneous parameters, the one or more erroneous parameters are adjusted based on the deviation, to calibrate the network model. The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description. BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and regarding the accompanying figures, in which: Figure 1 shows an exemplary environment of a calibration system to calibrate erroneous parameters in network model of a power system, in accordance with some embodiments of the present disclosure; Figure 2 shows a detailed block diagram of a calibration system to calibrate erroneous parameters in network model of a power system, in accordance with some embodiments of the present disclosure; Figures 3 illustrate exemplary embodiment of a power system for identifying erroneous parameters, in accordance with some embodiments of the present disclosure; Figure 4a illustrates a flowchart showing an exemplary method to calibrate erroneous parameters in network model of a power system, in accordance with some embodiments of present disclosure; Figure 4b illustrates a flowchart showing an exemplary method to identify erroneous parameters in network model of a power system, in accordance with some embodiments of present disclosure; and Figure 5 illustrates a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure. It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether such computer or processor is explicitly shown. DETAILED DESCRIPTION In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the spirit and the scope of the disclosure. The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device, or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or method. The terms “includes”, “including”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device, or method that includes a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “includes… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or method. In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense. Present disclosure relates to a calibration system and its method for calibrating erroneous parameters in a network model of a power system. The proposed disclosure focuses on accurate identification of erroneous parameters in the network model of the power system and calibrating of identified erroneous parameters. Oscillatory modes are derived by simulating the network model using state space model. Similarly, oscillatory modes of recorded disturbance data of the network model is also determined. By comparing the oscillatory modes from the state space with the oscillatory modes from the recorded disturbance data, mismatched modes are identified. By this, search space for identified erroneous parameters is reduced. By reducing the search space, the erroneous parameters can be identified better. Also, this may result in a reduction in number of disturbances data-sets required for accurate calibration. Figure 1 shows an exemplary environment 100 associated with calibration system 101 to calibrate erroneous parameters in network model of a power system 102. The exemplary environment 100 may include the calibration system 101, the power system 102, and a communication network 103. The calibration system 101 may be configured to perform the steps of the present disclosure. The power system 102 includes networks with a plurality of components. The components may include, but are not limited to, generators, controllers, busses, transmission lines, transformers, capacitors, reactors and so on. The power system 102 may be configured for the generation, transmission and distribution of electric power. A network model may be generated for the power system 102 to indicate and illustrate, components, connectivity of components, state variables, measurements and so on, of the power system 102. The network model may be used to simulate the power system 102 and compute data/information associated with the power system 102. Further, the power system 102 may be associated with disturbance data which is recorded by monitoring real-time data associated with the power system 102. In an embodiment, the recorded disturbance data may be function of time and potentially record a disturbance in the power system 102. In an embodiment, the recorded disturbance data may include, but is not limited to voltage, current and frequency and their variation, in response to a fault in the power system 102. In an embodiment, the recorded disturbance data may be lead to temporary or permanent outage of at least one component in the power system 102. The recorded disturbance data may be pre-stored in a repository associated with the power system 102. In an embodiment, the recorded disturbance data may be pre-stored in any machine or user readable format. The recorded disturbance data may be retrieved by the calibration system 101 during the calibration of the network model. In an embodiment, the calibration system 101, may be an integral part of the power system 102. In some embodiments, the calibration system 101 may be a cloud-based server or a dedicated server in communication with the power system 102, to perform the calibration. The calibration system 101 may be configured to communicate with the power system 102 via the communication network 103. In an embodiment, the communication network 103 may include, without limitation, a direct interconnection, Local Area Network (LAN), Wide Area Network (WAN), Controller Area Network (CAN), wireless network (e.g., using Wireless Application Protocol), the Internet, and the like. Further, the calibration system 101 may include a processor 104, I/O interface 105, and a memory 106. In some embodiments, the memory 106 may be communicatively coupled to the processor 104. The memory 106 stores instructions, executable by the processor 104, which, on execution, may cause the calibration system 101 to calibrate erroneous parameters in the network model of the power system 102, as disclosed in the present disclosure. In an embodiment, the memory 106 may include one or more modules 107 and data 108. The one or more modules 107 may be configured to perform the steps of the present disclosure using the data 108, to calibrate the erroneous parameters. In an embodiment, each of the one or more modules 107 may be a hardware unit which may be outside the memory 106 and coupled with the calibration system 101. The calibration system 101 may be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a Personal Computer (PC), a notebook, a smartphone, a tablet, e-book readers, a server, a network server, a cloud-based server, and the like. In an embodiment, the calibration system 101 may receive and transmit data via the I/O interface 105 through the communication network 103. For the calibration, the calibration system 101 may be configured to determine at least one recorded oscillatory mode in a recorded disturbance data associated with a power system 102. In an embodiment, the at least one recorded oscillatory modes may be determined by generating frequency spectrum for the recorded disturbance data. The calibration system 101 may be configured to divide the frequency spectrum to the one or more recorded oscillatory modes. The frequency spectrum may be divided based on at least one of frequency, damping, amplitude and phase associated with the computed oscillatory modes. Further, the calibration system 101 may be configured to identify the one or more erroneous parameters in a network model of the power system 102. The one or more erroneous parameters are identified by generating a state space model for the network model. One or more attributes associated with at least one computed oscillatory mode for the network model is determined based on the state space model. In an embodiment, the recorded disturbance data is simulated for determining the one or more attributes. In an embodiment, the one or more attributes may be computed from eigenvalues obtained from the state space model of the network model. The one or more attributes may include, but are not limited to, frequency data and damping data associated with the at least one computed oscillatory mode. For identifying the erroneous parameters, the calibration system 101 may be configured to compare the one or more attributes with the at least one recorded oscillatory mode in the recorded disturbance data. By comparing, deviation between the at least one computed oscillatory mode and the at least one recorded oscillatory mode may be identified. In an embodiment, the deviation may be associated with frequency, damping, amplitude and phase between the at least one computed oscillatory mode and the at least one recorded oscillatory mode. Further, the identified deviation is compared with a predefined threshold value. When the deviation is lesser the predefined threshold value, the deviation may be considered negligible. The calibration system 101 may find that there are no erroneous parameters in the network model. When the deviation is greater than the predefined threshold value, the calibration system 101 may be configured to identify one or more states in the network model, which are contributing to the deviation. The one or more states contributing to the deviation are identified from plurality of states in the network model. Impact factor and contribution factor associated with each of the plurality of states and the least one computed oscillatory modes are used to identify the one or more states amongst the plurality of states. Such one or more states may be considered to faulty states contributing to instability of the power system 102. The calibration system 101 may be configured to identify the one or more parameters corresponding to each of such one or more states to be the one or more erroneous parameters. Upon identifying the one or more erroneous parameters, the one or more erroneous parameters are adjusted based on the deviation. In an embodiment, the one or more erroneous parameters may be adjusted to reduce the deviation associated with the one or more attributes. By this, calibration of parameters in the network model and, thereby accurate calibration or validation of the network model may be achieved. Figure 2 shows a detailed block diagram of the calibration system 101 to calibrate the one or more erroneous parameters in the network model of the power system 102, in accordance with some embodiments of the present disclosure. The data 108 and the one or more modules 107 in the memory 106 of the calibration system 101 is described herein in detail. In one implementation, the one or more modules 107 may include, but are not limited to, a recorded oscillatory mode determination module 201, an erroneous parameter identification module 202, an erroneous parameter adjustment module 203, and one or more other modules 204, associated with the calibration system 101. In an embodiment, the data 108 in the memory 106 may include recorded disturbance data 205, recorded oscillatory mode data 205 ( also referred to as at least one recorded oscillatory mode 205), network model data 207 (also referred to as network model 207), state space data 208 (also referred to as state space model 208), computed oscillatory mode data 209 ( also referred to as at least one computed oscillatory mode 209), computed oscillatory mode attributes 210 (also referred to as one or more attributes 210), deviation data 211 (also referred to deviation 211), deviation contributing states data 212 (also referred to as one or more states 212), erroneous parameters 213 (also referred to as one or more erroneous parameters 213), adjusted erroneous parameters 214 and other data 215 associated with the calibration system 101. In an embodiment, the data 108 in the memory 106 may be processed by the one or more modules 107 of the calibration system 101. In an embodiment, the one or more modules 107 may be implemented as dedicated units and when implemented in such a manner, said modules may be configured with the functionality defined in the present disclosure to result in a novel hardware. As used herein, the term module may refer to an Application Specific Integrated Circuit (ASIC), an electronic circuit, a Field-Programmable Gate Arrays (FPGA), Programmable System-on-Chip (PSoC), a combinational logic circuit, and/or other suitable components that provide the described functionality. The one or more modules 107 of the present