CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
[001] The present application does not claim priority from any patent application.

TECHNICAL FIELD
[002] The present disclosure in general relates to the field of optimizing a performance of an Energy Conversion Plant. More particularly, the present invention relates to a system and method for optimizing a performance of an Energy Conversion Plant (ECP).
BACKGROUND
[003] Generally, main objective of an Energy Conversion Plant (ECP) product vendor is to achieve maximum efficiency during normal operating condition, during different weather condition and during the fault condition. A set of sensors are used to detect different operating conditions of the ECP. However, if any of the sensor fails, then the ECP may fail to work properly. Thus, using the sensors may not help to achieve the maximum efficiency in ECP. Further, an environmental condition may occur that is not faced by the ECP earlier. In this case, the ECP may not have idea regarding how to operate in the environmental condition. Thus, performance of the ECP may get degraded or the ECP may get damaged.
SUMMARY
[004] Before the present systems and methods for optimizing a performance of an Energy Conversion Plant (ECP), is described, it is to be understood that this application is not limited to the particular systems, and methodologies described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosure. It is also to be understood that the terminology used in the description is for the purpose of describing the particular versions or embodiments only, and is not intended to limit the scope of the present application. This summary is provided to introduce concepts related to systems and method for optimizing the performance of the ECP. 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 method for optimizing a performance of an Energy Conversion Plant (ECP). In one embodiment, the method may comprise obtaining sensor data, historical data, and operational data associated with a primary Energy Conversion Plant (ECP). The sensor data may comprise a pressure, a temperature, a wind speed, an air contamination, a wind direction, and an environmental temperature received from a set of sensors. The historical data may comprise a historical pressure, a historical temperature, a historical speed, a historical wind direction, and a historical environmental temperature. The operational data may comprise functioning of the primary ECP, and a historical action. Further, the method may comprise predicting an environmental condition corresponding to a predefined period based on the sensor data and the historical data. The method may further comprise obtaining solution data associated with the environmental condition from one or more secondary Energy Conversion Plants (ECPs). The solution data may comprise the working of the one or more secondary ECPs, and actions executed by the one or more secondary ECPs. Further, the method may comprise generating an action based on the solution data and the operational data. The method may further comprise executing the action, thereby optimizing the performance of primary ECP under the environmental condition.
[006] In another implementation, an edge system for optimizing a performance of an Energy Conversion Plant (ECP) is illustrated. The edge system comprises a memory and a processor coupled to the memory, further the processor is configured to execute instructions stored in the memory. In one embodiment, the processor may execute instructions stored in the memory for obtaining sensor data, historical data, and operational data associated with a primary Energy Conversion Plant (ECP). The sensor data may comprise a pressure, a temperature, a wind speed, an air contamination, a wind direction, and an environmental temperature received from a set of sensors. The historical data may comprise a historical pressure, a historical temperature, a historical speed, a historical wind direction, and a historical environmental temperature. The operational data may comprise functioning of the primary ECP, and a historical action. Further, the processor may execute instructions stored in the memory for predicting an environmental condition corresponding to a predefined period based on the sensor data and the historical data. The processor may further execute instructions stored in the memory for obtaining solution data associated with the environmental condition from one or more secondary Energy Conversion Plants (ECPs). The solution data may comprise working of the one or more secondary ECPs, and actions executed by the one or more secondary ECPs. Further, the processor may execute instructions stored in the memory for generating an action based on the solution data and the operational data. The processor may further execute instructions stored in the memory for executing the action, thereby optimizing the performance of primary ECP under the environmental condition.
BRIEF DESCRIPTION OF DRAWINGS
[007] The detailed description is described 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 like features and components.
[008] Figure 1 illustrates a network implementation of a system for optimizing a performance of an Energy Conversion Plant (ECP), in accordance with an embodiment of the present subject matter.
