Power primary equipment intelligent operation and maintenance simulation test method based on production operation support system

Abstract

The application provides a power primary equipment intelligent operation and maintenance simulation test method based on a production operation support system, which comprises the following steps: constructing a simulation model system of core equipment of the production operation support system, and embedding an artificial intelligence platform interaction module in the simulation model to realize real-time data interaction between the simulation model and the artificial intelligence platform. The simulation model calls the power primary equipment state evaluation and fault diagnosis algorithm of the artificial intelligence platform through the artificial intelligence platform interaction module to simulate and evaluate the equipment operation condition and potential fault hidden danger in real time. When a fault hidden danger is detected, the corresponding intelligent inspection scheme is automatically matched and issued according to the preset equipment type, fault type and operation state in the intelligent operation and maintenance plan library of the artificial intelligence platform. Through the above process, the intelligent operation and maintenance state of the power system primary equipment based on the production operation support system is precisely tested and closed-loop verified.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system operation and maintenance and simulation test, and particularly relates to a power primary equipment intelligent operation and maintenance simulation test method based on a production operation support system. BACKGROUND

[0002] With the continuous expansion of the scale of the power system and the acceleration of the digital transformation process, higher requirements are put forward for the operation state monitoring and intelligent maintenance of the power primary equipment, and the production operation support system integrates the Internet of Things and artificial intelligence technology to provide core support for the intelligent operation and maintenance of the power primary equipment. However, the lack of related technical simulation test scheme affects the research, test and promotion of the intelligent operation and maintenance technology of the power primary equipment. SUMMARY

[0003] To solve the above problems, the present application discloses a power primary equipment intelligent operation and maintenance simulation test method based on a production operation support system, and provides a simulation test method capable of overall planning of overall data acquisition, state evaluation, fault diagnosis and intelligent operation and maintenance plan matching, thereby providing a comprehensive, real and efficient data and communication simulation platform for actual use.

[0004] To achieve the above purpose, the present application adopts the following technical scheme: a power primary equipment intelligent operation and maintenance simulation test method based on a production operation support system, comprising the following contents:

[0005] Based on a topology structure of a production operation support system to be tested and simulation models of physical devices in the production operation support system, a power system power primary equipment intelligent operation and maintenance simulation project based on the production operation support system is configured, wherein the simulation model of at least one of the physical devices is configured with an artificial intelligence platform interaction module, the physical devices at least include a production analysis processing host, an intelligent gateway, a production data one-way collection device and an intelligent sensing terminal, the intelligent sensing terminal includes monitoring devices and auxiliary devices, the monitoring devices include measurement and control, protection, safety control, fault recording, online monitoring, power quality monitoring and digital metering, and the auxiliary devices include fire fighting, security defense, dynamic environment system, camera, robot and unmanned aerial vehicle; the intelligent operation and maintenance simulation project is executed, and when the intelligent operation and maintenance simulation project is executed: based on the artificial intelligence platform interaction module, a state evaluation and fault diagnosis algorithm of the power primary equipment provided by an artificial intelligence platform is called to evaluate the running state of the power primary equipment based on the state evaluation and fault diagnosis algorithm according to data obtained from the simulation model of the monitoring device and running information of a power system and state information of power equipment, so as to determine whether the power primary equipment has a fault hidden danger; and if it is determined that the power primary equipment has a fault hidden danger, a corresponding intelligent inspection scheme is matched out according to an intelligent operation and maintenance plan library, and the simulation model of the production analysis processing host outputs the intelligent inspection scheme to the simulation model of the auxiliary device through the simulation model of the intelligent gateway.

[0006] The present application has the following advantages due to the above technical solutions:

[0007] 1. The present application covers multiple dimensions such as power system running data, device state, auxiliary device operation and maintenance information, etc., and can simulate the actual running of a substation local area network and a wide area integrated data network based on a production operation support system in a simulation environment, to provide a more real data basis for subsequent fault diagnosis and intelligent operation and maintenance.