disclosure function to calibrate the one or more erroneous parameters 213. The one or more modules 107 along with the data 108, may be implemented in any system, for calibrating the one or more erroneous parameters 213. For the calibration, the recorded oscillatory mode determination module 201 of the calibration system 101 may be configured to determine the at least one recorded oscillatory mode 206 in the recorded disturbance data 205. In an embodiment, the recorded disturbance data 205 may be recorded for the power system 102 using known techniques. The recorded disturbance data 205 may be stored in the repository associated with the power system 102. In an embodiment, the recorded disturbance data 205 may be retrieved from the repository, for the calibration. In an embodiment, the disturbance data 205 may be recorded and stored in the memory 106 of the calibration system 101. In an embodiment, to determine the at least one recorded oscillatory mode 206, the recorded oscillatory mode determination module 201 may be configured to generate the frequency spectrum for the recorded disturbance data 205. The frequency spectrum may be divided into smaller windows, where each of the windows corresponds to an oscillatory mode from the at least one recorded oscillatory mode 206. Obtaining the frequency spectrum may be done using one or more techniques, known to a person skilled in the art. The one or more techniques, may include, but are not limited to, Fourier Transform, Prony Analysis, Matrix Pencil, Eigenvalue Realization Algorithm, Koopman Modes and so on. In the present disclosure, the at least one recorded oscillatory mode 206 is reference, which is compared with the at least one computed oscillatory mode 209, for identifying the one or more erroneous parameters 213. Upon determining the at least one recorded oscillatory mode 206, the one or more erroneous parameters 213 are identified in the network model. The erroneous parameter identification module 202 may be configured to identify the one or more erroneous parameters 213. Initially, oscillatory modes for the network model are computed. The erroneous parameter identification module 202 may compute oscillatory modes for the network model. In an embodiment, the at least one computed oscillatory mode 209 for the network model may be computed using the state space. The state-space model is used to determine oscillatory frequencies and damping associated with the power system 102. The state space model is used to formulate system matrix of the power system 102 with plurality of generators with corresponding controllers. Eigenvalues are derived from the system matrix to yield the one or more attributes 210 i.e., the frequency data and the damping data associated with the at least one computed oscillatory mode 209. For example, the state space model for a power system may be defined as shown below: [X ? ]= [A][X]+ [B][U] ………. (1) [Y]= [C][X]+ [D][U] ………. (2) where, (X ) ?is first derivative of plurality of states of the power system, with respect to time, indicating change in state; A is the system matrix that relates the plurality of states to the state space model; X is system state vector; B is control matrix which relates the change in each of the plurality of states with corresponding inputs; U is vector of inputs to the power system; Y is output vector of the power system; C is output matrix which relates output to each of the plurality of states of the power system; and D is feedforward matrix which relates the input to the output of the power system. The stability of the power system depends on the input given to the power system, and in case of non-linear systems, the stability depends on the initial state before the input is given. In present disclosure, the input is the disturbance in the power system and the system being studied may be a generator with its controllers. The inputs are in the form of voltage and frequency, and the output is the power, both real and reactive from the one or more generators. The number of states and the system matrix depend on structure and level of detail in the network model. Additional controllers also add their own states to the state-space representation/model. In an embodiment, the system to be studied may be extended to an area which two or more generators and corresponding controllers. Once the state-space model is generated for the network model, eigenvalues of the system matrix (A) may provide the frequency data and the damping data of the at least one computed mode. The eigenvalues are computed by solving the characteristic equation shown below: |A- ?I|=0 ……….. (3) where, A is the system matrix; I is identity matrix which is of same dimensions as that of A; and ? = ?1, ?2, ?3……… represent the eigenvalues. The eigenvalues may be real or complex. In case of complex eigenvalues, the damping data is real part of the eigenvalue and angular frequency is imaginary part. By comparing these values with the frequency components computed from the at least one recorded oscillatory modes, the deviation 211 in the time response of the network model may be determined. Further, the identified deviation 211 is compared with a predefined threshold value. When the deviation 211 is lesser the predefined threshold value, the deviation 211 may be considered negligible. The calibration system 101 may identify that is no erroneous parameters in the network parameters. When the deviation 211 is greater than the predefined threshold value, the calibration system 101 may be configured to identify one or more states 212 in the network model, which are contributing to the deviation 211. The one or more states contributing to the deviation 211 are identified from plurality of states in the network model. Consider the power system illustrated in Figure 4. The power system is a two area 300.1 and 300.2, four generators 301.1-301.4 and eleven bus 302.1-302.11 system. The system matrix for generator 301.4 may be derived as illustrated in Table 1 below: States 1 2 3 4 5 6 7 8 9 10 ?? 0.00 -0.21 -0.07 -0.07 -0.01 -0.11 0.00 0.00 0.00 0.00 ?d 377.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ??fd 0.00 -0.18 -1.20 0.85 0.00 0.00 -26.7 0.00 0.00 26.67 ??1d 0.00 -5.97 26.65 -38.2 0.01 0.04 0.00 0.00 0.00 0.00 ??1q 0.00 -0.91 0.00 0.00 -9.72 7.82 0.00 0.00 0.00 0.00 ??2q 0.00 -11.9 -0.05 -0.05 13.06 -37.8 0.00 0.00 0.00 0.00 ?v1 0.00 -7.23 12.98 13.84 -2.61 -20.4 -100 0.00 0.00 0.00 ?v2 0.00 -4.20 -1.31 -1.40 -0.28 -2.22 0.00 -0.10 0.00 0.00 ?v3 0.00 -10.5 -3.28 -3.50 -0.71 -5.56 0.00 49.75 -50 0.00 ?vs 0.00 -5.83 -1.82 -1.94 -0.39 -3.09 0.00 27.64 -27.6 -0.19 Table 1 Accordingly, the eigenvalues computed using the characteristic equation (3) are as given in table 3 below: Eigenvalue Frequency data (Hz) Damping data ?1 -98.1229 + 0.0000i - - ?2 -50.6727 + 0.0000i - - ?3 -40.7842 + 0.0000i - - ?4 -32.0827 + 0.0000i - - ?5 -1.9599 + 9.9132i 1.58 0.19 ?6 -1.9599 - 9.9132i 1.58 0.19 ?7 -8.3786 + 0.0000i - - ?8 -2.8995 + 0.0000i - - ?9 -0.2098 + 0.1102i 0.018 0.89 ?10 -0.2098 - 0.1102i 0.018 0.89 Table 3 From Table 3, the deviation 211 between the at least one recorded oscillatory mode 206 and at least one computed oscillatory mode 209 is identified. Further, the erroneous parameter identification module 202 is configured to identify the one or more states 212 associated with the deviation 211. Impact factor and contribution factor associated with each of the plurality of states and the least one computed oscillatory mode 209 are used to identify the one or more states 212 amongst the plurality of states of the power system. One or more techniques, known to a person skilled in the art, may be used to identify the one or more states 212. In an embodiment, participation factor for each of the plurality of states may be computed based on corresponding impact factor and contribution factor. In an exemplary embodiment, the participation factor for each of the plurality of states may be derived from left eigenvector and right eigenvector associated with the power system. The left eigenvector and the right eigenvector may be computed for each eigenvalue, which satisfy conditions in equations 4 and 5 below: A f_i= ?_i f_i ………. (4) ?_i A= ?_i ?_i ………. (5) where, ?i is ith eigenvalue; f represents right eigenvector; and ? represents the left eigenvector. The right eigenvector is used to isolate the impact factor of states on the computed oscillatory modes, and the left eigenvector provides the information regarding the contribution made by each of the plurality of state to the at least one computed oscillatory mode 209. By combining the right eigenvector and the left eigenvector, the participation factor for each of the plurality of states is determined. In another exemplary embodiment, the participation factor may be computed using equation 6 given below: p_ki= f_ki*?_ik ………. (6) where, p_kiis the participation factor of kth state for ith eigenvalue; f_kiis the right eigenvector of kth state for ith eigenvalue; and ?_ikis the left eigenvector of kth state for ith eigenvalue. One or more other techniques, known to person skilled in the art, may be implemented in the present disclosure to determine the participation factor. By using the participation factor, the impact of the plurality of states on each the at least one computed oscillatory mode 209 may be determined and thereby the one or more states 212 contributing to the deviation 211 may be determined. Thus, some of parameters associated with such one or more states 212 may be identified to be the one or more erroneous parameters 213. By identifying said one or more states 212, the search space for identifying the one or more erroneous parameters 213 may be minimized. Subsequently, the erroneous parameter adjustment module 203 may be configured to adjust such one or more erroneous parameters and reduce the deviation. Further, the one or more erroneous parameters 213 which are adjusted may be stored as the adjusted erroneous parameters 214 in the memory of the calibration system 101. The network model may be updated with the adjusted erroneous parameters 214 as a counter measure to handle stability in the power system. In some embodiment, upon updating the network model with the adjusted erroneous parameters, the calibration proposed in the present disclosure may be repeated to monitor the deviation. The steps of the proposed method may be performed in an iterative manner, to reduce the deviation/ mismatch in the power system. The proposed calibration system may be implemented to improve generator models used in all dynamic simulations. The proposed calibration system may be implements in products related to dynamic security assessment, special protection schemes, decision support systems and so on. The other data 215 may store data, including temporary data and temporary files, generated by modules for performing the various functions of the calibration system 101. The one or more modules 107 may also include other modules 204 to perform various miscellaneous functionalities of the calibration system 101. It will be appreciated that such modules may be represented as a single module or a combination of different modules. Figure 4a illustrates a flowchart showing an exemplary method to calibrate the one or more erroneous parameters, in accordance with some embodiments of present disclosure. At block 401, the recorded oscillatory mode determination module 201 may be configured to determine the at least one recorded oscillatory mode 206 in the recorded disturbance data 205 associated with the power system 102. In an embodiment, the at least one recorded oscillatory mode 206 may be determined by generating frequency spectrum for the at least one recorded disturbance data 206 and dividing the frequency spectrum to the at least one recorded oscillatory modes 206. The frequency spectrum may be divided based on at least one of frequency, damping, amplitude and phase associated with the at least one computed oscillatory modes 209. At block 402, the erroneous parameter identification module 202 may be configured to identify the one or more erroneous parameters 213 in the network model of the power system 102. Figure 4b illustrates a flowchart showing an exemplary method to identify the one or more erroneous parameters 213 in the network model of the power system 102, in accordance with