RESILIENT POWER SYSTEM WITH COLLABORATIVE ARCHITECTURE

BACKGROUND

[0001] The subject matter disclosed herein generally relates to electric power systems. More specifically, the subject matter relate to methods and systems for power grid management.

[0002] Power grids have become large and complex as the electrical infrastructure expands to satisfy growing demands. Much longer and higher rated transmission lines are used to transmit large amounts of power across very long distances. Simultaneously, state of the art communication techniques are being used along with adoption of smart grid technology for distribution automation, active load management and real-time metering. The technologies being used for the control, stability and management are becoming inadequate as customers and energy participants demand high reliability, and resilient grids. As one example, if power is down, customers are increasingly seeking prompt return of service in a specified time.

[0003] In addition, with synchronous mode of operation across large areas, protection of the grid against introduction of malicious data, cyber-attacks, and physical faults is gaining importance. Enhanced power system stability and optimal integration of distributed generation sources all around the grid are driving the need for better technologies. The introduction of cloud concepts, and distributed technological solutions have enabled faster introduction of improvements within the legacy systems and heterogeneous systems.

[0004] Modern Energy Management System (EMS) relies extensively on accurate snapshots of system parameters. While there have been attempts to enhance conventional state estimation by using synchro-phasor measurements, such estimations are not keeping pace with the technological requirements.

[0005] Thus there is an on-going need for enhanced systems and methods for managing a power grid.

BRIEF DESCRIPTION

[0006] In accordance with one aspect of the present technique, a method for managing a power grid is disclosed. The method includes receiving a first power grid data including a first state estimate associated with a first area disposed in a first hierarchical region and receiving a second power grid data including a second state estimate associated with a second area disposed in the first hierarchical region and electrically coupled to the first area. The method further includes determining a third state estimate of the first area based on the first power grid data and the second power grid data. The method includes generating a first modified power grid data based on the third state estimate. The method also includes determining a fourth state estimate of the second area based on the first modified power grid data, and the second power grid data. The method further includes generating a second modified power grid data based on the fourth state estimate. The method includes comparing the first state estimate and the second state estimate to predefined ranges of the third state estimate and the fourth state estimate respectively. The method also includes substituting into the power grid, the first power grid data and the second power grid data by the first modified power grid data and the second modified power grid data respectively if at least one of the first state estimate and the second state estimate are not within predefined ranges of the third state estimate and the fourth state estimate respectively. The method includes iteratively performing all the steps discussed herein till the first state estimate and the second state estimate are within predefined ranges of the third state estimate and the fourth state estimate respectively to generate a converged state estimate for the first area.

[0007] In accordance with one aspect of the present system, a system for managing a power grid is disclosed. The system includes a state estimation module associated with a first area disposed in a first hierarchical region. The state estimation module is configured to receive a first power grid data including a first state estimate associated with the first area and to receive a second power grid data including a second state estimate associated with a second area disposed in the first hierarchical region and electrically coupled to the first area. The state estimation module is further configured to determine a third state estimate of the first area based on the first power grid data and the second power grid data, and to generate a first modified power grid data based on the third state estimate.

The state estimation module is also configured to determine a fourth state estimate of the second area based on the first modified power grid data, and the second power grid data, and generate a second modified power grid data based on the fourth state estimate. The state estimation module is also configured to compare the first state estimate and the second state estimate to predefined ranges of the third state estimate and the fourth state estimate respectively. The state estimation module is further configured to substitute into the power grid, the first power grid data and the second power grid data by the first modified power grid data and the second modified power grid data respectively if at least one of the first state estimate and the second state estimate are not within predefined ranges of the third state estimate and the fourth state estimate respectively. The state estimation module iteratively performs all the steps discussed herein till the first state estimate and the second state estimate are within predefined ranges of the third state estimate and the fourth state estimate respectively to generate a converged state estimate for the first area.

[0008] In accordance with another aspect of the present technique, a non-transitory
computer readable medium encoded with a program to instruct a processor based state estimation module is disclosed. The program instructs the processor based state estimation module to receive a first power grid data including a first state estimate associated with a first area disposed in a first hierarchical region and to receive a second power grid data including a second state estimate associated with a second area disposed in the first hierarchical region and electrically coupled to the first area. The program also instructs the processor based state estimation module to determine a third state estimate of the first area based on the first power grid data and the second power grid data and to generate a first modified power grid data based on the third state estimate. The program also instructs the processor based state estimation module to determine a fourth state estimate of the second area based on the first modified power grid data, and the second power grid data, and generate a second modified power grid data based on the fourth state estimate. The program also instructs the processor based state estimation module to compare the first state estimate and the second state estimate to predefined ranges of the third state estimate and the fourth state estimate respectively. The program also instructs the processor based state estimation module to substitute into the power grid, the first power grid data and the second power grid data by the first modified power grid data and the second modified power grid data respectively if at least one of the first state estimate and the second state estimate are not within predefined ranges of the third state estimate and the fourth state estimate respectively. The program also instructs the processor based state estimation module to iteratively perform all the steps discussed herein till the first state estimate and the second state estimate are within predefined ranges of the third state estimate and the fourth state estimate respectively to generate a converged state estimate for the first area.