[0008] 2. The present application sets an artificial intelligence interaction module as a data interaction interface to realize efficient connection of the simulation model and the artificial intelligence platform, so that the simulation system can call built-in power primary equipment state evaluation and fault diagnosis algorithms and match corresponding inspection schemes in real time, to realize seamless integration of power secondary system simulation and artificial intelligence technology.

[0009] 3. The present application provides a simulation test method capable of overall planning of data collection, state evaluation, fault diagnosis and intelligent operation and maintenance plan matching, to provide a comprehensive, real and efficient data and communication simulation platform for actual use. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced. Obviously, the accompanying drawings in the following description only aim to explain the embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of the provided drawings.

[0011] Figure 1 The present application is a production operation support system-based power primary equipment intelligent operation and maintenance simulation test method implementation flowchart.

[0012] Figure 2 The present application is a production operation support system-based power primary equipment intelligent operation and maintenance simulation test method implementation flowchart.

[0013] Figure 3 The present application is a production operation support system-based power primary equipment intelligent operation and maintenance simulation test method implementation flowchart.

[0014] Figure 4 The present application is a production operation support system-based power primary equipment intelligent operation and maintenance simulation test method implementation flowchart.

[0015] Figure 5a The present application is a production operation support system-based power primary equipment intelligent operation and maintenance simulation test method implementation flowchart.

[0016] Figure 5b The present application is a production operation support system-based power primary equipment intelligent operation and maintenance simulation test method implementation flowchart.

[0017] Figure 5c The present application is a production operation support system-based power primary equipment intelligent operation and maintenance simulation test method implementation flowchart.

[0018] Figure 6a The present application is a production operation support system-based power primary equipment intelligent operation and maintenance simulation test method implementation flowchart.

[0019] Figure 6b The present application is a production operation support system-based power primary equipment intelligent operation and maintenance simulation test method implementation flowchart. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0021] The flowchart shown in the drawing is only an example and does not necessarily include all contents and operations / steps, nor does it necessarily need to be executed in the order described. For example, some operations / steps can also be decomposed, combined or partially merged, so the actual execution order can be changed according to the actual situation.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The use of the terms "include", "includes" or "including" in this specification is merely to describe certain examples of embodiments of the application, and is not intended to be limiting of the application.

[0023] Referring to Figure 1 , Figure 1 is a flowchart of a power primary equipment intelligent operation and maintenance simulation test method based on a production operation support system provided by the embodiments of the application. The simulation test method can be applied in a computer device and used for simulation testing of power primary equipment intelligent operation and maintenance based on a production operation support system in a professional field, such as for primary equipment such as main transformers, circuit breakers, disconnectors and the like in smart grids / smart substations, and for simulation testing of the whole process of operation and process of equipment health state monitoring, fault prediction and diagnosis, intelligent maintenance strategy drilling and emergency plan verification based on a production operation support system framework. The reliability, real-time performance and effectiveness of the intelligent operation and maintenance system architecture based on the production operation support system and the power primary equipment state evaluation and fault diagnosis algorithm are verified through exemplary testing under multiple working conditions and multiple scenarios.

[0024] In some embodiments, the simulation environment can be based on OPNET simulation software, but is not limited to using OPNET simulation software. Other network simulation software such as NS-2, MATLAB, etc. can also be used to build the simulation environment of the embodiments of the application. The artificial intelligence platform can use a Python artificial intelligence platform, but is not limited to using a Python artificial intelligence platform. Other artificial intelligence platforms such as PyTorch, Stable Baselines3, etc. can also be used to build the artificial intelligence platform of the embodiments of the application.

[0025] Referring to Figure 1 , the power primary equipment intelligent operation and maintenance simulation test method based on a production operation support system of the embodiments of the application includes steps S110-S140.

[0026] Step S110, obtaining a simulation model of a production operation support system, including all devices for implementing power primary equipment intelligent operation and maintenance application simulation.