[009] Figure 2 illustrates the system for optimizing the performance of the ECP, in accordance with an embodiment of the present subject matter.
[0010] Figure 3 illustrates a method for optimizing a performance of an Energy Conversion Plant (ECP), in accordance with an embodiment of the present subject matter.
DETAILED DESCRIPTION
[0011] Some embodiments of the present disclosure, illustrating all its features, will now be discussed in detail. The words “obtaining”, “predicting”, “generating”, “executing”, 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 systems 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 for optimizing a performance of an Energy Conversion Plant (ECP) are now described. The disclosed embodiments of the system and method for optimizing the performance of the ECP are merely exemplary of the disclosure, which may be embodied in various forms.
[0012] 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 for optimizing a performance of an Energy Conversion Plant (ECP) is not intended to be limited to the embodiments illustrated, but is to be accorded the widest scope consistent with the principles and features described herein.
[0013] The present subject matter relates to optimize a performance of an Energy Conversion Plant (ECP). In one embodiment, sensor data, historical data and operational data associated with a primary ECP may be obtained. Further, the sensor data and the historical data may be used to predict an environmental condition corresponding to a predefined period. Once the environmental condition is predicted, solution data associated with the environmental condition may be obtained from one or more secondary Energy Conversion Plants (ECPs). The solution data and the operational data may be further used to generate an action. The action may be further executed by the primary ECP, thereby optimizing the performance of the primary ECP under the environmental condition.
[0014] Referring now to Figure 1, a network implementation 100 of a system 102 for optimizing a performance of an Energy Conversion Plant (ECP) is disclosed. In one embodiment, the Energy Conversion Plant (ECP) may be one of a solar panel, a wind turbine and the like. Further, a system 102-1, a system 102-2, collectively referred to as the system 102 hereinafter, may be implemented at each Energy Conversion Plant (ECP). In one aspect, the system 102 may be referred to as an edge system 102. Further, a set of sensors may be mounted at each ECP. The set of sensors may be further connected to the system 102. In one aspect, the system 102 of each ECP may be connected to each other.
[0015] It will be understood that the system 102 may be accessed by multiple users through one or more user devices 104-1, 104-2…104-N, collectively referred to as user device 104 hereinafter, or applications residing on the user device 104. Examples of the user device 104 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device, and a workstation. The user device 104 may be communicatively coupled to the system 102 through a network 106.
[0016] In one implementation, the network 106 may be a wireless network, a wired network or a combination thereof. The network 106 may be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network 106 may either be a dedicated network or a shared network. The shared network 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), and the like, to communicate with one another. Further, the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
[0017] Further, the system 102 of each ECP may be connected to a master server 108. The master server 108 may be referred as Artificial Intelligence (AI) master server. The master server 108 may be configured to receive data from the set of sensors associated with each ECP. In one example, the AI master server may be connected with three different power generation farms like wind power generation farm, Solar panel power generation farm and solar thermal power generation farm all are AI enabled ECP’s, but non-similar. Further, these three ECPs may be located at different locations. The AI master server 108 may collect the sensor data from this different power generation farm and store this as data mining.
[0018] The set of sensors may comprise common sensors, product specific sensors and the like. These sensors monitor the environmental weather condition. The common sensors may be used to monitor pressure, temperature, wind speed, Air contamination, wind direction, GPS and the like. The product specific sensors may be used to monitor product working and the fault detection. The product specific sensors may not be the common between each ECP. In one embodiment, the product specific sensors may be highly correlated with the common sensors. This is because each ECP is highly influenced by the weather conditions.
[0019] In one example, a wind turbine and a solar panel may be the ECP. The set of sensors may comprise a temperature sensor for bearing, stator winding, transformer, an accelerometer tower sway, gear box, a pressure sensor for blade, a position sensor for tower levelling, feathering and the like.
