TECHNICAL FIELD
[001] The present subject matter described herein, in general, relates to a system and a method for controlling electrical power distribution system, and more particularly a system and a method for controlling electrical power distribution system based on a neural network schema.
BACKGROUND
[002] A power plant converts energy from nonelectrical to electrical form. Generally, power plant consists of several generating units which work together to meet the electric load demand. While Power Plant mainly concern electric power generation, those corresponding to electric power distribution system concern transmission, sub-transmission and distribution of electric power. An electrical power distribution system can be seen as a network of interconnected components with the purpose of: converting nonelectrical energy into electrical form; transforming electrical energy into a specific form subject to strict requirements; transporting electrical energy over long distances; converting electrical energy into another energy form. During distribution of electricity in such electrical power distribution system loss and theft of electricity is encountered.
[003] Generally, a smart grid is utilized for collection of power data. A smart grid is a modernized electrical grid that uses analog or digital information and communications technology to gather and act on the data gathered. Generally, in conventional smart grid power data is collected at a central location, which is further used for analysis and prediction. However, such conventional collection of data and analysis have an overriding cost, such as processing power cost, time and space for data collection-storing-handling and time to analyze and draw conclusion.
SUMMARY
[004] Before the present system(s) and methods for controlling electrical power distribution system based on a neural network schema, are described, it is to be understood that this application is not limited to the particular system(s), and methodologies described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosures. It is also to be understood that the terminology used in the description is for the purpose of describing the particular implementations or versions or embodiments only, and is not intended to limit the scope of the present application. This summary is provided to introduce aspects related to a system(s) and a method for controlling electrical power distribution system based on a neural network schema. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
[005] In one implementation, a system for controlling electrical power distribution system based on a neural network schema. The system comprising a memory and a processor. The processor is capable of executing instructions stored in the memory to perform steps of measuring a plurality of electric parameters of an electrical power distribution system. Further, the plurality of electric parameters is measured by a node of a network based upon a neural network schema. Upon measuring, generating one or more patterns by analyzing the plurality of electric parameters based on a machine learning methodology. Further, the one or more patterns are at least indicative of one of an electrical power generation and an electrical power usage. Further to generating, communicating plurality of electric parameters and the one or more patterns to one or more other nodes in the network over a power line communication channel. Subsequent to communicating, performing one or more decisive actions based on the plurality of electric parameters, the one or more patterns and a predefined criterion.
[006] In another implementation, a method for controlling electrical power distribution system based on a neural network schema is disclosed. In one aspect, the method may comprise measuring a plurality of electric parameters of an electrical power distribution system. Further, the plurality of electric parameters is measured by a node of a network based upon a neural network schema. Upon measuring, the method may comprise generating one or more patterns by analyzing the plurality of electric parameters based on a machine learning methodology. Further, the one or more patterns are at least indicative of one of an electrical power generation and an electrical power usage. Further to generating, the method may comprise communicating plurality of electric parameters and the one or more patterns to one or more other nodes in the network over a power line communication channel. Subsequent to communicating, the method may comprise performing one or more decisive actions based on the plurality of electric parameters, the one or more patterns and a predefined criterion.
[007] In yet another implementation, non-transitory computer readable medium embodying a program executable in a computing device for controlling electrical power distribution system based on a neural network schema is disclosed. The program may comprise a program code for measuring a plurality of electric parameters of an electrical power distribution system. Further, the plurality of electric parameters is measured by a node of a network based upon a neural network schema. Upon measuring, the program may comprise a program code for generating one or more patterns by analyzing the plurality of electric parameters based on a machine learning methodology. Further, the one or more patterns are at least indicative of one of an electrical power generation and an electrical power usage. Further to generating, the program may comprise a program code for communicating plurality of electric parameters and the one or more patterns to one or more other nodes in the network over a power line communication channel. Subsequent to communicating, the program may comprise a program code for performing one or more decisive actions based on the plurality of electric parameters, the one or more patterns and a predefined criterion.
BRIEF DESCRIPTION OF THE DRAWINGS
[008] The foregoing detailed description of embodiments is better understood when read in conjunction with the appended drawings. For the purpose of illustrating of the present subject matter, an example of construction of the present subject matter is provided as figures; however, the invention is not limited to the specific method and system disclosed in the document and the figures.