[0027] In some embodiments, the simulation model of the physical device in the production operation support system includes a simulation structure of a production analysis processing host simulation model, an intelligent gateway simulation model, a production data unidirectional acquisition device simulation model, and an intelligent sensing terminal simulation model as shown in Figure 2 The seven-layer protocol model architecture based on OSI includes an application layer, a presentation layer, a session layer, a transport layer, a network layer, a data link layer, and a plurality of physical layers, and each of the physical layers includes at least one pair of sending / receiving ports, can realize the intelligent operation and maintenance device model architecture based on TCP / IP, and each simulation model supports a plurality of communication protocols (such as IEC-104, DL / T 860, MODBUS, etc.), simulates the data generated by each physical device in the production operation support system and the transmission thereof.

[0028] In order to realize the calling of the power primary equipment state evaluation and fault diagnosis algorithm of the artificial intelligence platform in the simulation environment, the calling of the intelligent operation and maintenance preplan operation and maintenance library, and the nesting of the artificial intelligence platform interaction module in the application layer of the simulation model, the core of the artificial intelligence platform interaction module is a connection submodule, a request submodule, a transmission submodule, a response submodule, and a release submodule.

[0029] For example, as shown in Figure 2 The artificial intelligence platform interaction module is set as a process model state machine in the OPNET simulation software, the connection submodule corresponds to a "connection building" state, the request submodule corresponds to an "initiating request" state, the transmission submodule corresponds to an "initiating transmission" state, the response submodule corresponds to a "receiving response" state, and the release submodule corresponds to a "resource release" state, and the execution logic of the state jump includes:

[0030] 1) "initialization" state, when the OPNET simulation starts, a simulation start interrupt is triggered to enter the "initialization" state, the interaction attribute storage unit, the interaction data storage unit, and the attribute setting parameter of the simulation model are initialized, the interaction attribute storage unit stores the artificial intelligence platform server address, port, AI model access path, authorization information, data type information, and data length in the request header, the interaction data storage unit stores the power system operation information and power equipment state information in the request body, the attribute setting parameter of the simulation model refers to the setting of the interaction mode between the simulation model and the artificial intelligence platform, which can be directly calling the state evaluation and fault diagnosis algorithm of the power primary equipment, or calling the diagnosis result of the state evaluation and fault diagnosis algorithm of the power primary equipment, the state evaluation and fault diagnosis algorithm of the power primary equipment includes a transformer device intelligent analysis algorithm, a voltage transformer and current transformer device intelligent analysis algorithm, a lightning arrester device intelligent analysis algorithm, and a system harmonic intelligent analysis algorithm, etc. After the "initialization" state is executed, it unconditionally enters the "idle / waiting" state.

[0031] 2) "Idle / waiting" state, in the "Idle / waiting" state, the simulation model waits for the external interrupt initiated by the artificial intelligence platform server and / or the interrupt triggered by the simulation model according to its own needs, and jumps to different states according to different interrupt types.

[0032] 3) "Connection building" state, and only when the artificial intelligence platform server responds to the interrupt trigger, jump to the "connection building" state, in the "connection building" state, the simulation model and the artificial intelligence platform server try to establish a communication connection channel, if the channel building function returns "false", it remains in the "connection building" state, when the communication building function returns "ture", the communication connection is built and jumps to the "initiate request" state.

[0033] 4) "Initiate request" state, in the "initiate request" state, the simulation model constructs a request header containing the server address, port, AI model access path, authorization information, data type information and data length and initiates a connection request, after the request header is constructed and the artificial intelligence platform receives the request header, jump to the "idle / waiting" state.

[0034] 5) "Initiate transmission" state, and only when the simulation model receives the operation information of the power system and the operation information of the power equipment and the connection channel has been established, jump to the "initiate transmission" state, in the "initiate transmission" state, the simulation model formats the operation information of the power system and the state information of the power equipment into a string and writes it into the request body and into the output stream, in this state, if the platform receiving function has not "ture" Boolean parameter, it remains in the "initiate transmission" state, when the platform receiving function returns "ture", jump to the "idle / waiting" state.