[0020] In one embodiment, the system 102 may obtain sensor data, historical data and operational data associated with a primary Energy Conversion Plant (ECP). The sensor data may comprise a pressure, a temperature, a speed, an air contamination, a direction, an environmental temperature, a location and the like. The sensor data may be received from the set of sensors mounted at the primary ECP. The historical data may comprise a historical pressure, a historical temperature, a historical speed, a historical direction, a historical environmental temperature and like. The operational data may comprise functioning of the primary ECP, a historical action executed by the primary ECP and the like.
[0021] Once the sensor data, the historical data and the operational data is obtained, the system 102 may predict an environmental condition corresponding to a predefined period. The environmental condition may be predicted using the sensor data and the historical data.
[0022] Upon predicting the environmental condition, the system 102 may obtain solution data associated with the environmental condition from one or more secondary Energy Conversion Plants (ECPs). The solution data may comprise working of the one or more secondary ECPs, actions executed by the one or more ECPs and the like.
[0023] The one or more secondary ECPs may be present in a vicinity of the primary ECP. The one or more secondary ECPs may be one of a similar ECP and a non-similar ECP. In one example, if the primary ECP is a wind turbine, then other wind turbine in the vicinity of the primary ECP may be the similar ECP. Further, a solar panel in the vicinity of the primary ECP may be the non-similar ECP.
[0024] In one embodiment, the one or more secondary ECPs may be connected to same master server. In another embodiment, the one or more secondary ECPs may be connected to different master servers.
[0025] In one embodiment, if the solution data is not present at the one or more secondary ECPs, then the system 102 may obtain the solution data from the master server 108.
[0026] Once the solution data is obtained, the system 102 may generate an action. The action may be generated using the solution data and the operational data. The action may be one of generating an alert, switching off the primary ECP, changing parameters of the primary ECP and the like.
[0027] Upon generation of the action, the system 102 may execute the action. The action may be executed to mitigate the environmental condition. Based on the execution of the action, the system 102 may optimize the performance of the primary ECP under the environmental condition. In one aspect, the system 102 may use health data associated with the primary ECP to modify the action. The health data may comprise an age, a speed, a height, a size and the like.
[0028] In one embodiment, the system 102 may identify a current environmental condition based on analysis of the sensor data and the historical data. The current environmental condition may be compared with the environmental condition predicted. If the current environmental condition is similar to the environmental condition, then the system 102 may execute the action to mitigate the current environmental condition.
[0029] In another embodiment of the present subject matter, the primary ECP may be referred as an ECP edge and the secondary ECPs may be referred to as other ECP edges. The environmental condition may be referred as a weather condition. The ECP edge may request data from other ECP edges present in a network. The data may be in xml file format which contain all common sensor data. It is to be noted that all sensor values may not be used by the ECP edge, depend upon the requirement a particular sensor values may be filtered by the received ECP edge. Further, the ECP edge may have a storage and save its own developed/borrowed knowledge, as a file (like .sav) and its tagged with respect to the environmental condition. The ECP edge may predict the environmental condition using the sensor values associated with the ECP edge. The ECP edge may further verify its own knowledge and check if it is able to handle the predicted weather condition or not. If not, the ECP edge may request for a knowledge from the other ECP edges by using the tag name. The other ECP edges may response with respect to the availability of the knowledge. If the other ECP edges have the requested knowledge, then the knowledge file may get transferred to the requested ECP edge as a response. And the knowledge file may be used to handle the predicted weather condition. In one embodiment, knowledge transfer may take place between similar AI based ECP Edge.
[0030] In one embodiment, if the requested knowledge is not available at the other ECP edge, then request may be sent to the AI master server. The ECP edge may request the required sensor data from the AI master server with respect to the predicted weather condition. Further, the required data may get transferred as response in a xml file format. Once the sensor data xml file is received, the ECP edge may compute the knowledge by using machine learning algorithm. Further, the ECP edge may save the developed knowledge as a .sav file in the storage. The developed knowledge may get transferred to other ECP edges on request bases.