some embodiments of present disclosure. At block 404, the erroneous parameter identification module 202 may be configured to generate a state space model for the network model. One or more techniques, known to a person skilled in the art, may be implemented for generating the state space model. At block 405, the erroneous parameter identification module 202 may be configured to compute the one or more attributes 210 associated with the at least one computed oscillatory mode 209. In an embodiment, the one or more attributes 210 are computed from eigenvalues obtained from the state space model of the network model. At block 406, the erroneous parameter identification module 202 may be configured to compare the one or more attributes 210 with the at least one recorded oscillatory mode 206 to identify deviation 211. The identified deviation 211 indicates deviation between the at least one recorded oscillatory mode 206 and the at least one computed oscillatory mode 209. At block 407, the erroneous parameter identification module 202 may be configured to check if the deviation 211is greater than the predefined threshold value. If the deviation 211 is greater than the predefined threshold value, step in block 408 is performed. If the deviation 211 is lesser than the predefined threshold value, step in block 406 is repeated, to identify further deviation. At block 408, when the deviation 211 is greater than the predefined threshold value, the erroneous parameter identification module 202 may be configured to identify the one or more states 212 contributing to the deviation 211. One or more techniques, known to a person skilled in the art, may be used to identify the one or more states 212. At block 409, the erroneous parameter identification module 202 may be configured to identify the one or more parameters associated with the one or more states 212 to be the one or more erroneous parameters 213. Referring back to Figure 4a, at block 403, the erroneous parameter adjustment module 203 may be configured to adjust the one or more erroneous parameters based on the deviation 211. In an embodiment, the one or more erroneous parameters 213 may be adjusted to reduce the deviation 211. As illustrated in Figures 4a and 4b, the methods 400 and 402 may include one or more blocks for executing processes in the calibration system 101. The methods 400 and 402 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions or implement particular abstract data types. The order in which the methods 400 and 402 are described may not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. Computing System Figure 5 illustrates a block diagram of an exemplary computer system 500 for implementing embodiments consistent with the present disclosure. In an embodiment, the computer system 500 is used to implement the calibration system 101. The computer system 500 may include a central processing unit (“CPU” or “processor”) 502. The processor 502 may include at least one data processor for executing processes in Virtual Storage Area Network. The processor 502 may include specialized processing units such as, integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc. The processor 502 may be disposed in communication with one or more input/output (I/O) devices 509 and 510 via I/O interface 501. The I/O interface 501 may employ communication protocols/methods such as, without limitation, audio, analog, digital, monaural, RCA, stereo, IEEE-1394, serial bus, universal serial bus (USB), infrared, PS/2, BNC, coaxial, component, composite, digital visual interface (DVI), high-definition multimedia interface (HDMI), RF antennas, S-Video, VGA, IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., code-division multiple access (CDMA), high-speed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMax, or the like), etc. Using the I/O interface 501, the computer system 500 may communicate with one or more I/O devices 509 and 510. For example, the input devices 509 may be an antenna, keyboard, mouse, joystick, (infrared) remote control, camera, card reader, fax machine, dongle, biometric reader, microphone, touch screen, touchpad, trackball, stylus, scanner, storage device, transceiver, video device/source, etc. The output devices 510 may be a printer, fax machine, video display (e.g., cathode ray tube (CRT), liquid crystal display (LCD), light-emitting diode (LED), plasma, Plasma display panel (PDP), Organic light-emitting diode display (OLED) or the like), audio speaker, etc. In some embodiments, the computer system 500 may consist of the calibration system 101. The processor 502 may be disposed in communication with the communication network 511 via a network interface 503. The network interface 503 may communicate with the communication network 511. The network interface 503 may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), transmission control protocol/internet protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. The communication network 511 may include, without limitation, a direct interconnection, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, etc. Using the network interface 503 and the communication network 511, the computer system 500 may communicate with power system 512 for calibrating the one or more erroneous parameters. The network interface 503 may employ connection protocols include, but not limited to, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), transmission control protocol/internet protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. The communication network 511 includes, but is not limited to, a direct interconnection, an e-commerce network, a peer to peer (P2P) network, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, Wi-Fi, and such. The first network and the second network may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the first network and the second network may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc. In some embodiments, the processor 502 may be disposed in communication with a memory 505 (e.g., RAM, ROM, etc. not shown in Figure 5) via a storage interface 504. The storage interface 504 may connect to memory 505 including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as, serial advanced technology attachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394, Universal Serial Bus (USB), fibre channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc. The memory 505 may store a collection of program or database components, including, without limitation, user interface 506, an operating system 507 etc. In some embodiments, computer system 500 may store user/application data 506, such as, the data, variables, records, etc., as described in this disclosure. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle ® or Sybase®. The operating system 507 may facilitate resource management and operation of the computer system 500. Examples of operating systems include, without limitation, APPLE MACINTOSH® OS X, UNIX®, UNIX-like system distributions (E.G., BERKELEY SOFTWARE DISTRIBUTIONTM (BSD), FREEBSDTM, NETBSDTM, OPENBSDTM, etc.), LINUX DISTRIBUTIONSTM (E.G., RED HATTM, UBUNTUTM, KUBUNTUTM, etc.), IBMTM OS/2, MICROSOFTTM WINDOWSTM (XPTM, VISTATM/7/8, 10 etc.), APPLE® IOSTM, GOOGLE® ANDROIDTM, BLACKBERRY® OS, or the like. Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, non-volatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media. Advantages An embodiment of the present disclosure provisions a solution for difficulty in identifying erroneous parameters in existing disturbance-based techniques by making use of oscillatory modes. An embodiment of the present disclosure provisions to compute oscillatory modes for better identification of erroneous parameters. Accurate identification of the erroneous parameters may be achieved by comparing oscillatory modes of recorded disturbance data and network model. An embodiment of the present disclosure provisions to reduce search space i.e., number of data-sets of disturbances data required to accurately identify the erroneous parameters. By which, number of model parameters which are to be calibrated is reduced. The described operations may be implemented as a method, system or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof. The described operations may be implemented as code maintained in a “non-transitory computer readable medium”, where a processor may read and execute the code from the computer readable medium. The processor is at least one of a microprocessors and a processor capable of processing and executing the queries. A non-transitory computer readable medium may include media such as magnetic storage medium (e.g., hard disk drives, floppy disks, tape, etc.), optical storage (CD-ROMs, DVDs, optical disks, etc.), volatile and non-volatile memory devices (e.g., EEPROMs, ROMs, PROMs, RAMs, DRAMs, SRAMs, Flash Memory, firmware, programmable logic, etc.), etc. Further, non-transitory computer-readable media may include all computer-readable media except for a transitory. The code implementing the described operations may further be implemented in hardware logic (e.g., an integrated circuit chip, Programmable Gate Array (PGA), Application Specific Integrated Circuit (ASIC), etc.). An “article of manufacture” includes non-transitory computer readable medium, and /or hardware logic, in which code may be implemented. A device in which the code implementing the described embodiments of operations is encoded may include a computer readable medium or hardware logic. Of course, those skilled in the art will recognize that many modifications may be made to this configuration without departing from the scope of the invention, and that the article of manufacture may include suitable information bearing medium known in the art. The terms “an embodiment”, “embodiment”, “embodiments”, “the embodiment”, “the embodiments”, “one or more embodiments”, “some embodiments”, and “one embodiment” mean “one or more (but not all) embodiments of the invention(s)” unless expressly specified otherwise. The terms “including”, “comprising”, “having” and variations thereof mean “including but not limited to”, unless expressly specified otherwise. The enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise. The terms “a”, “an” and “the” mean “one or more”, unless expressly specified otherwise. A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention. When a single device or article is described herein, it will be readily apparent that more than one device/article (whether or not they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be readily apparent that a single device/article may be used in place of the more than one device or article or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself. The illustrated operations of Figures 4a and 4b show certain events occurring in a certain order. In alternative embodiments, certain operations may be performed in a different order, modified, or removed. Moreover, steps may be added to the above described logic and still conform to the described embodiments. Further, operations described herein may occur sequentially or certain operations may be processed in parallel. Yet further, operations may be performed by a single processing unit or by distributed processing units. Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims. While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims. Referral numerals: Reference Number Description 100 Environment 101 Calibration system 102 Power system 103 Communication network 104 Processor 105 I/O interface 106 Memory 107 Modules 108 Data 201 Recorded oscillatory mode determination module 202 Erroneous parameters identification module 203 Erroneous parameters adjustment module 204 Other modules 205 Recorded disturbance data 206 Recorded oscillatory mode data 207 Network model data 208 State space model data 209 Computed oscillatory mode data 210 Computed oscillatory mode attributes 211 Deviation data 212 Deviation contributing states data 213 Erroneous parameters 214 Adjusted erroneous parameters 215 Other data 301.1-301.4 Generators 302.1-302.11 One or more states 500 Computer System 501 I/O Interface 502 Processor 503 Network Interface 504 Storage Interface 505 Memory 506 User Interface 507 Operating System 508 Web Server 509 Input Devices 510 Output Devices 511 Communication Network 512 Power system