[009] The present subject matter is described detail with reference to the accompanying figures. 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 drawings to refer various features of the present subject matter.
[010] Fig 1(a) and Fig 1(b) illustrates an implementation of a system for controlling electrical power distribution system based on a neural network schema, in accordance with an embodiment of the present subject matter.
[011] Fig 2 illustrates the system, in accordance with an embodiment of the present subject matter.
[012] Fig 3 illustrates a method for controlling electrical power distribution system based on a neural network schema, in accordance with an embodiment of the present subject matter.
DETAILED DESCRIPTION
[013] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words "comprising," "having," "containing," and "including," and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any system and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary, systems and methods are now described. The disclosed embodiments are merely examples of the disclosure, which may be embodied in various forms. Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure is not intended to be limited to the embodiments described, but is to be accorded the widest scope consistent with the principles and features described herein.
[014] In an implementation, a system and method for controlling electrical power distribution system based on a neural network schema, is described. In one example, a network of systems may be implemented in an electric power distribution system. For example a system may be implemented at an electric power plant, a substation, a factory, a house, a commercial establishment, a factory and the like. Further, the system may be implemented at electric power consumption point for example, air conditioning, refrigeration, lights and the like.
[015] In the implementation for controlling electrical power distribution system based on a neural network schema, a plurality of electric parameters of an electrical power distribution system may be measured. The plurality of electric parameters may be measured by a system in a network of systems. Further the network may be based upon a neural network schema. In an example, the predefined criterion comprises change in electrical power generation, change in electrical power usage, change in environmental conditions, change in efficiency and data error.
[016] Upon measuring the plurality of electric parameters generating, one or more patterns by analyzing the plurality of electric parameters based on a machine learning methodology may be generated. The one or more patterns may be indicative of an electrical power generation and an electrical power usage.
[017] Further to generation of patterns, the plurality of electric parameters and the one or more patterns may be communicated to one or more other systems in the network over a power line communication channel. Power line communication may be understood as a communication technology that enables sending data over existing power cables. In other words, with electrical power cables running to an electronic device one can both power it up and at the same time control/retrieve data from it in a half-duplex manner.
[018] Subsequent to communication, one or more decisive actions may be performed. In one implementation the one or more decisive actions may be based on the plurality of electric parameters, the one or more patterns and predefined criteria. Further the predefined criterion comprises at least one of change in electrical power generation, change in electrical power usage, change in environmental conditions, and change in efficiency.
[019] In one example, the decisive action comprises one of an activation of electric power supply, a deactivation of electric power supply, a generation of electric power usage statistics, a generation of electric power usage bill and a rising of alarm. In an example, the alarm is raised for one of maintenance of network, maintenance of subsystem, leakage of electricity. Thus based on the execution of decisive action controlling of electrical power distribution system based on a neural network schema is enabled.
[020] Referring now to Fig 1(a) and Fig 1(b), a implementation of systems 102-1, 102-2, 102-3a….. 102-4c, herein after referred to collectively and individually as system 102 for controlling electrical power distribution system based on a neural network schema, in accordance with an embodiment of the present subject matter may be described. In an example, the system 102 may also be understood as a node of a neural network schema, as shown in Fig 1(b).
[021] In another embodiment, the systems 102 may be implemented in an electric power distribution system with other system 102 to form a network based on a neural network schema, as shown in fig 1(b). The system 102 may be implemented at an electric power plant 104, a substation 110, a large factory 108, a house 116, a commercial establishment 114, a small factory 112 and the like. Further, the system 102 may be implemented at electric power consumption point for example, air conditioning, refrigeration, lights and the like. Further, based on the implementation the system 102 along with other system may form a network, as shown in fig 1(b), based on a neural network schema.
[022] Referring now to Figure 2, the system 102 is illustrated in accordance with an embodiment of the present subject matter. In one embodiment, the system 102 may include at least one processor 202, an input/output (I/O) interface 204, and a memory 206. The at least one processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 202 may be configured to fetch and execute computer-readable instructions stored in the memory 206.
[023] The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 204 may allow the system 102 to interact with the user. Further, the I/O interface 204 may enable the system 102 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface 204 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 204 may include one or more ports for connecting a number of devices to one another or to another server.
[024] The memory 206 may include any computer-readable medium or computer program product known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory 206 may include modules 208 and data 210.
