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

Abstract

The invention provides an electric power primary equipment intelligent operation and maintenance simulation test method based on a production operation support system, and the method comprises the steps: constructing a simulation model system of core equipment of the production operation support system, and embedding an artificial intelligence platform interaction module in a simulation model, so as to achieve the real-time data interaction between each simulation model and an artificial intelligence platform. The simulation model calls an electric power primary equipment state evaluation and fault diagnosis algorithm of the artificial intelligence platform in real time through the artificial intelligence platform interaction module, and carries out real-time simulation evaluation on the equipment operation condition and the potential fault hidden danger; and when a fault hidden danger is detected, automatically matching and issuing a corresponding intelligent inspection scheme according to a preset device type, a fault type and an operation state in an intelligent operation and maintenance plan library of the artificial intelligence platform. Through the process, full-process accurate testing and closed-loop verification are carried out on the intelligent operation and maintenance state of the primary equipment of the power system based on the production operation support system.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation and maintenance and simulation testing, and in particular to an intelligent operation and maintenance simulation testing method for primary power equipment based on a production operation support system. Background Art

[0002] The continuous expansion of power systems and the acceleration of digital transformation are driving higher demands on the operational status monitoring and intelligent maintenance of primary power equipment. Production and operation support systems, integrating IoT and artificial intelligence technologies, provide core support for intelligent operation and maintenance of primary power equipment. However, the simulation and testing solutions for these technologies have hindered the research, testing, and promotion of intelligent operation and maintenance technologies for primary power equipment. Summary of the Invention

[0003] To solve the above problems, the present invention discloses an intelligent operation and maintenance simulation test method for primary power equipment based on a production and operation support system, providing a simulation test method that can coordinate overall data collection, status assessment, fault diagnosis and intelligent operation and maintenance plan matching, and providing a comprehensive, realistic and efficient data and communication simulation platform for practical application.

[0004] To achieve the above objectives, the present invention adopts the following technical solutions: a simulation test method for intelligent operation and maintenance of primary power equipment based on a production and operation support system, comprising the following contents:

[0005] Based on the topological structure of the production and operation support system to be tested, and the simulation model of the physical equipment in the production and operation support system, an intelligent operation and maintenance simulation project of the power system primary equipment based on the production and operation support system is configured, wherein at least one simulation model of the physical equipment is configured with an artificial intelligence platform interaction module, and the physical equipment at least includes a production analysis and processing host, an intelligent gateway, a one-way production data acquisition device and an intelligent sensor terminal, and the intelligent sensor terminal includes monitoring equipment and auxiliary equipment, and the monitoring equipment includes measurement and control, protection, safety control, fault recording, online monitoring, power quality monitoring and digital meters, and the auxiliary equipment includes fire protection, security defense, dynamic environment system, camera, robot and drone; execute the intelligent Operation and maintenance simulation project, when the intelligent operation and maintenance simulation project is executed: based on the artificial intelligence platform interaction module, the status evaluation and fault diagnosis algorithm of the primary power equipment provided by the artificial intelligence platform is called, so as to evaluate the operating status of the primary power equipment based on the data obtained from the simulation model of the monitoring equipment and the operating information of the power system and the status information of the power equipment based on the status evaluation and fault diagnosis algorithm, so as to determine whether the primary power equipment has potential fault hazards; and if it is determined that the primary power equipment has potential fault hazards, the corresponding intelligent inspection plan is matched according to the intelligent operation and maintenance plan library, and the simulation model of the production analysis and processing host outputs the intelligent inspection plan to the simulation model of the auxiliary equipment through the simulation model of the intelligent gateway.

[0006] The present invention has the following advantages due to the adoption of the above technical solution:

[0007] 1. This invention covers multiple dimensions such as power system operation data, equipment status, and auxiliary equipment operation and maintenance information. It can realistically simulate the actual operation of the substation local area network and wide area integrated data network based on the production and operation support system in a simulation environment, providing a more realistic data basis for subsequent fault diagnosis and intelligent operation and maintenance.