Claims:We claim:
1. A method of calibrating erroneous parameters in a network model of a power system, comprising:
determining, by a calibration system, at least one recorded oscillatory mode in a recorded disturbance data associated with a power system;
identifying, by the calibration system, one or more erroneous parameters in a network model of the power system, wherein the identification comprises:
generating a state space model for the network model;
computing one or more attributes associated with at least one computed oscillatory mode for the network model, based on the state space model by simulation of the recorded disturbance data;
comparing the one or more attributes with the at least one recorded oscillatory mode in the recorded disturbance data, to identify deviation between the at least one computed oscillatory mode and the at least one recorded oscillatory mode; and
identifying one or more states, contributing to the deviation, in the network model, when the deviation is greater than a predefined threshold value, wherein one or more parameters corresponding to each of the one or more states are identified to be the one or more erroneous parameters; and
adjusting, by the calibration system, the one or more erroneous parameters based on the deviation, to calibrate the network model.

2. The method as claimed in claim 1, wherein the at least one recorded oscillatory modes are computed for the recorded disturbance data by:
generating frequency spectrum for the recorded disturbance data; and
dividing the frequency spectrum to the one or more recorded oscillatory modes based on at least one of frequency, damping, amplitude and phase associated with the computed oscillatory modes.

3. The method as claimed in claim 1, wherein the one or more attributes, associated with the at least one computed oscillatory mode, are computed from eigenvalues obtained from the state space model of the network model.

4. The method as claimed in claim 1, wherein the one or more attributes comprises frequency data and damping data associated with the at least one computed oscillatory mode.

5. The method as claimed in claim 1, wherein the one or more states contributing to the deviation are identified from plurality of states in the network model, based on impact factor and contribution factor associated with each of the plurality of states and the least one computed oscillatory modes.

6. A calibration system to calibrate erroneous parameters in a network model of a power system, said calibration system comprises:
a processor; and
a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, cause the processor to:
determine at least one recorded oscillatory mode in a recorded disturbance data associated with a power system;
identify one or more erroneous parameters in a network model of the power system, wherein the identification comprises:
generate a state space model for the network model;
compute one or more attributes associated with at least one computed for the network model, based on the state space model by simulation of the recorded disturbance data;
compare the one or more attributes with the at least one recorded oscillatory mode in the recorded disturbance data, to identify deviation between the at least one computed oscillatory mode and the at least one recorded oscillatory mode; and
identify one or more states, contributing to the deviation, in the network model, when the deviation is greater than a predefined threshold value, wherein one or more parameters corresponding to each of the one or more states are identified to be the one or more erroneous parameters; and
adjust the one or more erroneous parameters based on the deviation, to calibrate the network model.
7. The calibration system as claimed in claim 6, wherein the at least one recorded oscillatory modes are computed for the recorded disturbance data by:
generating frequency spectrum for the recorded disturbance data; and
dividing the frequency spectrum to the one or more recorded oscillatory modes based on at least one of frequency, damping, amplitude and phase associated with the computed oscillatory modes.

8. The calibration system as claimed in claim 6, wherein the one or more attributes, associated with the at least one computed oscillatory mode, are computed from eigenvalues obtained from the state space model of the network model.

9. The calibration system as claimed in claim 6, wherein the one or more attributes comprises frequency data and damping data associated with the at least one computed oscillatory mode.

10. The calibration system as claimed in claim 6, wherein the one or more states contributing to the deviation are identified from plurality of states in the network model, based on impact factor and contribution factor associated with each of the plurality of states and the least one computed oscillatory modes.

Dated this 9th Day of May, 2019

Swetha G N
IN/PA-2847
Of K & S Partners
Agent for the Applicant

, Description:TECHNICAL FIELD

The present subject matter is related in general to power system, more particularly, but not exclusively to calibrate one or more erroneous parameters in a network model of a power system.

BACKGROUND

Electric power systems include a plurality of components forming a network, which facilitate the generation, transmission and distribution, of electric power. Power system operators are reliant on simulations to predict stability of the network. When threats are identified to the stability of the network, counter-measures are planned to ensure the threats are mitigated. Accuracy of simulations is dependent on mathematical parameters which capture the network response, which may be referred to as network model. If models used for simulations are not capable of capturing physical response of the network, simulations used to predict grid stability may be pessimistic. This may lead to under-utilization of assets. On the other hand, incorrect simulations may be optimistic, causing unforeseen instability, which may further lead to blackouts. Hence, it is very important for network models to be periodically validated and calibrated to ensure accuracy of simulation results.

