A substation equipment operation state analysis method and device based on a rule model

Abstract

The application discloses a substation equipment operation state analysis method and device based on a rule model, and the method comprises the following steps: collecting key operation characteristic information of substation equipment; establishing an equipment finite state set according to the key operation characteristic information; establishing a rule model according to the key operation characteristic information and the equipment finite state set; and predicting the operation state of the substation equipment according to the rule model. The method can predict the operation state of the substation equipment by establishing the rule model, can strengthen real-time sensing of the state, and can achieve better and more comprehensive state analysis effects.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of substation management, and particularly relates to a substation equipment operation state analysis method and device based on a rule model. BACKGROUND

[0002] Comprehensive perception of the state of power equipment is a prerequisite for ensuring safe operation and realizing intelligent inspection. State perception includes two meanings: one is to perceive the current state of the equipment in a timely and accurate manner, and the other is to analyze the subsequent development trend of the equipment state. Power equipment has the characteristics of complex structure, long operation cycle and harsh working environment, and long-term research and practice have been carried out on online state monitoring and Internet of Things of power equipment in the prior art. Specifically, a variety of sensors and data processing methods are applied to comprehensively consider and integrate equipment state monitoring, fault prediction and maintenance decision-making, thereby improving equipment reliability. For large and complex equipment such as transformers, converter valves and GIS, the connection relationship between devices and components in the equipment is complex, and various factors are strongly intertwined, which makes it difficult to establish a physical model that meets the speed and accuracy constraints. At the same time, the error propagation mechanism and error accumulation effect between the physical models of various devices and components are difficult to describe and evaluate. At present, three-dimensional modeling and laser point cloud technology have been widely used in substations to establish three-dimensional models of substation equipment. For example, patent document CN110610542A discloses a substation equipment state monitoring panoramic analysis system, which establishes a big data analysis platform based on fused control data, measurement data and equipment management data, establishes a three-dimensional panoramic model based on three-dimensional data and GIS geographic data, and displays the real-time state information of the substation of the big data analysis platform and the three-dimensional panoramic model through a three-dimensional panoramic display platform.

[0003] However, the above scheme can only display the current state of the substation through the three-dimensional model, and cannot predict the equipment operation state at the next moment according to the initial state, so the accuracy of state perception is insufficient. SUMMARY

[0004] The present application provides a substation equipment operation state analysis method and device based on a rule model, which predicts the operation state of substation equipment by establishing a rule model, enhances real-time perception of the state, and has better and more comprehensive state analysis effect.

[0005] A substation equipment operation state analysis method based on a rule model, comprising:

[0006] Collecting key operation characteristic information of substation equipment;

[0007] Establishing a device finite state set according to the key operation characteristic information;

[0008] Based on the key operational characteristic information and the finite set of equipment states, a rule model is established;

[0009] Based on the rule model, the operating status of substation equipment is predicted.

[0010] Furthermore, the key operating characteristic information includes the control signals of the substation equipment and the sensor data of the substation equipment in various states.

[0011] Furthermore, the finite set of equipment states includes the set of normal operation state, stop operation state, fault state, hot standby state and cold standby state of substation equipment.

[0012] Furthermore, based on the aforementioned key operational characteristic information and the finite set of equipment states, a rule model is established, including:

[0013] Define the factors that affect the status of substation equipment;

[0014] Based on the finite state set of the equipment and the factors affecting the state of the substation equipment, a real-time state function for the substation equipment is established.

[0015] Based on the real-time state function, the control signals of the substation equipment, and the sensor data of the substation equipment in various states, a rule function for the operation state transition is established to obtain a rule model.

[0016] Furthermore, the real-time state function is expressed by the following formula:

[0017]

[0018] Among them, S t Let X(t) represent the state of the substation equipment at time t, X(t) be the control action of the current control signal on the substation equipment, flow(U,I,P,Q,f,……) be the factors affecting the state of the substation equipment, and S be the control action of the current control signal on the substation equipment. t-1 To represent the state of the substation equipment at time t-1, C(t) represents the flag bit of the equipment maintenance command.

[0019] Furthermore, the rule model is represented by the following formula:

[0020]

[0021] f(t) = λ1·f1(X) t )+λ2·f2(Y t );

[0022] Among them, S t+1 S represents the state of the substation equipment at time t+1. tLet f(t) be the state of the substation equipment at time t, f1(X) be the rule function for the transition of the operating state, and f1(X) be the state of the substation equipment at time t. t f2(Y) is the influence function of the control signal. t Y is the influence function of the sensor data, λ1 and λ2 are the coefficients of the influence function of the control signal and the influence function of the sensor data, respectively. t Let t be the sensor data at time t.