DRAWINGS

[0009] These and other features and aspects of embodiments of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

[0010] FIG. 1 is a diagrammatic illustration of an electrical grid having a power generator, a transmission grid and a distribution grid, in accordance with an exemplary embodiment;

[0011] FIG. 2 is a diagrammatical representation of an hierarchical architecture of the power grid in accordance with an exemplary embodiment;

[0012] FIG. 3 is a diagrammatical representation of a collaborative architecture of a power grid in accordance with an exemplary embodiment;

[0013] FIG. 4 is a schematic representation of a substation of a power grid in accordance with an exemplary embodiment;

[0014] FIG. 5 is a diagrammatical representation of a steady state AC power system with boundary buses used for determination of a state estimate of an area in accordance with an exemplary embodiment;

[0015] FIG. 6 is a flow chart illustrating the steps involved in determining state estimate of an area in accordance with an exemplary embodiment; and

[0016] FIG. 7 is a schematic representation of two iterations of the exemplary technique involving three peer areas of a hierarchical region in accordance with an exemplary embodiment of the present technique.

DETAILED DESCRIPTION

[0017] Embodiments of the present technique relate to systems and methods for state estimation using a collaborative architecture of a power system. According to one embodiment, an iterative peer to peer collaborative technique is used for estimating states of a power system having a set of hierarchical regions. A neighborhood of a predetermined area having a plurality of electrically coupled areas in the power system is considered for determining state estimation of the predetermined area. Power grid data including at least one of the measurement data and the state estimates of each of the peer areas in the neighborhood, are used to determine the state estimate of the neighborhood.

From the state estimates of the neighborhood, a new state estimate for the predetermined area is determined. Similarly, state estimation for each of the peer area in the neighborhood is determined using the new state estimate for the predetermined area. The above mentioned steps are performed iteratively till the state estimates are converged. Iterative collaborative method for determining state estimation of an area in the power system facilitates to avoid malicious data associated with the power system.

[0018] FIG. 1 is a diagrammatic illustration of an electrical or power grid 100
having a power generation network 102 for generation of electric power, a transmission grid 104 for transmitting the generated electric power, and a distribution grid 106 for distributing the electric power from the transmission grid 104 to the consumers. The power stations 108, 110 generate electric power using energy sources such as coal, nuclear, natural gas, biomass, wind, solar, hydropower, and the conditions thereof. The transmission grid 104 typically includes high voltage transformers 112 and high voltage transmission lines 114. The power generated from the power stations 108, 110 are generally stepped up in voltage by transformers 112 and are transmitted at high voltage ranges via the transmission lines 114 to a substation 118 that steps down the voltage for local distribution of the power. In the illustrated embodiment, the distribution grid 106 includes distribution transformers 116, power substations 118, residential loads 120, commercial loads 122, and industrial loads 124. The power in the transmission grid 104 is typically transmitted as high voltage AC or High Voltage Direct Current (HVDC) and may be stepped down suitably to appropriate voltages during the transmission. The transmission voltages are stepped down in several stages via transformers 116 and are delivered to consumers through the distribution network 106 at the appropriate levels. The power substation 118 distributes the power to a plurality of end users including the residential load 120, the commercial load 122, and the industrial load 124. Recent advances in power generation have even enabled many consumers to contribute to the grid, thereby increasing the complexity of the power grid. Consumers are increasingly using energy generators such as solar panels and wind turbines so they can use the generated power and in some cases feed the grid at certain times and draw from the grid at other times. In accordance with the embodiments of the present system, superior infrastructure facilities required for optimizing energy efficiency, demand profile, utility, cost and emission levels are supported by the substation 118. The exemplary embodiments discussed herein, disclose advanced communication and computation techniques integrated into the grid infrastructure for creating a smart grid to manage increasing complexity of transmission and distribution. The exemplary smart electrical grids have capabilities to offer failure protection, security and privacy protection services, along with enhanced grid reliability. Embodiments of the present techniques for determining state estimate of an area of the power grid are adopted by smart systems associated with the grid 100. Systems and associated methods for determining state estimate are disclosed in further detail with reference to subsequent figures. It should be noted herein that the grid 100 illustrated herein is an exemplary embodiment, and the configurations of the grid may vary in other embodiments depending on the applications.