As generators play a major role in stability analysis, ensuring their model accuracy is essential for realistic simulations. When stability studies are considered, along with synchronous generator model, controllers such as Automatic Voltage Regulator (AVR), Power System Stabilizer (PSS), turbine governor, over-excitation limiter and so on, need to be modelled. From some of the recorded data of previous blackouts, it may be concluded that model inaccuracies led to incorrect prediction of system stability. Optimistic predictions may not capture impending instability leading to grid-wide incidents as operators could not predict the grid’s response. This has led to grid regulations which necessitate periodic validation of the generator model.

Some of conventional techniques to validate may include performing staged tests at generation stations. However, on-site model validation techniques require the generators to be “out of service”. Hence, the staged tests cannot be carried out frequently, and are normally carried out during scheduled maintenance. With the growing popularity of Phasor Measurement Units (PMUs) in the power system, the recorded data may be used to validate models at regular intervals while the generation units are in service. In this case, mismatches are identified by comparing the recorded PMU data with the simulated disturbance. The network model may be calibrated using the mismatch to identify erroneous parameters. This will ensure better model performance in simulations. However, the conventional techniques require multiple recorded disturbances to identify the erroneous parameters in the network model. Multiple disturbances are needed to distinguish between the impacts of different parameters on the simulation and to calibrate the parameters.

Some of conventional methods for calibration monitor the parameters of the network with disturbance data recorded from the network. When a parameter mismatch is detected, the tuning of the erroneous parameters is performed by minimizing the error between the simulation and the recorded data. In the conventional methods, to ensure that the calibration is accurate, large amount of disturbance data is required to validate all parameters and re-calibrate them, if required. This increases the number of disturbance data-sets required to identify and calibrate the model as the search space for the erroneous parameters is quite large.

The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

SUMMARY

In an embodiment, the present disclosure relates to a method to calibrate erroneous parameters in a network model of a power system. For the calibration, at least one recorded oscillatory mode is determined in a recorded disturbance associated with a power system. Further, one or more erroneous parameters are identified in a network model of the power system. The one or more erroneous parameters are identified by generating a state space model for the network model and computing one or more attributes associated with at least one computed oscillatory mode for the network model, based on the state space model. The one or more attributes are compared with the at least one recorded oscillatory mode in the recorded disturbance data, to identify deviation between the at least one computed oscillatory mode and the at least one recorded oscillatory mode. When the deviation is greater than a predefined threshold value, one or more states contributing to the deviation are identified in the network model. One or more parameters corresponding to each of the one or more states are identified to be the one or more erroneous parameters. Upon identifying the one or more erroneous parameters, the one or more erroneous parameters are adjusted based on the deviation, to calibrate the network model.

In an embodiment, the present disclosure relates to a calibration system to calibrate erroneous parameters in a network model of a power system. The calibration system includes a processor and a memory communicatively coupled to the processor. The memory stores processor-executable instructions, which on execution cause the processor to calibrate the one or more erroneous parameters. For the calibration, at least one recorded oscillatory mode is determined in a recorded disturbance data associated with a power system. Further, the one or more erroneous parameters is identified in a network model of the power system. The one or more erroneous parameters are identified by generating a state space model for the network model and computing one or more attributes associated with at least one computed oscillatory mode for the network model, based on the state space model. The one or more attributes are compared with the at least one recorded oscillatory mode in the recorded disturbance data, to identify deviation between the at least one computed oscillatory mode and the at least one recorded oscillatory mode. When the deviation is greater than a predefined threshold value, one or more states contributing to the deviation are identified in the network model. One or more parameters corresponding to each of the one or more states are identified to be the one or more erroneous parameters. Upon identifying the one or more erroneous parameters, the one or more erroneous parameters are adjusted based on the deviation, to calibrate the network model.

The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and regarding the accompanying figures, in which:

Figure 1 shows an exemplary environment of a calibration system to calibrate erroneous parameters in network model of a power system, in accordance with some embodiments of the present disclosure;

Figure 2 shows a detailed block diagram of a calibration system to calibrate erroneous parameters in network model of a power system, in accordance with some embodiments of the present disclosure;

Figures 3 illustrate exemplary embodiment of a power system for identifying erroneous parameters, in accordance with some embodiments of the present disclosure;

Figure 4a illustrates a flowchart showing an exemplary method to calibrate erroneous parameters in network model of a power system, in accordance with some embodiments of present disclosure;

Figure 4b illustrates a flowchart showing an exemplary method to identify erroneous parameters in network model of a power system, in accordance with some embodiments of present disclosure; and

Figure 5 illustrates a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.

It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether such computer or processor is explicitly shown.

DETAILED DESCRIPTION

In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the spirit and the scope of the disclosure.

The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device, or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or method.

The terms “includes”, “including”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device, or method that includes a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “includes… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or method.

In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

Present disclosure relates to a calibration system and its method for calibrating erroneous parameters in a network model of a power system. The proposed disclosure focuses on accurate identification of erroneous parameters in the network model of the power system and calibrating of identified erroneous parameters. Oscillatory modes are derived by simulating the network model using state space model. Similarly, oscillatory modes of recorded disturbance data of the network model is also determined. By comparing the oscillatory modes from the state space with the oscillatory modes from the recorded disturbance data, mismatched modes are identified. By this, search space for identified erroneous parameters is reduced. By reducing the search space, the erroneous parameters can be identified better. Also, this may result in a reduction in number of disturbances data-sets required for accurate calibration.

Figure 1 shows an exemplary environment 100 associated with calibration system 101 to calibrate erroneous parameters in network model of a power system 102. The exemplary environment 100 may include the calibration system 101, the power system 102, and a communication network 103. The calibration system 101 may be configured to perform the steps of the present disclosure. The power system 102 includes networks with a plurality of components. The components may include, but are not limited to, generators, controllers, busses, transmission lines, transformers, capacitors, reactors and so on. The power system 102 may be configured for the generation, transmission and distribution of electric power. A network model may be generated for the power system 102 to indicate and illustrate, components, connectivity of components, state variables, measurements and so on, of the power system 102. The network model may be used to simulate the power system 102 and compute data/information associated with the power system 102. Further, the power system 102 may be associated with disturbance data which is recorded by monitoring real-time data associated with the power system 102. In an embodiment, the recorded disturbance data may be function of time and potentially record a disturbance in the power system 102. In an embodiment, the recorded disturbance data may include, but is not limited to voltage, current and frequency and their variation, in response to a fault in the power system 102. In an embodiment, the recorded disturbance data may be lead to temporary or permanent outage of at least one component in the power system 102. The recorded disturbance data may be pre-stored in a repository associated with the power system 102. In an embodiment, the recorded disturbance data may be pre-stored in any machine or user readable format. The recorded disturbance data may be retrieved by the calibration system 101 during the calibration of the network model.

In an embodiment, the calibration system 101, may be an integral part of the power system 102. In some embodiments, the calibration system 101 may be a cloud-based server or a dedicated server in communication with the power system 102, to perform the calibration. The calibration system 101 may be configured to communicate with the power system 102 via the communication network 103. In an embodiment, the communication network 103 may include, without limitation, a direct interconnection, Local Area Network (LAN), Wide Area Network (WAN), Controller Area Network (CAN), wireless network (e.g., using Wireless Application Protocol), the Internet, and the like.

Further, the calibration system 101 may include a processor 104, I/O interface 105, and a memory 106. In some embodiments, the memory 106 may be communicatively coupled to the processor 104. The memory 106 stores instructions, executable by the processor 104, which, on execution, may cause the calibration system 101 to calibrate erroneous parameters in the network model of the power system 102, as disclosed in the present disclosure. In an embodiment, the memory 106 may include one or more modules 107 and data 108. The one or more modules 107 may be configured to perform the steps of the present disclosure using the data 108, to calibrate the erroneous parameters. In an embodiment, each of the one or more modules 107 may be a hardware unit which may be outside the memory 106 and coupled with the calibration system 101. The calibration system 101 may be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a Personal Computer (PC), a notebook, a smartphone, a tablet, e-book readers, a server, a network server, a cloud-based server, and the like. In an embodiment, the calibration system 101 may receive and transmit data via the I/O interface 105 through the communication network 103.