[025] The modules 208 include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. In one implementation, the modules 208 may include a measurement module 212, a generation module 214, a communication module 216, an execution module 218 and an other module 220. The other modules 218 may include programs or coded instructions that supplement applications and functions of the system 102. The modules 208 described herein may be implemented as software modules that may be executed in the cloud-based computing environment of the system 102.
[026] The memory 206, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the modules 208. The memory 206 may include data generated as a result of the execution of one or more modules in the other module 220. In one implementation, the memory may include data 210. Further, the data 210 may include a system data 222 for storing data processed, received, and generated by one or more of the modules 208. Furthermore, the data 210 may include other data 224 for storing data generated as a result of the execution of one or more modules in the other module 220.
[027] Construe an example, where network of systems 102 (not shown in figures) based on a neural network schema are implemented in a commercial complex. In the said example, a first system 102-A may be implemented at the main electric power distribution board in the commercial building. Further, second system 102-B and third system 102-C may be implemented at the first floor and second floor of the commercial building respectively. Furthermore, systems 102-Ba, 102-Bb may be implemented for rooms a & b of first floor and systems 102-Cc, 102-Cd may be implemented for rooms c & d of second floor. In the said example, the systems 102-A, 102-B, 102-C, 102-Ba, 102-Bb, 102-Cc, and 102-Cd form a network based on a neural network schema for controlling electrical power distribution system. In an example, the systems 102 may be configured to execute decisions based on the level in the network at which the system is implemented.
MEASUREMENT MODULE 212
[028] Referring to figure 2, in an implementation, a system and method implementations for controlling electrical power distribution system based on a neural network schema, is described. In the implementation, the measurement module 212 may measure a plurality of electric parameters of an electrical power distribution system. In example, the plurality of electric parameters comprises voltage, current, and power factor.
[029] In the example of network of system 102, the measurement module 212 of each of the systems 102-A, 102-B, 102-C, 102-Ba, 102-Bb, 102-Cc, and 102-Cd may measure a plurality of electric parameters of an electrical power distribution system. In the example each of the systems 102-A, 102-B, 102-C, 102-Ba, 102-Bb, 102-Cc, and 102-Cd may measure the electrical parameters at its locations.
GENERATION MODULE 214
[030] In the embodiment, subsequent to measurement of the plurality of electric parameters the generator module 214 may generate one or more patterns. The one or more patterns are generated by analyzing the plurality of electric parameters and utilizing a machine learning methodology. In one example, the one or more patterns are at least indicative of one of an electrical power generation and an electrical power usage. Electrical power generation may be understood as the total amount of electrical power generated, the frequency of electrical power generation and time period of electrical power generated. Electrical power usage may be understood as the total amount of electrical power used, the frequency of electrical power usage and time period of electrical power usage. In one more example, the machine learning methodology may be supervised, semi-supervised, unsupervised and the like.
[031] In the example of network of system 102, the generator module 214 of each of the systems 102-A, 102-B, 102-C, 102-Ba, 102-Bb, 102-Cc, and 102-Cd may generate one or more patterns. In the example each of the systems 102-A, 102-B, 102-C, 102-Ba, 102-Bb, 102-Cc, and 102-Cd may generate one or more patterns.
COMMUNICATION MODULE 216
[032] In the embodiment upon generation of one or more patterns, the communication module 216 may communicate the plurality of electric parameters and the one or more patterns to one or more other systems in the network. In one embodiment the communication may be performed over a power line communication channel. In one other embodiment, the communication module 216 may also receive a plurality of other system electric parameters and one or more other system parameters.
[033] In the example of network of system 102, the communication module 214 of each of the systems 102-A, 102-B, 102-C, 102-Ba, 102-Bb, 102-Cc, and 102-Cd may communicate the plurality of electric parameters and the one or more patterns to one or more other systems in the network. In the example each of the systems 102-A, 102-B, 102-C, 102-Ba, 102-Bb, 102-Cc, and 102-Cd may communicate the plurality of electric parameters and the one or more patterns to one or more other systems in the network. In one example, systems, 102-Ba, 102-Bb, and 102-Cc, 102-Cd may communicate with system 102-B and 102-C respectively. Further, systems 102-B and 102-C may communicate with system 102-A. In one implementation, the each of the systems 102-A, 102-B, 102-C, 102-Ba, 102-Bb, 102-Cc, and 102-Cd may receive a plurality of other system electric parameters and a one or more other system patterns.
EXECUTION MODULE 216
[034] In the embodiment, further to communication, the execution module 216 may perform one or more decisive actions based on the plurality of electric parameters, the one or more patterns and a predefined criterion. In one other example, the predefined criterion comprises at least one of change in electrical power generation, change in electrical power usage, change in environmental conditions, and change in efficiency. In an example, the decisive action may be one of an activation of electric power supply, a deactivation of electric power supply, a generation of electric power usage statistics, a generation of electric power usage bill and rising of alarm. In one more example, the alarm may be raised for one of maintenance of network, maintenance of subsystem, leakage of electricity and theft of electricity. Further based on the decisive action the system for controlling electrical power distribution system based on a neural network schema is enabled. In one example, the system 102 may be configured to perform different decisive actions based on the location of the system 102 in the neural network schema.