[0035] 6) "Receive response" state, and only when the artificial intelligence platform triggers the response body sending interrupt, jump to the "receive response" state, in the "receive response" state, the simulation model analyzes the response body returned by the artificial intelligence platform and the response body data through the response body analysis function, when the response body analysis function returns "false", it remains in the "receive response" state, when the response body analysis function returns "ture", it jumps to the "idle / waiting" state.

[0036] 7) "Resource release" state, and only when the simulation model data transmission and algorithm calling are completed, jump to the "resource release" state, in the "resource release" state, the interactive data storage unit resources are released through the buffer resource release function, after the response buffer resource release is completed, it is forced to jump to the "idle / waiting" state, and waits for the next interrupt trigger state jump.

[0037] For example, the physical device based on the production operation support system is modeled and simulated in OPNET software, the artificial intelligence platform adopts a Python platform, and please refer to Figure 3 , the overall architecture of the interaction between the two. With reference to the embodiment, the interaction between the simulation model and the artificial intelligence platform includes the following steps:

[0038] 1) After the OPNET simulation end is started, a self-defined C language code is embedded in a simulation process model, an artificial intelligence platform interaction module is activated, an artificial intelligence platform service end port is bound to a simulation environment local port (such as a TCP 65432), and a listening state is entered, that is, Figure 2 the "idle / waiting" state.

[0039] 2) The OPNET simulation engine triggers data collection of power system power information, power equipment state information and auxiliary equipment operation information at a preset time interval (for example, every 0.1 seconds of simulation time). The collected multi-dimensional state data are converted into a standardized string format, as shown in Figure 4 , "substation 1 outgoing line: voltage 220 kV, frequency 50 Hz, active power 10.5 MW • transformer T1: running state "normal", temperature 85 ℃ • cooling pump: running, fan speed 1200 rpm" is converted into a standard string format, and the encapsulated state data are sent to the Python service end through a transmission submodule in the artificial intelligence interaction module.

[0040] 3) The Python service end receives the original state string through the service end port, parses it into a data type state vector (such as [220, 50, 10.5, 0, 85, 1, 1200]), inputs the state vector into a pre-trained reinforcement learning model (such as PPO, DQN), produces a decision action (such as issuing a device fault diagnosis algorithm, issuing an intelligent operation and maintenance plan library), and verifies the effectiveness of the decision action, filters illegal values (such as discrete actions exceeding the preset range).

[0041] 4) The Python service end encodes the verified decision action into a string, returns it to the artificial intelligence platform interaction module in the simulation model in OPNET through the service end port, the OPNET process model receives the decision action, parses it and continues subsequent simulation.

[0042] 5) The OPNET simulation engine updates the network state according to the action execution result, judges whether the simulation termination condition (such as the maximum simulation time length hitting) is reached, if not, returns to step 2) to continue the next period of data collection; if yes, releases resources and closes the artificial intelligence platform communication connection channel, and exports a final simulation report.

[0043] Step S120, configuring the power system power primary equipment intelligent operation and maintenance simulation project based on the topology structure of the production operation support system to be tested.

[0044] In some embodiments, the topology of the production operation support system at least includes wide-area integrated data network equipment, substation local area network equipment, production analysis processing host and its connection topology, the wide-area integrated data network equipment includes routers, communication links, the substation local area network equipment includes intelligent sensing terminals, production data unidirectional acquisition devices, intelligent gateways, switches and communication links. The configured wide-area integrated data network structure is as shown in Figure 5a The configured substation local area network structure is as shown in Figure 5b The configured structure of the entire production operation support system is as shown in Figure 5c

[0045] Please refer to Figure 5a In the simulation environment, the wide-area integrated data network is deployed with routers and switches to simulate the core layer, aggregation layer and access layer equipment of the wide-area integrated data network in the actual environment, the devices are connected directly through fiber links, and can also communicate with each other through wireless means, which is determined by the actual simulation project.