For the calibration, the calibration system 101 may be configured to determine at least one recorded oscillatory mode in a recorded disturbance data associated with a power system 102. In an embodiment, the at least one recorded oscillatory modes may be determined by generating frequency spectrum for the recorded disturbance data. The calibration system 101 may be configured to divide the frequency spectrum to the one or more recorded oscillatory modes. The frequency spectrum may be divided based on at least one of frequency, damping, amplitude and phase associated with the computed oscillatory modes.

Further, the calibration system 101 may be configured to identify the one or more erroneous parameters in a network model of the power system 102. The one or more erroneous parameters are identified by generating a state space model for the network model. One or more attributes associated with at least one computed oscillatory mode for the network model is determined based on the state space model. In an embodiment, the recorded disturbance data is simulated for determining the one or more attributes. In an embodiment, the one or more attributes may be computed from eigenvalues obtained from the state space model of the network model. The one or more attributes may include, but are not limited to, frequency data and damping data associated with the at least one computed oscillatory mode.

For identifying the erroneous parameters, the calibration system 101 may be configured to compare the one or more attributes with the at least one recorded oscillatory mode in the recorded disturbance data. By comparing, deviation between the at least one computed oscillatory mode and the at least one recorded oscillatory mode may be identified. In an embodiment, the deviation may be associated with frequency, damping, amplitude and phase between the at least one computed oscillatory mode and the at least one recorded oscillatory mode.

Further, the identified deviation is compared with a predefined threshold value. When the deviation is lesser the predefined threshold value, the deviation may be considered negligible. The calibration system 101 may find that there are no erroneous parameters in the network model. When the deviation is greater than the predefined threshold value, the calibration system 101 may be configured to identify one or more states in the network model, which are contributing to the deviation. The one or more states contributing to the deviation are identified from plurality of states in the network model. Impact factor and contribution factor associated with each of the plurality of states and the least one computed oscillatory modes are used to identify the one or more states amongst the plurality of states. Such one or more states may be considered to faulty states contributing to instability of the power system 102. The calibration system 101 may be configured to identify the one or more parameters corresponding to each of such one or more states to be the one or more erroneous parameters.

Upon identifying the one or more erroneous parameters, the one or more erroneous parameters are adjusted based on the deviation. In an embodiment, the one or more erroneous parameters may be adjusted to reduce the deviation associated with the one or more attributes. By this, calibration of parameters in the network model and, thereby accurate calibration or validation of the network model may be achieved.

Figure 2 shows a detailed block diagram of the calibration system 101 to calibrate the one or more erroneous parameters in the network model of the power system 102, in accordance with some embodiments of the present disclosure.

The data 108 and the one or more modules 107 in the memory 106 of the calibration system 101 is described herein in detail.

In one implementation, the one or more modules 107 may include, but are not limited to, a recorded oscillatory mode determination module 201, an erroneous parameter identification module 202, an erroneous parameter adjustment module 203, and one or more other modules 204, associated with the calibration system 101.

In an embodiment, the data 108 in the memory 106 may include recorded disturbance data 205, recorded oscillatory mode data 205 ( also referred to as at least one recorded oscillatory mode 205), network model data 207 (also referred to as network model 207), state space data 208 (also referred to as state space model 208), computed oscillatory mode data 209 ( also referred to as at least one computed oscillatory mode 209), computed oscillatory mode attributes 210 (also referred to as one or more attributes 210), deviation data 211 (also referred to deviation 211), deviation contributing states data 212 (also referred to as one or more states 212), erroneous parameters 213 (also referred to as one or more erroneous parameters 213), adjusted erroneous parameters 214 and other data 215 associated with the calibration system 101.

In an embodiment, the data 108 in the memory 106 may be processed by the one or more modules 107 of the calibration system 101. In an embodiment, the one or more modules 107 may be implemented as dedicated units and when implemented in such a manner, said modules may be configured with the functionality defined in the present disclosure to result in a novel hardware. As used herein, the term module may refer to an Application Specific Integrated Circuit (ASIC), an electronic circuit, a Field-Programmable Gate Arrays (FPGA), Programmable System-on-Chip (PSoC), a combinational logic circuit, and/or other suitable components that provide the described functionality.

The one or more modules 107 of the present disclosure function to calibrate the one or more erroneous parameters 213. The one or more modules 107 along with the data 108, may be implemented in any system, for calibrating the one or more erroneous parameters 213. For the calibration, the recorded oscillatory mode determination module 201 of the calibration system 101 may be configured to determine the at least one recorded oscillatory mode 206 in the recorded disturbance data 205. In an embodiment, the recorded disturbance data 205 may be recorded for the power system 102 using known techniques. The recorded disturbance data 205 may be stored in the repository associated with the power system 102. In an embodiment, the recorded disturbance data 205 may be retrieved from the repository, for the calibration. In an embodiment, the disturbance data 205 may be recorded and stored in the memory 106 of the calibration system 101.

In an embodiment, to determine the at least one recorded oscillatory mode 206, the recorded oscillatory mode determination module 201 may be configured to generate the frequency spectrum for the recorded disturbance data 205. The frequency spectrum may be divided into smaller windows, where each of the windows corresponds to an oscillatory mode from the at least one recorded oscillatory mode 206. Obtaining the frequency spectrum may be done using one or more techniques, known to a person skilled in the art. The one or more techniques, may include, but are not limited to, Fourier Transform, Prony Analysis, Matrix Pencil, Eigenvalue Realization Algorithm, Koopman Modes and so on. In the present disclosure, the at least one recorded oscillatory mode 206 is reference, which is compared with the at least one computed oscillatory mode 209, for identifying the one or more erroneous parameters 213.

Upon determining the at least one recorded oscillatory mode 206, the one or more erroneous parameters 213 are identified in the network model. The erroneous parameter identification module 202 may be configured to identify the one or more erroneous parameters 213. Initially, oscillatory modes for the network model are computed. The erroneous parameter identification module 202 may compute oscillatory modes for the network model. In an embodiment, the at least one computed oscillatory mode 209 for the network model may be computed using the state space. The state-space model is used to determine oscillatory frequencies and damping associated with the power system 102. The state space model is used to formulate system matrix of the power system 102 with plurality of generators with corresponding controllers. Eigenvalues are derived from the system matrix to yield the one or more attributes 210 i.e., the frequency data and the damping data associated with the at least one computed oscillatory mode 209.

For example, the state space model for a power system may be defined as shown below:
[X ? ]= [A][X]+ [B][U] ………. (1)
[Y]= [C][X]+ [D][U] ………. (2)
where,
(X ) ?is first derivative of plurality of states of the power system, with respect to time, indicating change in state;
A is the system matrix that relates the plurality of states to the state space model;
X is system state vector;
B is control matrix which relates the change in each of the plurality of states with corresponding inputs;
U is vector of inputs to the power system;
Y is output vector of the power system;
C is output matrix which relates output to each of the plurality of states of the power system; and
D is feedforward matrix which relates the input to the output of the power system.

The stability of the power system depends on the input given to the power system, and in case of non-linear systems, the stability depends on the initial state before the input is given. In present disclosure, the input is the disturbance in the power system and the system being studied may be a generator with its controllers. The inputs are in the form of voltage and frequency, and the output is the power, both real and reactive from the one or more generators. The number of states and the system matrix depend on structure and level of detail in the network model. Additional controllers also add their own states to the state-space representation/model. In an embodiment, the system to be studied may be extended to an area which two or more generators and corresponding controllers.

Once the state-space model is generated for the network model, eigenvalues of the system matrix (A) may provide the frequency data and the damping data of the at least one computed mode. The eigenvalues are computed by solving the characteristic equation shown below:

|A- ?I|=0 ……….. (3)

where,
A is the system matrix;
I is identity matrix which is of same dimensions as that of A; and
? = ?1, ?2, ?3……… represent the eigenvalues.