[0008] 2. The present invention realizes efficient connection between the simulation model and the artificial intelligence platform by setting up an artificial intelligence interaction module as a data interaction interface, so that the simulation system can call the built-in power primary equipment status evaluation and fault diagnosis algorithm in real time and match the corresponding inspection plan, realizing the seamless integration of power secondary system simulation and artificial intelligence technology.

[0009] 3. The present invention provides a simulation testing method that can coordinate overall data collection, status assessment, fault diagnosis and intelligent operation and maintenance plan matching, providing a comprehensive, realistic and efficient data and communication simulation platform for practical application. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0011] Figure 1 Flowchart of an implementation method of intelligent operation and maintenance simulation test method for power primary equipment based on a production and operation support system of the present invention;

[0012] Figure 2 A schematic diagram of a framework of a production operation support system in one embodiment of the present invention;

[0013] Figure 3 A simulation structure diagram of a simulation model of a physical device in a production operation support system in one embodiment of the present invention;

[0014] Figure 4 A schematic diagram of a standardized character string format after encapsulation by a transmission submodule in one embodiment of the present invention;

[0015] Figure 5a A typical wide area integrated data network structure in one embodiment of the present invention;

[0016] Figure 5b A typical substation local area network structure in one embodiment of the present invention;

[0017] Figure 5c The structure of the entire production operation support system in one embodiment of the present invention;

[0018] Figure 6a In one embodiment of the present invention, a request body includes power system operation information;

[0019] Figure 6b In one embodiment of the present invention, a request body includes power device status data. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those readily understood by those skilled in the art to which this application pertains. Terms used herein in the specification of this application are for the purpose of describing specific embodiments only and are not intended to limit this application.

[0023] See also Figure 1 , Figure 1 This is a flow chart of a simulation test method for intelligent operation and maintenance of primary power equipment based on a production and operation support system provided in an embodiment of the present application. The simulation test method can be applied in computer equipment for simulation testing of intelligent operation and maintenance of primary power equipment based on a production and operation support system in professional fields, such as for primary equipment such as main transformers, circuit breakers, and disconnectors in smart grids / smart substations. It conducts simulation testing of the entire operation process of equipment health status monitoring, fault prediction and diagnosis, intelligent maintenance strategy drills, and emergency plan verification under the framework of the production and operation support system. By way of example, through tests under multiple working conditions and multiple scenarios, the reliability, real-time nature, and effectiveness of the intelligent operation and maintenance system architecture based on the production and operation support system and the primary power equipment status evaluation and fault diagnosis algorithms are verified.

[0024] In some embodiments, the simulation environment can use OPNET simulation software as a platform, but is not limited to 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 present application; the artificial intelligence platform can use the Python artificial intelligence platform, but is not limited to the Python artificial intelligence platform. Other artificial intelligence platforms, such as PyTorch, Stable Baselines3, etc., can also be used to build the artificial intelligence platform for implementing the embodiments of the present application.

[0025] See Figure 1 The intelligent operation and maintenance simulation test method of primary power equipment based on the production and operation support system in an embodiment of the present application includes steps S110 to S140.

[0026] Step S110: Acquire a simulation model of the production operation support system, including all equipment for realizing the simulation of intelligent operation and maintenance application of primary power equipment.

[0027] In some embodiments, the simulation model of the physical equipment in the production operation support system includes a simulation model of a production analysis and processing host, a simulation model of an intelligent gateway, a simulation model of a one-way production data acquisition device, and a simulation model of an intelligent sensor terminal. Figure 2 As shown, it is a seven-layer protocol model architecture based on OSI, including application layer, presentation layer, session layer, transport layer, network layer, data link layer and several physical layers, and each of the physical layers includes at least one pair of send / receive ports, which can realize the intelligent operation and maintenance equipment model architecture based on TCP / IP. Each simulation model supports multiple communication protocols (such as IEC-104, DL / T 860, MODBUS, etc.), simulating the data generated by each physical device in the production operation support system to monitor the data and its transmission.