[0019] FIG. 2 illustrates one example of the distribution grid 106 having a
hierarchical structure. It should be noted herein that the embodiments of the present system are also equally applicable to the transmission grid 104 or any other power grids depending on the application. In some embodiments, a part of the distribution grid 106, or transmission grid 104 of FIG. 1 may also have such a hierarchical structure. In the illustrated embodiment, the distribution grid 106 includes a plurality of hierarchical regions 202, 204, and 206. The first hierarchical region 202 has a plurality of areas 208, 210, 212 electrically coupled to each other. The areas 208, 210, 212 at the hierarchical region 202 are referred to as "peer areas". Peer areas 208, 210 and 212 at the hierarchical region 202 form an area 214 in the second hierarchical region 204. Similarly, a plurality of areas 222, 224, and 226 are disposed in the first hierarchical region 202. The areas 222, 224 and 226 also form "peer areas" to the areas 208, 210, 212 disposed in the same hierarchical region 202. Further, the areas 222, 224, and 226 form an area 216 in the second hierarchical region 204. The peer areas 214, 216 at the second hierarchical region 204 form an area 218 in the third hierarchical region 206. In the illustrated embodiment, the first hierarchical region 202 has areas which are disposed within an area of the second hierarchical region 204, and similarly the hierarchical region 204 has areas which are disposed within an area of the third hierarchical region 206. In one example, there is a plurality of hierarchical areas. The grid 106 is provided with a computing system 220 such as a centralized server or a distributed computing system accessible by the plurality of peer areas of the hierarchical regions. The computing system 220 includes one or more state estimation modules 228 required to determine state estimates of peer areas. The computing system 220 performs data processing and computations required by the power grid, such as acquisition of measurement data, and analysis of state estimates of peer areas. The computing system 220 includes a memory for storing data such as measurement data, historical data, and state estimate data.

[0020] The hierarchical structure of the grid 106, in accordance with the present
embodiment, facilitates management of large complex grids. The term "area" may refer to a geographical or an administrative area associated with the grid. For example, area 218 may refer to the grid of an entire country. The areas 214 and 216 refer to sub-areas of the area 218. For example, peer areas 214, 216 may refer to the grids corresponding to states of a country. Further, peer areas 208, 210, 212 are sub-areas of the area 214. Similarly, peer areas 222, 224, 226 refer to sub-areas of the area 216. Peer areas corresponding to similar administrative areas are disposed in the same hierarchical region. Lower hierarchical regions are composed of smaller areas whereas the upper hierarchical regions are composed of larger areas as compared to the areas of the lower hierarchical regions. For example, the area 214 of the hierarchical region 204 includes the relatively smaller area 208 disposed within the hierarchical region 202. Embodiments of the present technique are applicable to a plurality of areas disposed in different hierarchical regions for determining state estimates of the respective areas.

[0021] FIG. 3 illustrates another embodiment of a distribution grid 300, having a
different hierarchical structure in accordance with an exemplary embodiment. The grid 300 includes a first area 304 and a plurality of peer areas 302, 306, 308 for the first area 304 in a first hierarchical region 318. It should be noted herein that alternately that the peer area 302 may also referred be to as "second area". The grid 300 further includes a second hierarchical region 316 and a third hierarchical region 320. The second hierarchical region 316 has a third area 310 disposed in the first area 304. Similarly, the first area 304 is disposed in a fourth area 328 disposed in the third hierarchical region 320. Although hierarchical regions 316, 318, and 320 are shown as layers for simplicity of illustration, it should be noted herein that the second hierarchical region 316 is disposed within the first hierarchical region 318 and the first hierarchical region 318 is disposed within the third hierarchical region 320. The first area 304 of the grid has a state estimation module (SEM) 324 for determining state estimates of the first area. The state estimates of the first area in one example refer to a plurality of voltages and phase angles corresponding to the buses of the first area. The state estimation module 324 receives measurement data of the first area 304 from one or more sensors 322. Measurement data includes features or attributes such as power flows, power injections, voltage magnitude, phase angles and current magnitudes. The power flows may be a real power flow (also referred to as "active power flow") or reactive (inductive or capacitive) power flows. The power grid data of the first area 304 comprises measurement data and the state estimation data corresponding to the first area 304, which is disposed in the first hierarchical region 318. The SEM 324 of the first area 304 may also receive power grid data of the second area 302 having measurement data and state estimate of the second area 302 disposed in the first hierarchical region 318. The first power grid data and the second power grid data may also have a state estimate associated with the third area 310 disposed in the second hierarchical region 316. The SEM 324 of the first area 304 may also receive power grid data from other areas. In the illustrated embodiment, the SEM 324 of the first area 304 determines state estimates of the first area 304 either from the measurement data/ the state estimates of the peer areas 302, 306, 308 of the first hierarchical region 318. The
determined state estimate of the first area 304 in this example is communicated to the fourth area 328 in the third hierarchical region 320. It should be noted herein that the hierarchical regions 316, 318, 320 are similar to the regions 202, 204, 206 shown in FIG. 2. The exemplary technique for determining the state estimate of the first area 304 is also applied to other peer areas of the hierarchical region 318. The SEM 324 corresponding to the first area 304 and the SEMs 332, 326, and 330 of the peer areas 302, 306, 308 respectively, may have their own computational resources or may have access to computational services provided by a computing resource such as a cloud computing system 314. Measurement data or state estimates of the peer areas of hierarchical region 318 or from peer areas of the hierarchical regions 316, 320 may be stored and retrieved from the cloud computing system 314.