The eigenvalues may be real or complex. In case of complex eigenvalues, the damping data is real part of the eigenvalue and angular frequency is imaginary part. By comparing these values with the frequency components computed from the at least one recorded oscillatory modes, the deviation 211 in the time response of the network model may be determined.

Further, the identified deviation 211 is compared with a predefined threshold value. When the deviation 211 is lesser the predefined threshold value, the deviation 211 may be considered negligible. The calibration system 101 may identify that is no erroneous parameters in the network parameters. When the deviation 211 is greater than the predefined threshold value, the calibration system 101 may be configured to identify one or more states 212 in the network model, which are contributing to the deviation 211. The one or more states contributing to the deviation 211 are identified from plurality of states in the network model.

Consider the power system illustrated in Figure 4. The power system is a two area 300.1 and 300.2, four generators 301.1-301.4 and eleven bus 302.1-302.11 system. The system matrix for generator 301.4 may be derived as illustrated in Table 1 below:

States 1 2 3 4 5 6 7 8 9 10
?? 0.00 -0.21 -0.07 -0.07 -0.01 -0.11 0.00 0.00 0.00 0.00
?d 377.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
??fd 0.00 -0.18 -1.20 0.85 0.00 0.00 -26.7 0.00 0.00 26.67
??1d 0.00 -5.97 26.65 -38.2 0.01 0.04 0.00 0.00 0.00 0.00
??1q 0.00 -0.91 0.00 0.00 -9.72 7.82 0.00 0.00 0.00 0.00
??2q 0.00 -11.9 -0.05 -0.05 13.06 -37.8 0.00 0.00 0.00 0.00
?v1 0.00 -7.23 12.98 13.84 -2.61 -20.4 -100 0.00 0.00 0.00
?v2 0.00 -4.20 -1.31 -1.40 -0.28 -2.22 0.00 -0.10 0.00 0.00
?v3 0.00 -10.5 -3.28 -3.50 -0.71 -5.56 0.00 49.75 -50 0.00
?vs 0.00 -5.83 -1.82 -1.94 -0.39 -3.09 0.00 27.64 -27.6 -0.19

Table 1
Accordingly, the eigenvalues computed using the characteristic equation (3) are as given in table 3 below:

Eigenvalue Frequency data (Hz) Damping data
?1 -98.1229 + 0.0000i - -
?2 -50.6727 + 0.0000i - -
?3 -40.7842 + 0.0000i - -
?4 -32.0827 + 0.0000i - -
?5 -1.9599 + 9.9132i 1.58 0.19
?6 -1.9599 - 9.9132i 1.58 0.19
?7 -8.3786 + 0.0000i - -
?8 -2.8995 + 0.0000i - -
?9 -0.2098 + 0.1102i 0.018 0.89
?10 -0.2098 - 0.1102i 0.018 0.89

Table 3
From Table 3, the deviation 211 between the at least one recorded oscillatory mode 206 and at least one computed oscillatory mode 209 is identified. Further, the erroneous parameter identification module 202 is configured to identify the one or more states 212 associated with the deviation 211. Impact factor and contribution factor associated with each of the plurality of states and the least one computed oscillatory mode 209 are used to identify the one or more states 212 amongst the plurality of states of the power system. One or more techniques, known to a person skilled in the art, may be used to identify the one or more states 212. In an embodiment, participation factor for each of the plurality of states may be computed based on corresponding impact factor and contribution factor. In an exemplary embodiment, the participation factor for each of the plurality of states may be derived from left eigenvector and right eigenvector associated with the power system.

The left eigenvector and the right eigenvector may be computed for each eigenvalue, which satisfy conditions in equations 4 and 5 below:

A f_i= ?_i f_i ………. (4)

?_i A= ?_i ?_i ………. (5)

where,
?i is ith eigenvalue;
f represents right eigenvector; and
? represents the left eigenvector.

The right eigenvector is used to isolate the impact factor of states on the computed oscillatory modes, and the left eigenvector provides the information regarding the contribution made by each of the plurality of state to the at least one computed oscillatory mode 209. By combining the right eigenvector and the left eigenvector, the participation factor for each of the plurality of states is determined. In another exemplary embodiment, the participation factor may be computed using equation 6 given below:

p_ki= f_ki*?_ik ………. (6)

where,
p_kiis the participation factor of kth state for ith eigenvalue;
f_kiis the right eigenvector of kth state for ith eigenvalue; and
?_ikis the left eigenvector of kth state for ith eigenvalue.

One or more other techniques, known to person skilled in the art, may be implemented in the present disclosure to determine the participation factor.

By using the participation factor, the impact of the plurality of states on each the at least one computed oscillatory mode 209 may be determined and thereby the one or more states 212 contributing to the deviation 211 may be determined. Thus, some of parameters associated with such one or more states 212 may be identified to be the one or more erroneous parameters 213. By identifying said one or more states 212, the search space for identifying the one or more erroneous parameters 213 may be minimized. Subsequently, the erroneous parameter adjustment module 203 may be configured to adjust such one or more erroneous parameters and reduce the deviation. Further, the one or more erroneous parameters 213 which are adjusted may be stored as the adjusted erroneous parameters 214 in the memory of the calibration system 101. The network model may be updated with the adjusted erroneous parameters 214 as a counter measure to handle stability in the power system. In some embodiment, upon updating the network model with the adjusted erroneous parameters, the calibration proposed in the present disclosure may be repeated to monitor the deviation. The steps of the proposed method may be performed in an iterative manner, to reduce the deviation/ mismatch in the power system.

The proposed calibration system may be implemented to improve generator models used in all dynamic simulations. The proposed calibration system may be implements in products related to dynamic security assessment, special protection schemes, decision support systems and so on.

The other data 215 may store data, including temporary data and temporary files, generated by modules for performing the various functions of the calibration system 101. The one or more modules 107 may also include other modules 204 to perform various miscellaneous functionalities of the calibration system 101. It will be appreciated that such modules may be represented as a single module or a combination of different modules.

Figure 4a illustrates a flowchart showing an exemplary method to calibrate the one or more erroneous parameters, in accordance with some embodiments of present disclosure.

At block 401, the recorded oscillatory mode determination module 201 may be configured to determine the at least one recorded oscillatory mode 206 in the recorded disturbance data 205 associated with the power system 102. In an embodiment, the at least one recorded oscillatory mode 206 may be determined by generating frequency spectrum for the at least one recorded disturbance data 206 and dividing the frequency spectrum to the at least one recorded oscillatory modes 206. The frequency spectrum may be divided based on at least one of frequency, damping, amplitude and phase associated with the at least one computed oscillatory modes 209.

At block 402, the erroneous parameter identification module 202 may be configured to identify the one or more erroneous parameters 213 in the network model of the power system 102. Figure 4b illustrates a flowchart showing an exemplary method to identify the one or more erroneous parameters 213 in the network model of the power system 102, in accordance with some embodiments of present disclosure.

At block 404, the erroneous parameter identification module 202 may be configured to generate a state space model for the network model. One or more techniques, known to a person skilled in the art, may be implemented for generating the state space model.

At block 405, the erroneous parameter identification module 202 may be configured to compute the one or more attributes 210 associated with the at least one computed oscillatory mode 209. In an embodiment, the one or more attributes 210 are computed from eigenvalues obtained from the state space model of the network model.

At block 406, the erroneous parameter identification module 202 may be configured to compare the one or more attributes 210 with the at least one recorded oscillatory mode 206 to identify deviation 211. The identified deviation 211 indicates deviation between the at least one recorded oscillatory mode 206 and the at least one computed oscillatory mode 209.

At block 407, the erroneous parameter identification module 202 may be configured to check if the deviation 211is greater than the predefined threshold value. If the deviation 211 is greater than the predefined threshold value, step in block 408 is performed. If the deviation 211 is lesser than the predefined threshold value, step in block 406 is repeated, to identify further deviation.