[0046] Please refer to Figure 5b Each substation is configured with at least two production data unidirectional acquisition device simulation models, one of which is connected to the measurement and control, protection, safety control and fault recording intelligent sensing terminal simulation model to collect the operation information of the power system in the form of IEC-104 protocol and DL / T 860 protocol; the other is connected to the power primary equipment online monitoring, power quality monitoring and digital meter intelligent sensing terminal simulation model to collect the state information of the power equipment in the form of DL / T 860 protocol, MODBUS protocol and DL / T 645 protocol; at least one intelligent gateway simulation model is configured to receive the operation information of the power system and the state information of the power equipment sent by the two production data unidirectional acquisition device simulation models, and simultaneously receive the operation information of the auxiliary equipment, and after information aggregation and protocol conversion, send to the production analysis processing host simulation model in the form of MQTT protocol, GB28181 and ONVIF protocol.

[0047] Please refer to Figure 5c The production analysis processing host is configured in the patrol and maintenance center to concentrate the operation information of the power system, the state information of the power equipment and the operation information of the auxiliary equipment sent by the intelligent gateway of multiple substations, and after information aggregation, send to the artificial intelligence platform through the artificial intelligence platform interaction module.

[0048] ​Step S130, execute the intelligent operation and maintenance simulation project, call the artificial intelligence platform state evaluation and fault diagnosis algorithm to determine whether the power primary equipment has hidden faults.

[0049] In some embodiments, each intelligent sensing terminal simulation model continuously uploads the corresponding power system data, and the production data unidirectional collection device simulation model uploads to the artificial intelligence platform through the artificial intelligence platform interaction module. The specific steps are as follows:

[0050] 1) The simulation model first calls the connection submodule to establish TCP communication with the artificial intelligence platform server.

[0051] 2) The request submodule constructs a request header containing necessary information, and the transmission submodule writes the power system operation information and equipment state data into the request body and sends them together, as shown in Figure 6a 、 Figure 6b

[0052] 3) The artificial intelligence platform server extracts data from the request body, calls the preset power primary equipment state evaluation and fault diagnosis algorithm from the algorithm warehouse, processes the data, and encapsulates the state evaluation result or fault hidden danger judgment result into the response body and returns it. The response submodule parses the data after receiving the response, and feeds back the result to the production analysis processing host simulation model. The algorithm warehouse is preloaded with multiple algorithms, including a transformer oil chromatographic analysis model based on deep learning, a circuit breaker mechanical state classification model driven by a support vector machine (SVM), etc. The algorithm is deployed in the form of containerized microservices, supporting dynamic expansion. Algorithm calling instance: when the intelligent gateway uploads abnormal transformer vibration data, the AI platform calls the vibration feature extraction algorithm (such as wavelet packet decomposition) and the fault diagnosis model, and returns the insulation aging probability value. If the probability exceeds the threshold (such as > 85%), a fault warning is triggered.

[0053] Step S140, if it is determined that the power primary equipment has hidden faults, a corresponding intelligent inspection scheme is matched according to the intelligent operation and maintenance plan library.

[0054] In some embodiments, after the production analysis processing host simulation model receives the equipment operation state machine fault hidden danger judgment result, the following operations are performed:

[0055] 1) The artificial intelligence platform returns the intelligent operation and maintenance plan library containing the equipment type, fault type, running state corresponding inspection scheme and matching conditions in the response body according to the received power primary equipment running state.

[0056] ​Exemplarily, the intelligent operation and maintenance plan library is stored in a relational database, and the key fields include: device type (such as transformer, GIS), fault type (such as partial discharge, mechanical jam), matching condition (such as temperature > 90℃ and load rate > 80%), and inspection scheme (such as unmanned aerial vehicle infrared scanning, robot partial discharge detection).