[0028] In order to realize the calling of the artificial intelligence platform's power primary equipment status evaluation and fault diagnosis algorithm in the simulation environment and the issuance of the intelligent operation and maintenance plan operation and maintenance library, an artificial intelligence platform interaction module is embedded in the simulation model application layer. The core of the artificial intelligence platform interaction module lies in the connection submodule, request submodule, transmission submodule, response submodule and release submodule.

[0029] For example, see 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 the "connection establishment" state, the request submodule corresponds to the "initiate request" state, the transmission submodule corresponds to the "initiate transmission" state, the response submodule corresponds to the "receive response" state, and the release submodule corresponds to the "resource release" state. The execution logic of the state jump includes:

[0030] 1) "Initialization" state: When an OPNET simulation begins, a simulation start interrupt is triggered to enter the "Initialization" state. This initializes the interactive attribute storage unit, interactive data storage unit, and simulation model attribute setting parameters. The interactive 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 interactive data storage unit stores the power system operation information and power equipment status information in the request body. The simulation model attribute setting parameters set the interaction method between the simulation model and the artificial intelligence platform. This can be directly calling the status assessment and fault diagnosis algorithm for primary power equipment, or calling the diagnosis results of the status assessment and fault diagnosis algorithm for primary power equipment. The status assessment and fault diagnosis algorithm for primary power equipment includes intelligent analysis algorithms for transformers, voltage transformers, current transformers, lightning arresters, and system harmonics. After the "Initialization" state is completed, the "Idle / Wait" state is unconditionally entered.

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

[0032] 3) "Connection Establishment" state: when and only when the artificial intelligence platform server responds to the interrupt trigger, it jumps to the "Connection Establishment" state. In the "Connection Establishment" state, the simulation model and the artificial intelligence platform server try to establish a communication connection channel. If the channel establishment function returns "false", it remains in the "Connection Establishment" state. When the communication establishment function returns "true", the communication connection is established 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, it jumps to the "Idle / Wait" state.

[0034] 5) "Initiate transmission" state: when and only when the simulation model receives the operating information of the power system and the operating information of the power equipment and the connection channel has been established, it jumps to the "Initiate transmission" state. In the "Initiate transmission" state, the simulation model formats the operating information of the power system and the status information of the power equipment into a string, writes it into the request body and writes it into the output stream. In this state, if the platform receiving function has not "true" the Boolean parameter, it remains in the "Initiate transmission" state. When the platform receiving function returns "true", it jumps to the "Idle / Waiting" state.

[0035] 6) "Receive Response" state: if and only if the artificial intelligence platform triggers the response body sending interrupt, it jumps to the "Receive Response" state. In the "Receive Response" state, the simulation model uses the response body parsing function to parse the response body returned by the artificial intelligence platform and the response body data. When the response body parsing function returns "false", it remains in the "Receive Response" state. When the response body parsing function returns "true", it jumps to the "Idle / Waiting" state.

[0036] 7) "Resource Release" state: when and only when the simulation model data transmission and algorithm call 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 / Wait" state and wait for the next interrupt to trigger the state jump.

[0037] For example, the physical equipment based on the production operation support system is modeled and simulated in OPNET software, and the artificial intelligence platform uses Python platform, see Figure 3 , the overall architecture of the interaction between the two. Taking this embodiment as a reference, the interaction between the simulation model and the artificial intelligence platform described in the present invention includes the following steps:

[0038] 1) After the OPNET simulation terminal is started, the custom C language code is embedded in the simulation process model, the artificial intelligence platform interaction module is activated, and the artificial intelligence platform server port is bound to the local port of the simulation environment (such as TCP 65432) and enters the listening state, that is, Figure 2 The "Idle / Wait" state in .