[0031] In one embodiment, the AI master server may obtain sensor data from the common sensors associated with a set of ECPs, which is connected in the network. Further, the sensor data may get segregated with respect to a location and an environment condition of each ECP. Furthermore, the AI mater server may simulate the data to other non-similar ECPs located in similar location/environment conditions. Thus may make the other AI based ECP’s to develop their own AI knowledge with respect to their own application. It is to be noted that this can be done by enabling the diagnostic module in each ECP, which receives the input from different ECPs and develop their own AI knowledge without affecting the existing operation. This can be done to keep the ECP’s at maximum efficiency throughput. Further, each ECP edge may have an interconnection between them to share the sensor information’s to develop the knowledge and fault handling without involving the master servers.
[0032] In one exemplary embodiment, the AI master server may collect the sensor data from this different power generation farm and store this as data mining. Further, a wind power generation farm located on a snow location and windy climate location. The wind generation farm may develop knowledge with respect to the environment location. Further, the AI master server may simulate the snow and windy climate data from the data mine to other non-similar ECP’s farms like solar panel and solar thermal farms. This ECP farm may receive the data through Ethernet connectivity and pass to the offline knowledge builder repository. In this case, the data may be converted to knowledge through offline. This means develops the knowledge without disturbing the existing operation of the system. And finally merge the developed knowledge to existing AI knowledge on the ECP’s.
[0033] In one embodiment, the product specific sensors failure may occur. In this case, the product specific sensor failure may be handled by two methods such as a hold or stop and a safe mode run. The hold and stop may correspond to completely shutting down the system 102 and in the safe mode, instead of shutting down the system 104, make the system run in safe mode which will give some output but not efficiently. If the product specific sensor fails instead of running the ECP in safe mode, the sensor value can be predicting by using the historical data which correlated with the common sensors. This prediction is enough to make the ECP to run in with good efficiency. Further, the common sensor failure values can also be predicted. The sensor value associated with the common sensors may be borrowed from other ECP edges which is in somewhat similar environment condition. It is to be noted that instead of running the ECP in safe mode, the above method makes the ECP run in the normal mode and retain the efficiency during the sensor failure condition.
[0034] Referring now to figure 2, the system 102 for optimizing a performance of an Energy Conversion Plant (ECP) 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, at least one processor 202 may be configured to fetch and execute computer-readable instructions stored in the memory 206.
[0035] 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 directly or through the user device 104. 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 may 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.
[0036] The memory 206 may include any computer-readable medium 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.
[0037] The modules 208 may include routines, programs, objects, components, data structures, and the like, which perform particular tasks, functions or implement particular abstract data types. In one implementation, the module 208 may include an obtaining module 212, a predicting module 214, a solution obtaining module 216, a generating module 218, an executing module 220, and other modules 222. The other modules 222 may include programs or coded instructions that supplement applications and functions of the system 102.
[0038] The data 210, amongst other things, serve as a repository for storing data processed, received, and generated by one or more of the modules 208. The data 210 may also include a repository 224, and other data 226. In one embodiment, the other data 226 may include data generated as a result of the execution of one or more modules in the other modules 222.
[0039] In one implementation, a user may access the system 102 via the I/O interface 204. The user may be registered using the I/O interface 204 in order to use the system 102. In one aspect, the user may access the I/O interface 204 of the system 102 for obtaining information, providing input information or configuring the system 102.
[0040] In one embodiment, the obtaining module 212 may obtain sensor data, historical data and operational data associated with a primary Energy Conversion Plant (ECP). The sensor data may comprise a pressure, a temperature, a speed, an air contamination, a direction, an environmental temperature and the like. The sensor data may be received from a set of sensors mounted at the primary ECP. The historical data may comprise a historical pressure, a historical temperature, a historical speed, a historical direction, a historical environmental temperature and the like. The operational data may comprise functioning of the primary ECP, a historical action executed by the primary ECP and the like. The primary ECP may be one of a wind power generation, a solar power generation, a solar thermal power generation and the like.
[0041] The set of sensors may comprise common sensors, product specific sensors and the like. These sensors monitor the environmental weather condition. The common sensors may be used to monitor pressure, temperature, wind speed, Air contamination, wind direction, GPS and the like. The product specific sensors may be used to monitor product working and the fault detection.