[0057] 2) The response submodule of the production analysis processing host simulation model obtains the operation and maintenance plan library, and matches the most suitable intelligent inspection scheme in the plan library in combination with the real-time operation data of the auxiliary equipment, and generates specific operation instructions.

[0058] Exemplarily, after the production analysis processing host simulation model receives the operation and maintenance plan library issued by the artificial intelligence platform, the state of the auxiliary equipment is queried under multiple conditions (such as camera availability, unmanned aerial vehicle power). Exemplarily, when the transformer oil temperature is too high and the camera simulation model is offline, the unmanned aerial vehicle simulation model is preferentially dispatched to perform a thermal imaging shooting task.

[0059] 3) Via the intelligent gateway, the production analysis processing host issues operation instructions to the auxiliary equipment. Finally, the auxiliary equipment implements the on-site inspection task according to the instructions.

[0060] It should also be understood that the term “and / or” used in the present application and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0061] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A power primary equipment intelligent operation and maintenance simulation test method based on a production operation support system, characterized in that, The application relates to a power system power primary equipment intelligent operation and maintenance simulation project based on a production operation support system, which comprises the following steps: based on a topology structure of a production operation support system to be tested and simulation models of physical equipment in the production operation support system, configuring the power system power primary equipment intelligent operation and maintenance simulation project based on the production operation support system, wherein an application layer of at least one simulation model of the physical equipment is configured with an artificial intelligence platform interaction module, the physical equipment at least comprises a production analysis processing host, an intelligent gateway, a production data one-way collection device and an intelligent sensing terminal, the intelligent sensing terminal comprises monitoring equipment and auxiliary equipment, the monitoring equipment comprises measurement and control, protection, safety control, fault recording, online monitoring, power quality monitoring and digitalized meters, and the auxiliary equipment comprises fire fighting, safety defense, dynamic environment system, camera, robot and unmanned aerial vehicle; the artificial intelligence platform interaction module comprises a connection submodule, a request submodule, a transmission submodule, a response submodule and a release submodule; the connection submodule establishes a connection channel with a service end of an artificial intelligence platform; the request submodule constructs a request header containing an address, a port, an AI model access path, authorization information, data type information and data length of the service end and initiates a connection request; the transmission submodule formats operation information of a power system corresponding to the production operation support system and state information of power equipment in the power system into a string to write into a request body and into an output stream; the response submodule obtains a response body returned by the artificial intelligence platform and analyzes response body data; and the release submodule releases buffer resource of a response after data transmission and algorithm calling are completed; the intelligent operation and maintenance simulation project is executed, and when the intelligent operation and maintenance simulation project is executed: based on the artificial intelligence platform interaction module, a state evaluation and fault diagnosis algorithm of the power primary equipment provided by the artificial intelligence platform is called, so that the running state of the power primary equipment is evaluated based on the state evaluation and fault diagnosis algorithm according to data obtained from a simulation model of the monitoring equipment and the operation information of the power system and the state information of the power equipment, and whether the power primary equipment has hidden faults is judged; and if it is judged that the power primary equipment has hidden faults, a corresponding intelligent inspection scheme is matched according to an intelligent operation and maintenance plan library, and the intelligent inspection scheme is output to a simulation model of the auxiliary equipment by a simulation model of the production analysis processing host through a simulation model of the intelligent gateway. The power system power primary equipment intelligent operation and maintenance simulation project based on the production operation support system comprises the following steps: acquiring a topology structure of a production operation support system to be tested, the topology structure of the production operation support system comprising wide-area comprehensive data network equipment, substation local area network equipment, a production analysis processing host and a connection topology thereof, the wide-area comprehensive data network equipment comprising routers and communication links, the substation local area network equipment comprising intelligent sensing terminals, production data one-way collection devices, intelligent gateways, switches and communication links; ​ ​ ​ ​ 2. The emulation test method of claim 1, wherein, ​ ​ An artificial intelligence platform is configured to interact with at least one of the simulation models of the wide-area integrated data network device, the substation local area network device, and the production analysis processing host. The simulation project of intelligent operation and maintenance of power system primary equipment based on the production operation support system is configured based on the topology of the production operation support system, and the simulation models of the wide-area integrated data network device, the substation local area network device, and the production analysis processing host.