[0039] 2) The OPNET simulation engine triggers the data collection of power system power information, power equipment status information and auxiliary equipment operation information at preset time intervals (e.g., every 0.1 second simulation time). The collected multi-dimensional status data is converted into a standardized string format, such as Figure 4 As shown in the figure, "Substation 1 outgoing line: voltage 220kV, frequency 50Hz, active power 10.5MW·Transformer T1: operating status "normal", temperature 85℃·Cooling pump: running, fan speed 1200rpm" is converted into a standard string format, and the encapsulated status data is sent to the Python server through the transmission submodule in the artificial intelligence interaction module.

[0040] 3) The Python server receives the original state string through the server 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 decision actions (such as issuing equipment fault diagnosis algorithms and intelligent operation and maintenance plan libraries), verifies the validity of decision actions, and filters out illegal values ​​(such as discrete actions that exceed the preset range).

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

[0042] 5) The OPNET simulation engine updates the network status based on the action execution results and determines whether the simulation termination conditions (such as the maximum simulation duration) have been met. If not, it returns to step 2) to continue data collection for the next cycle. If terminated, it releases resources, closes the AI ​​platform communication connection channel, and exports the final simulation report.

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

[0044] In some embodiments, the topology of the production and operation support system includes at least wide-area integrated data network equipment, substation local area network equipment, production analysis and processing host and their connection topology, wherein the wide-area integrated data network equipment includes routers and communication links, and the substation local area network equipment includes intelligent sensor terminals, one-way production data collection devices, intelligent gateways, switches and communication links. The configured wide-area integrated data network structure is as follows: Figure 5a As shown, the configured substation LAN structure is as follows Figure 5b As shown, the structure of the entire production operation support system is as follows Figure 5c shown.

[0045] See also Figure 5a In the simulation environment, routers and switches are deployed in the wide area integrated data network 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 directly connected through optical fiber links, and can also communicate with each other wirelessly, depending on the actual simulation project.

[0046] See also Figure 5b Each substation is equipped with at least two simulation models of the one-way production data acquisition device, one of which is connected to the measurement and control, protection, safety control, and fault recording intelligent sensor terminal simulation model to collect the operation information of the power system in the IEC-104 protocol and DL / T 860 protocol; the other is connected to the online monitoring of the primary power equipment, power quality monitoring, and digital meter intelligent sensor terminal simulation model to collect the status information of the power equipment in the 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 status information of the power equipment sent by the two simulation models of the one-way production data acquisition device, and the operation information of the auxiliary equipment at the same time, and after information aggregation and protocol conversion, send it to the production analysis and processing host simulation model in the MQTT protocol, GB28181, and ONVIF protocol.

[0047] See also Figure 5c The patrol and maintenance center is equipped with one of the above-mentioned production analysis and processing hosts, which collects the operation information of the power system, the status information of the power equipment, and the operation information of the auxiliary equipment sent by the above-mentioned intelligent gateways of multiple substations, aggregates the information, and sends it to the artificial intelligence platform through the artificial intelligence platform interaction module.

[0048] Step S130: Execute the intelligent operation and maintenance simulation project, and call the artificial intelligence platform status evaluation and fault diagnosis algorithm to determine whether there are any potential fault hazards in the primary power equipment.

[0049] In some implementations, each intelligent sensor terminal simulation model continuously uploads the corresponding power system data, which is then uploaded to the artificial intelligence platform via the artificial intelligence platform interaction module by the production data one-way acquisition device simulation model. 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 status data into the request body and sends it together, such as Figure 6a 、 Figure 6b shown.