[035] In the example of network of system 102, the execution module 216 of each of the systems 102-A, 102-B, 102-C, 102-Ba, 102-Bb, 102-Cc, and 102-Cd may perform one or more decisive actions based on the plurality of electric parameters, the one or more patterns and a predefined criterion. In the example each of the systems 102-A, 102-B, 102-C, 102-Ba, 102-Bb, 102-Cc, and 102-Cd perform one or more decisive actions based on the plurality of electric parameters, the one or more patterns and a predefined criterion. In one example, the decisive action may be one of an activation of electric power supply, a deactivation of electric power supply, a generation of electric power usage statistics, a generation of electric power usage bill and rising of alarm. In one example, systems102-A, 102-B, 102-C, 102-Ba, 102-Bb, 102-Cc, and 102-Cd may generate electric power usage statistics, generate electric power usage bill. In one example, in case the system 102-B may compare the electrical parameters and patterns received from 102-Bb, and 102-Ba with its own electrical parameters and patterns may raise an alarm in case of data mismatch. In one example, the systems102-A, 102-B, 102-C, 102-Ba, 102-Bb, 102-Cc, and 102-Cd may be configured to perform different decisive actions based on the location of the system in the neural network schema. Thus, the systems systems102-A, 102-B, 102-C, 102-Ba, 102-Bb, 102-Cc, and 102-Cd enable controlling electrical power distribution system based on a neural network schema.
[036] Exemplary embodiments discussed above may provide certain advantages. Though not required to practice aspects of the disclosure, these advantages may include those provided by the following features.
[037] Some embodiments of the system and the method enable implementation of low cost solution for controlling electric power distribution system.
[038] Some embodiments of the system and the method enable implementation as an add-on capability on existing electric power distribution system.
[039] Some embodiments of the system and the method may be modular in design and adapt to electrical system around the world.
[040] Some embodiments of the system and the method reduce manufacturing, implementation and maintenance cost.
[041] Some embodiments of the system and the method enable simultaneous connection to electricity power grid and alternate energy resources.
[042] Some embodiments of the system and the method are scalable according to the need.
[043] Some embodiments of the system and the method provide security to electric power distribution system to detect and avert misuse and sabotage.
[044] Some embodiments of the system and the method enable deployment of secure communication protocol.
[045] Some embodiments of the system and the method enable customization of newral network schema.
[046] Some embodiments of the system and the method enable coverage from electric power generation to highest node.
[047] Some embodiments of the system and the method enable automatic selective alarm for maintenance of network/subsystem, leakage/theft or to selectively shutdown a power line in case of an accident/disaster, without any centralized decision making system or user interface.
[048] Some embodiments of the system and the method have distributed and layered architecture
[049] Referring now to Figure 3, a method 300 for controlling electrical power distribution system based on a neural network schema is shown, in accordance with an embodiment of the present subject matter. The method 300 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, functions, etc., that perform particular functions or implement particular abstract data types.
[050] The order in which the method 300 is described is 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 300 or alternate methods. Additionally, individual blocks may be deleted from the method 300 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 300 may be considered to be implemented in the above described system 102.
[051] At block 302, a plurality of electric parameters of an electrical power distribution system is measured. The plurality of electric parameters is measured by a system in a network. Further, the network is based upon a neural network schema. In an implementation, the measurement module 212 may measure the plurality of electric parameters of an electrical power distribution system and store storage information from a user device in system data 222.
[052] At block 304, one or more patterns are generated. The one or more patterns are generated by analyzing the plurality of electric parameters based on a machine learning methodology. Further, the one or more patterns are at least indicative of one of an electrical power generation and an electrical power usage. In the implementation, the generation module 214 may generate one or more patterns and store the one or more patterns in system data 222.
[053] At block 306, the plurality of electric parameters and the one or more patterns are communicated to one or more other systems in the network over a power line communication channel. In the implementation, the communication module 216 may communicate the plurality of electric parameters and the one or more patterns to one or more other systems in the network over a power line communication channel and store the communicated data in the system data 222.
[054] At block 308, one or more decisive actions are executed based on the plurality of electric parameters, the one or more patterns and predefined criteria. In the implementation, the execution module 218 may execute one or more decisive actions based on the plurality of electric parameters, the one or more patterns and predefined criteria and store the execution data in system data 222.
[055] Exemplary embodiments discussed above may provide certain advantages. Though not required to practice aspects of the disclosure, these advantages may include a method for controlling electrical power distribution system based on a neural network schema.
[056] Although implementations for methods and systems for controlling electrical power distribution system based on a neural network schema have been described in language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations for controlling electrical power distribution system based on a neural network schema.