[0042] Further, the predicting module 214 may predict an environmental condition. The environmental condition may be predicted using the sensor data and the historical data. In one example, the sensor data and the historical data may be compared to predict the environmental condition. The environmental condition may be one of a storm, a heavy rainfall, a snow fall and the like.
[0043] Once the environmental condition is predicted, the solution obtaining module 216 may obtain solution data from one or more secondary Energy Conversion Plants (ECPs). The solution data may be associated with the environmental condition. The solution data may comprise working of the one or more secondary ECPs, actions executed by the one or more secondary ECPs and the like. The solution data may be in a XML format. In one embodiment, the primary ECP may send a request to the one or more secondary ECPs for the solution data. Based on the request, the one or more secondary ECPs may transmit the solution data to the primary ECP.
[0044] The one or more secondary ECPs may be in a vicinity of the primary ECP. In one example, the one or more secondary ECPs may be connected to a master server 108 associated with the primary ECP. In another example, the one or more secondary ECPs may be connected to different master servers.
[0045] The one or more secondary ECPs may be one of a similar ECP and a non-similar ECP. The one or more secondary ECPs may be one of a wind power generation, solar thermal power generation and the like. In one example, construe wind power generation as the primary ECP. In this case, the solar power generation may be a non-similar ECP. Further, other wind power generation may be the similar ECP.
[0046] In one embodiment, the obtaining module 216 may obtain the solution data from the master server 108 when the solution data is not present at the one or more secondary ECPs. The master server 108 may store a repository. The repository may comprise the sensor data from a set of ECPs present in a network. In one aspect, the master server 108 may be configured to receive the sensor data from the set of sensors mounted at each ECP from the set of ECPs.
[0047] Once the solution data is obtained, the generating module 218 may generate an action. The action may be generated using the solution data and the operational data. The solution data and the operational data may be analysed using a machine learning technique. The action may be used to mitigate the environmental condition. The action may be one of switching off the primary ECP, changing parameters of the primary ECP, generating an alert and the like. The action may be modified using health data associated with the primary ECP. The health data may be obtained from the primary ECP. The health data may comprise an age, a height, a size and the like.
[0048] In one embodiment, knowledge of the primary ECP may be developed based on the solution data from the one or more secondary ECPs. The knowledge may be developed using the machine learning technique. Further, the knowledge may be used to generate the action.
[0049] Once the action is generated, the executing module 220 may execute the action. Based on the execution of the action, the executing module 220 may optimize the performance of the primary ECP. In one embodiment, a current environmental condition associated with the primary ECP may be identified. The current environmental condition may be identified using the sensor data and the historical data. Further, the current environmental condition may be compared with the environmental condition. If the current environmental condition is similar to the environmental condition, then the action associated with the environmental condition may be executed. Based on the execution, the performance of the primary ECP may be optimized.
[0050] In one embodiment, optimizing the performance of the primary ECP may correspond to one of increasing the performance based on changing the parameters. In another embodiment, optimizing the performance may correspond to decreasing the performance based on changing the parameters. In yet another embodiment, optimizing the performance may correspond to operating the primary ECP in safe mode. In yet another embodiment, optimizing the performance may correspond to switching off the primary ECP to mitigate the environmental condition.
[0051] In one exemplary embodiment, construe a wind turbine plant and a solar energy plant as an ECP edges used to generate power. The wind turbine plant may require continuous wind, and the solar energy plant may require proper sun shine to generate the power efficiently. Thus, the wind turbine plant and the solar energy plant may be placed in the location where there are less obstacles like Sea, desert, hills etc. The wind turbine plant and the solar energy plant are non-similar AI based ECP edges. It is to be noted that the wind turbine plant and the solar energy plant may be placed in similar kind of location or environment.