3. The method of claim 1, wherein the method further comprises: The state evaluation and fault diagnosis algorithm of the power primary equipment provided by the artificial intelligence platform includes: The server of the artificial intelligence platform extracts the operation information of the power system and the state information of the power equipment in the request body output by each simulation model, calls the state evaluation and fault diagnosis algorithm of the power primary equipment from the algorithm warehouse built in the artificial intelligence platform, evaluates the operation state of the power primary equipment, and encapsulates the output result in the response body. The production analysis processing host simulation model receives and analyzes the response body returned by the artificial intelligence platform, and extracts the output result of the state evaluation and fault diagnosis algorithm of the power primary equipment.

4. The method of claim 1, wherein the method further comprises: The state evaluation and fault diagnosis algorithm of the power primary equipment provided by the artificial intelligence platform includes: The server of the artificial intelligence platform extracts the operation information of the power system and the state information of the power equipment in the request body output by each simulation model, calls the state evaluation and fault diagnosis algorithm of the power primary equipment from the algorithm warehouse built in the artificial intelligence platform, evaluates the operation state of the power primary equipment, and encapsulates the output result in the response body. The method for matching the corresponding intelligent inspection scheme in the intelligent operation and maintenance plan library includes:

5. The emulation test method according to claim 1 or 4, wherein The artificial intelligence platform matches the corresponding intelligent operation and maintenance plan library according to the operation state of the power primary equipment and encapsulates it in the response body. The intelligent operation and maintenance plan library includes the device type, fault type, operation state, inspection scheme, and matching condition. The production analysis processing host simulation model matches the intelligent inspection scheme that meets the device type, fault type, and operation state in the intelligent operation and maintenance plan library in combination with the operation information of the auxiliary equipment and generates an operation instruction. The operation instruction includes camera shooting, unmanned aerial vehicle inspection, and robot inspection. The topology of the production operation support system includes:

6. The emulation test method of claim 2, wherein, Each substation is configured with at least two one-way production data collection devices simulation models. One of them is connected to the intelligent sensing terminal simulation model of the measurement and control, protection, safety control, and fault recording to collect the operation information of the power system. The other one is connected to the online monitoring of the power primary equipment, power quality monitoring, and digital meter intelligent sensing terminal simulation model to collect the state information of the power equipment. ​ At least one of the intelligent gateway simulation model is configured, which accepts the operation information of the power system and the state information of the power equipment sent by two of the production data one-way collection device simulation model, and simultaneously accepts the operation information of the auxiliary equipment, and after information aggregation and protocol conversion, the production analysis processing host simulation model; The patrol and maintenance center is configured with one of the production analysis processing host, which collects the operation information of the power system, the state information of the power equipment and the operation information of the auxiliary equipment sent by the intelligent gateway of multiple substations, and sends the information to the wide-area comprehensive data network after information aggregation.

7. The emulation test method of claim 1, wherein, Further comprising: The operation information of the power system refers to the operation data and operation files of the power system obtained from the intelligent sensing terminal simulation model of the measurement and control, protection, safety control and fault recording in the substation local area network; The state information of the power equipment refers to the state data and state files of the power equipment obtained from the intelligent sensing terminal simulation model of the power primary equipment online monitoring, power quality monitoring and digital metering in the substation local area network; The operation information of the auxiliary equipment refers to the operation data and operation files of the auxiliary equipment obtained from the intelligent sensing terminal simulation model of the fire fighting, safety defense, dynamic system, camera, robot and unmanned aerial vehicle in the substation local area network.

Images