At block 408, when the deviation 211 is greater than the predefined threshold value, the erroneous parameter identification module 202 may be configured to identify the one or more states 212 contributing to the deviation 211. One or more techniques, known to a person skilled in the art, may be used to identify the one or more states 212.

At block 409, the erroneous parameter identification module 202 may be configured to identify the one or more parameters associated with the one or more states 212 to be the one or more erroneous parameters 213.

Referring back to Figure 4a, at block 403, the erroneous parameter adjustment module 203 may be configured to adjust the one or more erroneous parameters based on the deviation 211. In an embodiment, the one or more erroneous parameters 213 may be adjusted to reduce the deviation 211.

As illustrated in Figures 4a and 4b, the methods 400 and 402 may include one or more blocks for executing processes in the calibration system 101. The methods 400 and 402 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions or implement particular abstract data types.

The order in which the methods 400 and 402 are described may not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.

Computing System

Figure 5 illustrates a block diagram of an exemplary computer system 500 for implementing embodiments consistent with the present disclosure. In an embodiment, the computer system 500 is used to implement the calibration system 101. The computer system 500 may include a central processing unit (“CPU” or “processor”) 502. The processor 502 may include at least one data processor for executing processes in Virtual Storage Area Network. The processor 502 may include specialized processing units such as, integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.

The processor 502 may be disposed in communication with one or more input/output (I/O) devices 509 and 510 via I/O interface 501. The I/O interface 501 may employ communication protocols/methods such as, without limitation, audio, analog, digital, monaural, RCA, stereo, IEEE-1394, serial bus, universal serial bus (USB), infrared, PS/2, BNC, coaxial, component, composite, digital visual interface (DVI), high-definition multimedia interface (HDMI), RF antennas, S-Video, VGA, IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., code-division multiple access (CDMA), high-speed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMax, or the like), etc.

Using the I/O interface 501, the computer system 500 may communicate with one or more I/O devices 509 and 510. For example, the input devices 509 may be an antenna, keyboard, mouse, joystick, (infrared) remote control, camera, card reader, fax machine, dongle, biometric reader, microphone, touch screen, touchpad, trackball, stylus, scanner, storage device, transceiver, video device/source, etc. The output devices 510 may be a printer, fax machine, video display (e.g., cathode ray tube (CRT), liquid crystal display (LCD), light-emitting diode (LED), plasma, Plasma display panel (PDP), Organic light-emitting diode display (OLED) or the like), audio speaker, etc.

In some embodiments, the computer system 500 may consist of the calibration system 101. The processor 502 may be disposed in communication with the communication network 511 via a network interface 503. The network interface 503 may communicate with the communication network 511. The network interface 503 may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), transmission control protocol/internet protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. The communication network 511 may include, without limitation, a direct interconnection, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, etc. Using the network interface 503 and the communication network 511, the computer system 500 may communicate with power system 512 for calibrating the one or more erroneous parameters. The network interface 503 may employ connection protocols include, but not limited to, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), transmission control protocol/internet protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc.

The communication network 511 includes, but is not limited to, a direct interconnection, an e-commerce network, a peer to peer (P2P) network, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, Wi-Fi, and such. The first network and the second network may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the first network and the second network may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc.

In some embodiments, the processor 502 may be disposed in communication with a memory 505 (e.g., RAM, ROM, etc. not shown in Figure 5) via a storage interface 504. The storage interface 504 may connect to memory 505 including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as, serial advanced technology attachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394, Universal Serial Bus (USB), fibre channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc.

The memory 505 may store a collection of program or database components, including, without limitation, user interface 506, an operating system 507 etc. In some embodiments, computer system 500 may store user/application data 506, such as, the data, variables, records, etc., as described in this disclosure. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle ® or Sybase®.

The operating system 507 may facilitate resource management and operation of the computer system 500. Examples of operating systems include, without limitation, APPLE MACINTOSH® OS X, UNIX®, UNIX-like system distributions (E.G., BERKELEY SOFTWARE DISTRIBUTIONTM (BSD), FREEBSDTM, NETBSDTM, OPENBSDTM, etc.), LINUX DISTRIBUTIONSTM (E.G., RED HATTM, UBUNTUTM, KUBUNTUTM, etc.), IBMTM OS/2, MICROSOFTTM WINDOWSTM (XPTM, VISTATM/7/8, 10 etc.), APPLE® IOSTM, GOOGLE® ANDROIDTM, BLACKBERRY® OS, or the like.

Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, non-volatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.

Advantages

An embodiment of the present disclosure provisions a solution for difficulty in identifying erroneous parameters in existing disturbance-based techniques by making use of oscillatory modes.

An embodiment of the present disclosure provisions to compute oscillatory modes for better identification of erroneous parameters. Accurate identification of the erroneous parameters may be achieved by comparing oscillatory modes of recorded disturbance data and network model.

An embodiment of the present disclosure provisions to reduce search space i.e., number of data-sets of disturbances data required to accurately identify the erroneous parameters. By which, number of model parameters which are to be calibrated is reduced.

The described operations may be implemented as a method, system or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof. The described operations may be implemented as code maintained in a “non-transitory computer readable medium”, where a processor may read and execute the code from the computer readable medium. The processor is at least one of a microprocessors and a processor capable of processing and executing the queries. A non-transitory computer readable medium may include media such as magnetic storage medium (e.g., hard disk drives, floppy disks, tape, etc.), optical storage (CD-ROMs, DVDs, optical disks, etc.), volatile and non-volatile memory devices (e.g., EEPROMs, ROMs, PROMs, RAMs, DRAMs, SRAMs, Flash Memory, firmware, programmable logic, etc.), etc. Further, non-transitory computer-readable media may include all computer-readable media except for a transitory. The code implementing the described operations may further be implemented in hardware logic (e.g., an integrated circuit chip, Programmable Gate Array (PGA), Application Specific Integrated Circuit (ASIC), etc.).

An “article of manufacture” includes non-transitory computer readable medium, and /or hardware logic, in which code may be implemented. A device in which the code implementing the described embodiments of operations is encoded may include a computer readable medium or hardware logic. Of course, those skilled in the art will recognize that many modifications may be made to this configuration without departing from the scope of the invention, and that the article of manufacture may include suitable information bearing medium known in the art.
The terms “an embodiment”, “embodiment”, “embodiments”, “the embodiment”, “the embodiments”, “one or more embodiments”, “some embodiments”, and “one embodiment” mean “one or more (but not all) embodiments of the invention(s)” unless expressly specified otherwise.

The terms “including”, “comprising”, “having” and variations thereof mean “including but not limited to”, unless expressly specified otherwise.

The enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise.

The terms “a”, “an” and “the” mean “one or more”, unless expressly specified otherwise.

A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention.

When a single device or article is described herein, it will be readily apparent that more than one device/article (whether or not they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be readily apparent that a single device/article may be used in place of the more than one device or article or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself.

The illustrated operations of Figures 4a and 4b show certain events occurring in a certain order. In alternative embodiments, certain operations may be performed in a different order, modified, or removed. Moreover, steps may be added to the above described logic and still conform to the described embodiments. Further, operations described herein may occur sequentially or certain operations may be processed in parallel. Yet further, operations may be performed by a single processing unit or by distributed processing units.

Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.
While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

Referral numerals:
Reference Number Description
100 Environment
101 Calibration system
102 Power system
103 Communication network
104 Processor
105 I/O interface
106 Memory
107 Modules
108 Data
201 Recorded oscillatory mode determination module
202 Erroneous parameters identification module
203 Erroneous parameters adjustment module
204 Other modules
205 Recorded disturbance data
206 Recorded oscillatory mode data
207 Network model data
208 State space model data
209 Computed oscillatory mode data
210 Computed oscillatory mode attributes
211 Deviation data
212 Deviation contributing states data
213 Erroneous parameters
214 Adjusted erroneous parameters
215 Other data
301.1-301.4 Generators
302.1-302.11 One or more states
500 Computer System
501 I/O Interface
502 Processor
503 Network Interface
504 Storage Interface
505 Memory
506 User Interface
507 Operating System
508 Web Server
509 Input Devices
510 Output Devices
511 Communication Network
512 Power system