[0052] 3) After the artificial intelligence platform server extracts the data from the request body, it calls the preset power primary equipment status evaluation and fault diagnosis algorithm from the algorithm warehouse, processes the data, and encapsulates the status evaluation results or fault hidden danger determination results into the response body and returns it. After receiving the response, the response submodule parses the data and feeds the results back to the production analysis and processing host simulation model. The algorithm warehouse presets a variety of algorithms, including a transformer oil chromatography 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 and supports dynamic expansion. Algorithm call example: When the intelligent gateway uploads abnormal vibration data of a transformer, the AI ​​platform calls the vibration feature extraction algorithm (such as wavelet packet decomposition) and the fault diagnosis model to return 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 primary power equipment has a potential fault, a corresponding intelligent inspection plan is matched according to the intelligent operation and maintenance plan library.

[0054] In some embodiments, after receiving the equipment operation state machine fault hidden danger determination result, the production analysis and processing host simulation model performs the following operations:

[0055] 1) Based on the received operating status of the primary power equipment, the artificial intelligence platform encapsulates the intelligent operation and maintenance plan library containing the equipment type, fault type, inspection plan corresponding to the operating status and matching conditions in the response body and returns it.

[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 plan (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 plan 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 method for intelligent operation and maintenance simulation test of power primary equipment based on a production and operation support system, characterized in that: include: Based on the topological structure of the production and operation support system to be tested and the simulation model of the physical equipment in the production and operation support system, a power system primary equipment intelligent operation and maintenance simulation project based on the production and operation support system is configured, wherein the simulation model of at least one of the physical equipment is configured with an artificial intelligence platform interaction module, and the physical equipment at least includes a production analysis and processing host, an intelligent gateway, a one-way production data acquisition device and an intelligent sensor terminal, and the intelligent sensor terminal includes monitoring equipment and auxiliary equipment. The monitoring equipment includes measurement and control, protection, safety control, fault recording, online monitoring, power quality monitoring and digital meters, and the auxiliary equipment includes fire protection, security defense, dynamic environment system, camera, robot and drone; Executing the intelligent operation and maintenance simulation project, when the intelligent operation and maintenance simulation project is executed: calling the state evaluation and fault diagnosis algorithm of the primary power equipment provided by the artificial intelligence platform based on the artificial intelligence platform interaction module, so as to evaluate the operating state of the primary power equipment based on the data obtained from the simulation model of the monitoring device, the operating information of the power system, and the state information of the power equipment, so as to determine whether the primary power equipment has potential fault hazards; as well as If it is determined that the primary power equipment has a potential fault, a corresponding intelligent inspection plan is matched according to the intelligent operation and maintenance plan library, and the simulation model of the production analysis and processing host outputs the intelligent inspection plan to the simulation model of the auxiliary equipment through the simulation model of the intelligent gateway.

2. The simulation test method according to claim 1, wherein: The topology structure of the production and operation support system to be tested and the simulation model of the physical equipment in the production and operation support system are used to configure the power system primary equipment intelligent operation and maintenance simulation project based on the production and operation support system, including: Obtaining a topological structure of a production and operation support system to be tested, wherein the topological structure of the production and operation support system includes wide-area integrated data network equipment, substation local area network equipment, a production analysis and processing host and their connection topology, wherein the wide-area integrated data network equipment includes a router and a communication link, and the substation local area network equipment includes an intelligent sensor terminal, a one-way production data collection device, an intelligent gateway, a switch, and a communication link; Acquire a simulation model of the wide area integrated data network equipment, a simulation model of the substation local area network equipment, and a simulation model of the production analysis and processing host, wherein at least one of the simulation models is configured with an artificial intelligence platform interaction module; Based on the topological structure of the production and operation support system, as well as the simulation model of the wide area integrated data network equipment, the simulation model of the substation local area network equipment, and the simulation model of the production analysis and processing host, an intelligent operation and maintenance simulation project of the power system primary equipment based on the production and operation support system is configured.