[0022] The first hierarchical region 318 may also include a detector 312 which
receives the measurement data and state estimates of the first area 304, and the peer areas 302, 306, 308 for detecting malicious data within the first area 304. Similarly, the detector 312 may also be used to detect malicious data in the estimates of each of the peer areas disposed in the hierarchical region 318. In some embodiments, the detector 312 may be part of the cloud computing system 314. In other embodiments, the detector 312 may be part of one of the SEMs 332, 324, 326, and 330 associated with the respective areas 302, 304, 306 and 308 disposed in the hierarchical region 318. The detector 312 may be any type of computing device capable of performing computations required for detecting malicious data in the grid 300. The working of the detector 312 is explained in further detail in subsequent paragraphs with reference to FIG. 5.

[0023] The SEM 324 may be a processor-based device, referring to one or more
processors or multi-core devices. A processor used in the SEM 324 may be general purpose processor or a controller. The processor may use software instructions from a disk or from a memory to process the power grid data. The software can be encoded in any language, including, but not limited to, assembly language, VHDL (Verilog Hardware Description Language), high level languages such as Fortran, Pascal, C, C++, and Java, ALGOL (algorithmic language), and any combination or derivative of at least one of the foregoing languages. The processor based device may also read instructions from a non-transitory encoded computer medium having instructions to perform state estimation in accordance with the exemplary embodiments of the present technique.

[0024] FIG. 4 is a schematic illustration of the power substation 118 of the
distribution grid 106 shown in FIG. 1 in one embodiment. It should be noted herein that any peer area within the corresponding hierarchical region may include one or more such substations. The illustrated substation 118 includes feeder lines 402, 404. The primary feeder line 404 feeds the power into the substation 118, and the secondary power line 402 feeds power from the substation 118 such as from a customer power source. The substation 118 may include a plurality of buses 414 for distributing electric power. The substation 118 may include one or more transformers 406 for stepping down voltage to levels suitable for the consumers. In the illustrated embodiment, the substation 118 receives power from a generator or customer power source 412, and feeds into the grid or to the customers. Thus, the power to the load 408 can be from the primary feeding line 404 and/or the customer power source 412. The substation 118 serves one or more loads 408, 420 of varying sizes. The loads 408, 420 may be real, capacitive, or inductive load banks. The substation 118 may include one or more capacitive banks 418 positioned in the proximity of the load 408. The larger sized load 420 may be coupled to the grid through a switch 410. In a large substation, for example, the switch 410 may be in the form of a circuit breaker which is used to interrupt any short circuits or overload currents that may occur on the network. In a relatively smaller distribution substation, the switch 410 may be used as a circuit breaker or a fuse for protection purpose.

[0025] In the illustrated embodiment, substationl 18 is communicatively coupled to
a control center 416 providing the capability for measuring local electrical parameters required for switching, protection and control mechanisms. The control center 416 may have computing resources which are part of the centralized computing system 220, distributed state estimation module 324 or the cloud computing system 314. Electrical parameters that are measured include, but are not restricted to, reactive power flows, active power injections, reactive bus injections real power flows, branch current magnitudes and bus voltages and phase angles. Measurements may be obtained using supervisory control and data acquisition (SCADA) systems. Phasor measurements may also be obtained from Phasor Measurement Units (PMU)/ Phasor Data Concentrator (PDC). These measurements are used by the state estimation module 324 to determine state estimates of the area using exemplary embodiments of the present technique..

[0026] FIG. 5 is a schematic representation of a steady state AC power system 500
in accordance with an embodiment of the present technique. In the illustrated embodiment, the system 500 includes the first area 304, for example, for which state estimate is to be determined. Although the first area 304 is illustrated, in other embodiments, such a system is applicable to any grid, (for example, the distribution grid 106 shown in FIG. 1), any peer area in a hierarchical region (for example, peer area 214 of the hierarchical region 204 shown in FIG. 2) or to the substation 118 shown in FIG. 4. In the illustrated embodiment, the first area 304 includes three feeders 510, 512, and 514 having power flows of 50MW, 30MW and 100 MW respectively. Typically, a steady state power system with "«" buses has a plurality of bus voltage magnitudes "Vi"" measurements and a plurality of phase angles "#/" measurements where i = 1, 2, ..., n, active power flows of the AC power system 500 includes the active power flows "Pif from the bus "/" to bus "/'. Net active power injection "Pj" at the z'th bus is a sum of active powers flows of a plurality of branches coupled to the rth bus. In the illustrated embodiment, the system 500 includes three boundary buses 504, 506, 508 for the first area 304, having corresponding boundary power flows of 80MW, 30MW and 70MW respectively. The state of the power system 500 may be determined by using the measurement model represented by:

z = Hx + e (1)

where z is a vector of m measurements, x is a vector of n state variables, H is a matrix of m rows and n columns relating the states to the measurements and e is independent measurement noise. Voltage values "W and a phase angles "6i" of the buses 510, 512, and 514 are included in the state vector V. Least squares methods such as a weighted least squares (WLS) method or a recursive least squares (RLS) method may be used to determine state estimate 'V from the active power measurements.