CLAIMS:WE CLAIM:
1. A method for controlling electrical power distribution system based on a neural network schema, the method comprising:
measuring, by a processor of a system in a network, a plurality of electric parameters of an electrical power distribution system, wherein the plurality of electric parameters are measured by the network based upon a neural network schema;
generating, by the processor of a system in a network, one or more patterns by analyzing the plurality of electric parameters based on a machine learning methodology, wherein the one or more patterns is at least indicative of one of an electrical power generation and an electrical power usage;
communicating, by the processor of the system in the network, the plurality of electric parameters and the one or more patterns to one or more other systems in the network over a power line communication channel; and
performing, by the processor of the system in the network, one or more decisive actions based on the plurality of electric parameters, the one or more patterns and a predefined criteria.

2. The method of claim 1, wherein the decisive action comprises one of an activation of electric power supply, a deactivation of electric power supply, a generation of electric power usage statistics, a generation of electric power usage bill and a raising of alarm.

3. The method of claim 2, wherein the method further comprises
receiving a plurality of other systems electric parameters and a one or more other systems patterns associate to the one or more other systems; and
raising of alarm based on a comparison between the plurality of other systems electric parameters and the plurality of electric parameters.

4. The method of claim 2, wherein the alarm is raised for one of maintenance of network, maintenance of subsystem, leakage of electricity and theft of electricity.
5. The method of claim 1, wherein the predefined criterion comprises at least one of change in electrical power generation, change in electrical power usage, change in environmental conditions, change in efficiency and data error.

6. The method of claim 1, wherein the plurality of electric parameters comprises voltage, current, and power factor.

7. A system for controlling electrical power distribution system based on a neural network schema, the system comprising:
a memory; and
a processor coupled to the memory, wherein the processor executes instructions stored in the memory for:
measuring a plurality of electric parameters of an electrical power distribution system, wherein the plurality of electric parameters are measured by the network based upon a neural network schema;
generating one or more patterns by analyzing the plurality of electric parameters based on a machine learning methodology, wherein the one or more patterns is at least indicative of one of an electrical power generation and an electrical power usage;
communicating the plurality of electric parameters and the one or more patterns to one or more other systems in the network over a power line communication channel; and
performing one or more decisive actions based on the plurality of electric parameters, the one or more patterns and a predefined criteria.

8. The system of claim 7, wherein the decisive action comprises one of an activation of electric power supply, a deactivation of electric power supply, a generation of electric power usage statistics, a generation of electric power usage bill and a raising of alarm.

9. The system of claim 8, wherein the alarm is raised for one of maintenance of network, maintenance of subsystem, leakage of electricity and theft of electricity.

10. The system of claim 7, wherein the predefined criterion comprises at least one of change in electrical power generation, change in electrical power usage, change in environmental conditions, change in efficiency and data error.

11. The system of claim 7, wherein the plurality of electric parameters comprises voltage, current, and power factor.

12. A non-transitory computer program product having embodied thereon a computer program for controlling electrical power distribution system based on a neural network schema, the computer program product storing instructions, the instructions comprising instructions for:
measuring a plurality of electric parameters of an electrical power distribution system, wherein the plurality of electric parameters are measured by the network based upon a neural network schema;
generating one or more patterns by analyzing the plurality of electric parameters based on a machine learning methodology, wherein the one or more patterns is at least indicative of one of an electrical power generation and an electrical power usage;
communicating the plurality of electric parameters and the one or more patterns to one or more other systems in the network over a power line communication channel; and
executing one or more decisive actions based on the plurality of electric parameters, the one or more patterns and a predefined criteria.