3. The simulation test method according to claim 1, wherein: include: The artificial intelligence platform interaction module includes a connection submodule, a request submodule, a transmission submodule, a response submodule and a release submodule; When the intelligent operation and maintenance simulation project is executed: the connection submodule establishes a connection channel with the artificial intelligence platform server, the request submodule 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, the transmission submodule formats the operation information of the power system and the status information of the power equipment into a string, writes it into the request body and writes it into the output stream, the response submodule obtains the response body returned by the artificial intelligence platform and parses the response body data, and the release submodule releases the response buffer resources after the data transmission and algorithm call are completed.

4. The simulation test method according to any one of claims 1 and 3, characterized in that: The calling of the state evaluation and fault diagnosis algorithm of the primary power equipment provided by the artificial intelligence platform includes: The artificial intelligence platform server extracts the power system operation information and power equipment status information from the request body output by each simulation model, and uses the algorithm warehouse built into the artificial intelligence platform to call the status evaluation and fault diagnosis algorithm of the primary power equipment to evaluate the operating status of the primary power equipment and then encapsulate the output result in the response body; The production analysis and processing host simulation model receives and parses the response body returned by the artificial intelligence platform, and extracts the output results of the primary power equipment status evaluation and fault diagnosis algorithm therefrom.

5. The simulation test method according to any one of claims 1 and 3, characterized in that: The calling of the state evaluation and fault diagnosis algorithm of the primary power equipment provided by the artificial intelligence platform also includes: The artificial intelligence platform server extracts the power system operation information and power equipment status information in the request body output by each simulation model, and the algorithm warehouse built into the artificial intelligence platform calls the adapted power primary equipment status evaluation and fault diagnosis algorithm based on the power system operation information and power equipment status information in the request body and encapsulates the algorithm in the response body; The production analysis and processing host simulation model receives and parses the response body returned by the artificial intelligence platform, and extracts the primary power equipment status evaluation and fault diagnosis algorithm to evaluate the operating status of the primary power equipment.

6. The simulation test method according to claim 1, 3-5, characterized in that: The method for matching the intelligent operation and maintenance plan library with the corresponding intelligent inspection plan includes: The artificial intelligence platform matches the corresponding intelligent operation and maintenance plan library according to the operating status of the primary power equipment and encapsulates it in the response body. The intelligent operation and maintenance plan library includes the equipment type, fault type, inspection plan and matching conditions of the operating status; The production analysis and processing host simulation model combines the operating information of the auxiliary equipment to match the intelligent inspection plan that meets the equipment type, the fault type, and the operating status in the intelligent operation and maintenance plan library and generates operation instructions, which include video shooting, drone inspection, and robot inspection.

7. The simulation test method according to claim 2, wherein: The topology of the production operation support system includes: Each substation is equipped with at least two simulation models of the one-way production data acquisition device, one of which is connected to the measurement and control, protection, safety control, and fault recording intelligent sensor terminal simulation model to collect the operation information of the power system; the other is connected to the online monitoring, power quality monitoring, and digital meter intelligent sensor terminal simulation model of the power primary equipment to collect the status information of the power equipment; At least one intelligent gateway simulation model is configured to receive the operation information of the power system and the status information of the power equipment sent by the two production data one-way acquisition device simulation models, and the operation information of the auxiliary equipment, and the production analysis and processing host simulation model after information aggregation and protocol conversion; The patrol and maintenance center is equipped with one of the production analysis and processing hosts, which collects the operation information of the power system, the status information of the power equipment, and the operation information of the auxiliary equipment sent by the intelligent gateways of multiple substations, aggregates the information, and sends it to the wide area integrated data network.

8. The simulation test method according to claim 1, wherein: Also includes: The power system operation information refers to the power system operation data and operation files obtained from the intelligent sensor terminal simulation model for measurement and control, protection, safety control, and fault recording accessed from 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 online monitoring of the power primary equipment, the power quality monitoring, and the digital meter intelligent sensor terminal simulation model accessed from 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 fire protection, security defense, dynamic environment system, camera, robot, and drone intelligent sensor terminal simulation model accessed from the substation local area network.

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