[0027] Power system 500 may be affected by malicious data as mentioned previously. Malicious data may be injected to any of the buses of the power system intentionally or accidentally. State estimates obtained by least squares method discussed herein may be used to detect the malicious data in the power system by observing power balance in the first area 304. Here, power balance refers to a condition where power flows satisfy the law of conservation of power at every node of the first area. Malicious data can also be determined by analyzing a residual data as described herein. Determined state estimates may be used in the equation (1) to obtain an estimate of the measurement data. The state vector may also include some of the parameters of the measurement data and these state estimates may be considered as the estimate of corresponding measurement data. The residual data is determined based on a difference between the measurement data and the estimate of measurement data. Ideally, the measurement data and estimate of the measurement data should have approximately same value. But, measurement data may be affected by malicious data injected into the power grid. Due to the presence of malicious data, quality of least square estimates may be affected. Some of the estimated values of the measurement data may differ from the corresponding measured value. By comparing the residual data with a suitable threshold, a malicious data is detected.

[0028] FIG. 6 is a flow chart 600 illustrating the exemplary steps involved in a collaborative iterative technique for determining state estimate of the first area 304 of the system 500 in accordance with the embodiment of FIG. 5. In the illustrated embodiment, the proposed technique is initiated by considering the first area 304 as the "present area" 602. A neighborhood of the first area 304, including at least one more peer area 302 (second area) is determined 604. The neighborhood of the first area 304 may have additional peer areas. For example, in the embodiment of FIG. 5, two other peer areas 516, 518 along with the second area 302 are identified as "neighbors" of the first area 304. In the illustrated embodiment, the peer areas 516, 518, 302 are from the same hierarchical region 318 as the first area 304. Measurement data of the first area 304 is included in a first power grid data associated with the first area 304. The second area 302 has an additional measurement power flow which in this example is 30MW. A second power grid data associated with the second area 302, includes the additional measurement power flow. The peer area 516 has an additional measurement power flow of 100MW along with the measurement power flow of 80MW. The peer area 518 has a power flow measurement of 70MW. These additional power flow measurements are included in the power grid data associated with the peer areas 516 and 518 respectively. The exemplary technique involves receiving the first power grid data including a first state estimate associated with the first area 304, and a second power grid data including a second state estimate associated with the second area 302. Power grid data from other areas of the neighborhood 516, and 518 may also be received 606.

[0029] Based on the power grid data of the neighborhood areas 302, 516, and 518,
state estimates of the neighborhood are determined by solving equation (1) using a least squares method 608. Alternatively, state estimates may also be determined using a weighted least squares method. In another embodiment, state estimates of the peer areas 302, 516, and 518 are considered for determining state estimates for the first area 304. In the illustrated embodiment, measurement model of equation (1) may be modified as:
where zt, z2, z3, and z4 are measurement data associated with the areas 304, 302, 516, and 518 respectively. Parameters xt, x2, x3, and x4 are state variables of the areas 304, 302, 516, 518 respectively. A modified least squares method may be used to determine the state estimate of the first area 304 based on the available state estimates of the peer areas 302, 516 and 518. The measurement vector of equation (2) includes all the additional measurements of the peer areas 302, 516, and 518. The state estimate determined by using equation (2), has state estimates of the areas 304, 302, 516, and 518. From these estimates, state estimate of the first area 304 is determined 612.

[0030] The quality of measurement data may be affected by malicious data as explained before. Malicious data is detected and corrective steps are taken 610. Malicious data in the first area 304 in one example is detected by analyzing the state estimates of the neighborhood of the first area 304. For example, malicious data in the neighborhood including the first area 304 and the second area 302 may be determined by observing power balance in at least one of the first area 304 and the second area 302. If the location of the malicious data is in the peer areas 302, 516, or 518, the quality of the state estimate of the first area 304 is still reasonably "good". When the location of the malicious data is in the periphery of the first area 304, corrective steps are taken while determining the state estimate of the first area 304. In one embodiment, the measurements affected by the malicious data are excluded from the state estimation computation. In another embodiment, an intelligent algorithm is used to replace the malicious data by an estimate of a valid data. As an example, the valid data may be an estimate determined based on historical stored data values. In another embodiment, an operator may have the option of providing an estimate of the valid data manually.