[0052] In this case, the wind turbine plant may be a primary ECP and the solar energy plant may be a secondary ECP. The set of sensors may be mounted at the primary ECP and the secondary ECP. The primary ECP and the secondary ECP may be connected to the master server. Further, the sensor data, the historical data and the operation data of the wind turbine plant may be received. The sensor data may comprise a wind direction, wind speed, a wind turbine speed, and the like. The historical data may comprise a historical wind direction, a historical wind speed, a historical wind turbine direction and the like. Based on the sensor data and the historical data, an environmental condition of the wind turbine plant may be predicted. The environmental condition may be related to tower position turn, wind direction change and the like. In this case, the primary ECP may receive solution data from the secondary ECP. If the secondary ECP does not have the solution data, then the primary ECP may receive the solution data from the master server. Further, an action may be generated based on the solution data. Using the action, the knowledge of the wind turbine may get developed. The action may be executed by the wind turbine. In this case, efficiency of the wind turbine is improved. When the environmental condition as predicted occurs in future, the wind turbine may directly use its knowledge to mitigate the environmental condition.
[0053] 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.
[0054] Some embodiments of the system and the method is configured handle future environmental condition changes.
[0055] Some embodiments of the system and the method is configured to share knowledge across similar and non-similar AI based ECP’s, without involving the server or cloud.
[0056] Some embodiments of the system and method comprises AI network farm having an intelligence to maintain, knowledge and input simulation on the non- similar AI based ECP’s.
[0057] Some embodiments of the system and method is configured to predict a weather condition or wind flow by ECP edge itself using common sensors, without involving the satellite image, weather forecasting report from different server or cloud.
[0058] Some embodiments of the system and method is configured to develop knowledge of an ECP edge during run time, without affect the existing operation.
[0059] Some embodiments of the system and method is configured to predict failure of an ECP edge and send a message to other ECP edges to enable and balance the load, this maintain the efficiency during run time.
[0060] Referring now to figure 3, a method 300 for optimizing a performance of an Energy Conversion Plant (ECP), is disclosed 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, and the like, that perform particular functions or implement particular abstract data types. The method 300 may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.
[0061] 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 or alternate methods. Additionally, individual blocks may be deleted from the method without departing from the spirit and scope of the subject matter described herein. Furthermore, the method 300 can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method may be considered to be implemented in the above described system 102.
[0062] At block 302, sensor data, historical data and operational data associated with a primary Energy Conversion Plant (ECP) may be obtained. In one implementation, the obtaining module 212 may obtain the sensor data, the historical data and the operational data. The sensor data may comprise a pressure, a temperature, a speed, an air contamination, a direction, an environmental temperature and the like. The sensor data may be received from a set of sensors mounted at the primary ECP. The historical data may comprise a historical pressure, a historical temperature, a historical speed, a historical direction, a historical environmental temperature and the like. The operational data may comprise functioning of the primary ECP, a historical action executed by the primary ECP and the like.
[0063] At block 304, an environmental condition may be predicted. In one implementation, the predicting module 214 may predict the environmental condition corresponding to a predefined period. The environmental condition may be predicted using the sensor data and the historical data.
[0064] At block 306, solution data associated with the environmental condition may be obtained. In one implementation, the solution obtaining module 216 may obtain the solution data from one or more secondary Energy Conversion Plants (ECPs). The solution data may comprise working of the one or more secondary ECPs, actions executed by the one or more secondary ECPs and the like. The one or more secondary ECPs may be in a vicinity of the primary ECP.
[0065] At block 308, an action may be generated. In one implementation, the generating module 218 may generate the action. The action may be generated using the solution data and the operational data. The action may be one of changing parameters of the primary ECP, generating an alert, switching off the primary ECP and the like.
[0066] At block 310, the action may be executed. In one implementation, the executing module 220 may execute the action. Based on the execution of the action, a performance of the primary ECP may be optimized under the environmental condition.
[0067] Although implementations for systems and methods for optimizing a performance of an Energy Conversion Plant (ECP) have been described, 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 optimizing the performance of the ECP.