[0031] The steps 604, 606, 608, 610, and 612 are performed for each area of the
neighborhood of the first area 304 by an iterative loop resulting from a first conditional step 614. The conditional step 614 checks if the iterative loop is applied to all the peer areas of the first area 304. If state estimates of all the peer areas of the first area 304 are updated, the iterative loop is terminated. Otherwise, a new iteration of the loop is initiated to determine state estimation of one of the remaining peer areas of the first area 304. Before the initiation of the new iteration, the determined state estimates of the first area 304 are used to generate the modified power grid data of the present area 616. As an example, when the first area 304 and the second area 302 alone are involved, a third state estimate of the first area 304 is determined based on the first power grid data and the second power grid data. The first power grid data is modified by replacing the first state estimate by the third state estimate of the first area 304 to generate a first modified power grid data. Then, a fourth state estimate of the second area 302 is determined based on the first modified power grid data and the second power grid data. The second power grid data is modified by replacing the second state estimate by the fourth state estimate of the second area 302 to generate a second modified power grid data. In one embodiment, the state estimates of the first area 304 and the second area 302 are determined by separate SEMs 324, 332 respectively, and the first modified power grid data of the first area 304 may be stored in the cloud computing system subsequently retrieved by the SEM 332 of the second area 302, for computing the fourth sate estimate of the second area 302. Similarly, the second modified power grid data of the second area 302 may be stored in the cloud computing system and subsequently retrieved by the SEM 324 of the first area 304 to determine the third state estimate of the first area in the next iteration. Similarly, the measurement data of the first area 304 and the second area 302 may also be stored and retrieved from the cloud computing system. The first power grid data and the second power grid data may include a fifth state estimate of the third area 310 disposed in the second hierarchical region 316 (shown in FIG. 3). It may be noted herein, that the third area 310 is a sub-area of the first area 304 or the second area 302 disposed in the first hierarchical region 318. When new state estimates are available for all peer areas of the first area 304, state estimates of the first area 304 are tested for convergence 618. In the example involving the first area 304 and the second area 302, the first state estimate and the second state estimate are compared with the third state estimate and the fourth state estimate respectively. When the first state estimate and the second state estimate are within predefined ranges of the third state estimate and the fourth state estimate, the third state estimate of the first area 304 is considered as the converged state estimate. In certain embodiments, the predefined range has a value of the order of 0.0001 or 0.01%. In some other embodiments, the predefined range may be of the order of 0.001. A smaller tolerance value may also be assigned to the predefined range. The predefined ranges may be either be specified by the system operator or automatically determined based on the system parameters. If at least one of the first state estimate and the second state estimate are not within predefined ranges of the third state estimate and the fourth state estimate respectively, the first power grid data and the second power grid data are substituted by the first modified power grid data and the second modified power grid data respectively 622. Further, step 622 also includes introducing the first state estimate and the second state estimate into the power grid to be available for the next iteration of an iterative loop resulting from a second conditional step 618. If the state estimates of the first area 304 are not converged, steps 602, 604, 606, 608, 610, 612, 614, and 616 are performed. When the state estimates are converged, the available state estimates for the first area 304 is considered as converged state estimate for the first area 620.

[0032] FIG. 7 illustrates a schematic exemplary representation 700 of two
consecutive iterations 702, 704 of the iterative technique corresponding to steps 604, 606, 608, 610, 612, 614, and 616 of the flow chart 600 shown in FIG. 6. Two peer areas 302, 516 of the first area 304 are shown in the iterative technique for the illustration purpose. It should be noted herein that the area 518 is omitted in the illustrated embodiment, for easy comprehension of the steps involved in the iterative method and hence the omission does not restrict the scope of the illustrated embodiment. The number of peer areas may be more than two areas depending on the hierarchical region and topology of the grid. Since, two peer areas are considered in the illustrated embodiment, each iteration is performed in three steps. In the first step of the first iteration 702, state estimates of the peer areas 302, 516 are used, to determine a new state estimate of the first area 304. The new state estimate for the first area 304 is denoted by symbol "A". In the second step of the first iteration 702, the state estimate of the second area 302 is determined. A different set of peer areas for the second area 302 is identified in the same hierarchical region for estimation purpose. New state estimate "A" of the first area 304, computed in the first step of the iteration, is used in the second step to determine another new state estimate, denoted by symbol "B", of the second area 302. Similarly, another new state estimate of the area 516 is determined in the third step of the first iteration 702, using the new state estimate "A" of the first area 304.

[0033] In the second iteration 704, the steps of the first iteration are repeated with
new values of state estimates of the areas 304, 302, 516. In the first step of the second iteration 704, state estimates "B" and "C" of the areas 302, 516 respectively are used to determine a new state estimate of the first area 304 which is denoted by symbol "D". In the second step of the second iteration 704, state estimate "D" of the first area 304 is used to determine a new state estimate "E" of the second area 302. Similarly, in the third step of the second iteration 704, the state estimate "D" of the first area 304 is used to determine the state estimate "F" of the area 516. The state estimates of the first area 304 are checked for convergence as represented by numeral 618 and the steps 602, 604, 606, 608, 610, 612, 614, and 616 are repeated till the state estimates converge. In one embodiment, convergence of the state estimate is verified by comparing a difference of successive state estimates of an area with a threshold. As an example, a difference between new and old state estimates of the first area 304 (denoted by "A-D") is compared with a pre-determined threshold. Similarly, the difference between the new and old state estimates of the second area 302 (denoted by "B-E"), and difference between the new and old state estimates of the area 516 (denoted by "C-F") are compared with the corresponding the pre-determined threshold values. If all the differences discussed herein are less than the threshold value, the iteration loop of is terminated. If one or more of the difference discussed herein is greater than the threshold value, another iteration is initiated. If all the differences are less than the threshold values, the most recent state estimates are considered as converged state estimates. Converged state estimates represented by the numeral 620 of the first area 304 are considered as the determined state estimates. The converged state estimates are used for management of the power grid. It should be noted herein that "power grid management" discussed herein includes but not limited to anomaly detection due to malicious data contingency analysis, security constrained optimization, and optimal power flow analysis of the power grid.

[0034] The method of state estimation explained in FIG. 7 is used for state
estimation of each area of the first hierarchical region. It should also be noted that, state estimation in areas disposed in other hierarchical regions can also be performed using the exemplary technique discussed herein. For an area disposed in the base of the hierarchical structure, determination of the state estimate is performed from a measurement data.

Measurements are periodically acquired by the sensors and state estimates are determined based on a least squares method. State estimation of areas disposed in next hierarchical regions may be computed either from the measurement data corresponding to the areas at such regions or from the state estimates of the corresponding areas in a previous hierarchical region.

[0035] m accordance with the embodiments discussed herein, the iterative collaborative state estimation method determines state estimates of a power grid system. Collaborative approach of considering the peer areas improves the robustness of the power system from malicious data. Iterative method of state estimation improves the quality of the state estimates in a complex distributed grid.

[0036] It is to be understood that not necessarily all such objects or advantages
described above may be achieved in accordance with any particular embodiment. Thus, for example, those skilled in the art will recognize that the systems and techniques described herein may be embodied or carried out in a manner that achieves or optimizes one advantage or group of advantages as taught herein without necessarily achieving other objects or advantages as may be taught or suggested herein.

[0037] While the invention has been described in detail in connection with only a
limited number of embodiments, it should be readily understood that the invention is not limited to such disclosed embodiments. Rather, the invention can be modified to incorporate any number of variations, alterations, substitutions or equivalent arrangements not heretofore described, but which are commensurate with the spirit and scope of the invention. Additionally, while various embodiments of the invention have been described, it is to be understood that aspects of the invention may include only some of the described embodiments. Accordingly, the invention is not to be seen as limited by the foregoing description, but is only limited by the scope of the appended claims. What is claimed as new and desired to be protected by Letters Patent of the United States is:

CLAIMS:

1. A method comprising:

(i) receiving first power grid data comprising a first state estimate associated with a first area disposed in a first hierarchical region of a power grid;

(ii) receiving second power grid data comprising a second state estimate associated with a second area disposed in the first hierarchical region and electrically coupled to the first area;

(iii) determining a third state estimate of the first area based on the first power grid data and the second power grid data;

(iv) generating a first modified power grid data based on the third state estimate;

(v) determining a fourth state estimate of the second area based on the first modified power grid data and the second power grid data;

(vi) generating a second modified power grid data based on the fourth state estimate;

(vii) comparing the first state estimate and the second state estimate to predefined ranges of the third state estimate and the fourth state estimate respectively;

(viii) substituting into the power grid, the first power grid data and the second power grid data by the first modified power grid data and the second modified power grid data respectively if at least one of the first state estimate and the second state estimate are not within predefined ranges of the third state estimate and the fourth state estimate respectively;
iteratively performing steps (i) to (viii) until the first state estimate and the second state estimate are within predefined ranges of the third state estimate and the fourth state estimate respectively, to obtain a converged state estimate for the first area; and
managing the power grid based on the converged state estimate for the first area.

2. The method of claim 1, wherein each of the first power grid data and the second power grid data comprises at least one of measurement data and a fifth state estimate associated with a third area disposed in a second hierarchical region disposed within the first hierarchical region.

3. The method of claim 2, wherein the measurement data comprises at least one of a power flow, a voltage, and a phase angle associated with the third area.

4. The method of claim 2, wherein the generating further comprises generating at least one of the first power grid data and the second power grid data based on the measurement data.