Claims:
1. A method for optimizing a performance of an Energy Conversion Plant (ECP), the method comprising:
obtaining, by a processor (202), sensor data, historical data, and operational data associated with a primary Energy Conversion Plant (ECP), wherein the sensor data comprises a pressure, a temperature, a wind speed, an air contamination, a wind direction, and an environmental temperature received from a set of sensors, wherein the historical data comprises a historical pressure, a historical temperature, a historical speed, a historical wind direction, and a historical environmental temperature, and wherein the operational data comprise functioning of the primary ECP, and a historical action;
predicting, by the processor (202), an environmental condition corresponding to a predefined period based on the sensor data and the historical data;
obtaining, by the processor (202), solution data associated with the environmental condition from one or more secondary Energy Conversion Plants (ECPs), wherein the solution data comprises working of the one or more secondary ECPs, and actions executed by the one or more secondary ECPs;
generating, by the processor (202), an action based on the solution data and the operational data; and
executing, by the processor (202), the action, thereby optimizing the performance of primary ECP under the environmental condition.

2. The method as claimed in claim 1, wherein the one or more secondary ECPs are in a vicinity of the primary ECP.

3. The method as claimed in claim 1, wherein the action is one of generating an alert, changing parameters of the primary ECP, and switching off the primary ECP.

4. The method as claimed in claim 1, further comprises obtaining health data associated with the primary ECP, wherein the health data comprises an age, a speed, a size, and a height, and wherein the action is modified based on health data of the primary ECP.

5. The method as claimed in claim 1, wherein the executing further comprises
identifying a current environmental condition of the primary ECP using the sensor data and the historical data;
comparing the current environmental condition with the environmental condition; and
executing the action when the current environmental condition is similar to the environmental condition.

6. The method as claimed in claim 1, wherein the one or more secondary ECPs is one of a similar ECP and a non-similar ECP.

7. An edge system (102) for optimizing a performance of an Energy Conversion Plant (ECP), the system comprising:
a memory (206);
a processor (202) coupled to the memory (206), wherein the processor (202) is configured to execute instructions stored in the memory (206) to:
obtain sensor data, historical data, and operational data associated with a primary Energy Conversion Plant (ECP), wherein the sensor data comprises a pressure, a temperature, a wind speed, an air contamination, a wind direction, and an environmental temperature received from a set of sensors, wherein the historical data comprises a historical pressure, a historical temperature, a historical speed, a historical wind direction, and a historical environmental temperature, and wherein the operational data comprise functioning of the primary ECP, and a historical action;
predict an environmental condition corresponding to a predefined period based on the sensor data and the historical data;
obtain solution data associated with the environmental condition from one or more secondary Energy Conversion Plants (ECPs), wherein the solution data comprises working of the one or more secondary ECPs, and actions executed by the one or more secondary ECPs;
generate an action based on the solution data and the operational data; and
execute the action, thereby optimizing the performance of primary ECP under the environmental condition.

8. The edge system (102) as claimed in claim 7, wherein the one or more secondary ECPs are in a vicinity of the primary ECP.

9. The edge system (102) as claimed in claim 7, wherein the action is one of generating an alert, changing parameters of the primary ECP, and switching off the primary ECP.

10. The edge system (102) as claimed in claim 7, further configured to obtain health data associated with the primary ECP, wherein the health data comprises an age, a speed, a size, and a height, and wherein the action is modified based on health data of the primary ECP.

11. The edge system (102) as claimed in claim 7, wherein the executing further comprises
identifying a current environmental condition of the primary ECP using the sensor data and the historical data;
comparing the current environmental condition with the environmental condition; and
executing the action when the current environmental condition is similar to the environmental condition.

12. The edge system (102) as claimed in claim 7, wherein the one or more secondary ECPs is one of a similar ECP and a non-similar ECP.