5. The method of claim 1, further comprising storing at least one of the first modified power grid data, the second modified power grid data, and the measurement data in a cloud computing system and retrieving at least one of the first modified power grid data, the second modified power grid data, and the measurement data from the cloud computing system.

6. The method of claim 1, wherein determining at least one of the third state estimate and the fourth state estimate comprises:

removing malicious data from at least one of the first power grid data and the second power grid data.

7. The method of claim 6, wherein removing the malicious data comprises:

detecting a malicious data by observing power balance in at least one of the first area and the second area; and

replacing the malicious data by an estimate of a valid data corresponding to the first area and the second area.

8. The method of claim 1, wherein receiving at least one of the first power grid data and the second power grid data comprises retrieving at least one of the first power grid data and the second power grid data respectively from a cloud computing system.

9. The method of claim 1, wherein determining the third state estimate of the first area comprises applying a weighted least square method to the first power grid data and the second power grid data.

10. The method of claim 1, wherein determining the fourth state estimate of the second area comprises applying a weighted least square method to the first modified power grid data and the second power grid data.

11. A system for managing a power grid, the system comprising:

a state estimation module associated with a first area disposed in a first hierarchical region, configured to:

(i) receive first power grid data comprising a first state estimate associated with the first area;

(ii) receive second power grid data comprising a second state estimate associated with a second area disposed in the first hierarchical region and electrically coupled to the first area;

(iii) determine a third state estimate of the first area based on the first power grid data and the second power grid data;

(iv) generate the first modified power grid data based on the third state estimate;

(v) determine a fourth state estimate of the second area based on the first modified power grid data and the second power grid data;

(vi) generate the second modified power grid data based on the fourth state estimate;

(vii) compare the first state estimate and the second state estimate to predefined ranges of the third state estimate and the fourth state estimate respectively;

(viii) substitute into the power grid, the first power grid data and the second power grid data by the first modified power grid data and the second modified power grid data respectively if at least one of the first state estimate and the second state estimate are not within predefined ranges of the third state estimate and the fourth state estimate respectively;

iteratively perform steps (i) to (viii) until the first state estimate and the second state estimate are within predefined ranges of the third state estimate and the fourth state estimate respectively, to obtain a converged state estimate for the first area; and

manage the power grid based on the converged state estimate for the first area.

12. The system of claim 11, wherein the state estimation module comprises a first estimation module for determining the third state estimate of the first area, and a second estimation module for determining the fourth state estimate of the second area.

13. The system of claim 11, further comprising a plurality of sensors coupled to the state estimation module, wherein the plurality of sensors are used for acquiring measurement data comprising at least one of a power flow, a voltage and a phase angle, associated with a third area disposed in a second hierarchical region disposed within the first hierarchical region.

14. The system of claim 13, further comprising a cloud computing system coupled to the state estimation module, wherein the cloud computing system is used for storing at least one of the first power grid data, the second power grid data, the measurement data, the first modified power grid data, and the second modified power grid data.

15. The system of claim 13, wherein the state estimation module is further configured to generate at least one of the first power grid data and the second power grid data based on the measurement data.

16. The system of claim 11, wherein the state estimation module is further configured to remove a malicious data from at least one of the first power grid data and the second power grid data.

17. The system of claim 11, wherein the state estimation module is further
configured to:

detect a malicious data by observing power balance in at least one of the first area and the second area;

replacing the malicious data by an estimate of a valid data corresponding to the first area and the second area.

18. The system of claim 11, wherein the state estimation module is configured to determine the third state estimate by applying a weighted least square method to the first power grid data and the second power grid data.

19. The system of claim 11, wherein the state estimation module is configured to determine the fourth state estimate by applying a weighted least square method to the first modified power grid data and the second power grid data.

20. A non-transitory computer readable medium having instructions to enable a
processor-based state estimation module to:

(i) receive first power grid data comprising a first state estimate associated with a first area disposed in a first hierarchical region;

(ii) receive second power grid data comprising a second state estimate associated with a second area disposed in the first hierarchical region and electrically coupled to the first area;

(iii) determine a third state estimate of the first area based on the first power grid data and the second power grid data;

(iv) generate the first modified power grid data based on the third state estimate;

(v) determine a fourth state estimate of the second area based on the first modified power grid data and the second power grid data;

(vi) generate the second modified power grid data based on the fourth state estimate;

(vii) compare the first state estimate and the second state estimate to predefined ranges of the third state estimate and the fourth state estimate respectively;

(viii) substitute into the power grid, the first power grid data and the second power grid data by the first modified power grid data and the second modified power grid data respectively if at least one of the first state estimate and the second state estimate are not within predefined ranges of the third state estimate and the fourth state estimate respectively;

iteratively perform steps (i) to (viii) until the first state estimate and the second state estimate are within predefined ranges of the third state estimate and the fourth state estimate respectively, to obtain a converged state estimate for the first area; and
manage the power grid based on the converged state estimate for the first area.