[0023] Furthermore, based on the aforementioned rule model, the operating status of substation equipment is predicted, including:

[0024] Calculate the current status of substation equipment;

[0025] Set a threshold value that allows control signals to cause state transitions in substation equipment;

[0026] Based on the influence function of the control signal, a threshold judgment is made. When the influence function value of the control signal does not reach the threshold, the state of the substation equipment at the next moment is predicted according to the operating state transition rule function.

[0027] Furthermore, when the influence function value of the control signal reaches the threshold, the state of the substation equipment at the next moment is determined based on the influence function of the control signal.

[0028] A rule-based model-based substation equipment operation status analysis device, comprising:

[0029] The data acquisition module is used to collect key operating characteristic information of substation equipment;

[0030] The set establishment module is used to establish a set of finite states of the device based on the key operating characteristic information;

[0031] The model building module is used to build a rule model based on the key operational characteristic information and the finite set of equipment states;

[0032] The prediction module is used to predict the operating status of substation equipment based on the rule model.

[0033] Furthermore, in the acquisition module, the key operating characteristic information includes the control signals of the substation equipment and the sensor data of the substation equipment in various states.

[0034] Furthermore, in the set establishment module, the finite state set of equipment includes a set of normal operation state, stop operation state, fault state, hot standby state and cold standby state of substation equipment.

[0035] Furthermore, the model building module is also used for:

[0036] Define the factors that affect the status of substation equipment;

[0037] Based on the finite state set of the equipment and the factors affecting the state of the substation equipment, a real-time state function for the substation equipment is established.

[0038] Based on the real-time state function, the factors affecting the state of substation equipment, the control signals of substation equipment, and the sensor data of substation equipment in various states, an operating state transition rule function is established to obtain a rule model.

[0039] Furthermore, the real-time state function is expressed by the following formula:

[0040]

[0041] Among them, S t Let X(t) represent the state of the substation equipment at time t, X(t) be the control action of the current control signal on the substation equipment, flow(U,I,P,Q,f,……) be the factors affecting the state of the substation equipment, and S be the control action of the current control signal on the substation equipment. t-1 To represent the state of the substation equipment at time t-1, C(t) represents the flag bit of the equipment maintenance command.

[0042] Furthermore, the rule model is represented by the following formula:

[0043]

[0044] f(t) = λ1·f1(X) t )+λ2·f2(Y t );

[0045] Among them, S t+1 S represents the state of the substation equipment at time t+1. t Let f(t) be the state of the substation equipment at time t, f1(X) be the operating state transition rule function, and f1(X) be the state of the substation equipment at time t. t f2(Y) is the influence function of the control signal. t Y is the influence function of the sensor data, λ1 and λ2 are the coefficients of the influence function of the control signal and the influence function of the sensor data, respectively. t Let t be the sensor data at time t.

[0046] Furthermore, the prediction module is also used for:

[0047] Calculate the current status of substation equipment;

[0048] Set a threshold value that allows control signals to cause state transitions in substation equipment;

[0049] Based on the influence function of the control signal, a threshold judgment is made. When the influence function value of the control signal does not reach the threshold, the state of the substation equipment at the next moment is predicted according to the operating state transition rule function.

[0050] Furthermore, when the influence function value of the control signal reaches the threshold, the state of the substation equipment at the next moment is determined based on the influence function of the control signal.

[0051] An electronic device includes a processor and a storage device, the storage device storing a plurality of instructions, the processor being configured to read the plurality of instructions and execute the method described above.

[0052] The method and apparatus for analyzing the operating status of substation equipment based on a rule model provided by this invention have at least the following beneficial effects:

[0053] (1) Based on the finite state machine theory, the various operating states of substation equipment are abstracted into a set of finite state machine states and state connection relationships are established, so as to more intuitively represent the different states of substation equipment.

[0054] (2) Based on the control signals of the station control equipment, the key operating characteristic information of the substation equipment, and the internal laws of state transition, establish the rules of state transition, so as to predict the state of the substation equipment before obtaining the operating characteristic information of the next moment.

[0055] (3) The coefficients and judgment thresholds of the operation state transition rule function are updated based on historical data to realize the continuous self-learning optimization of the rule function, so that the adaptability of the rule model to the substation equipment is gradually improved, and the accuracy of state prediction through this model is high. Attached Figure Description

[0056] Figure 1 This is a flowchart of one embodiment of the rule-based model-based substation equipment operation status analysis method provided by the present invention.

[0057] Figure 2 This is a flowchart of one embodiment of the rule model-based substation equipment operation status analysis method provided by the present invention.

[0058] Figure 3 This is a flowchart of an embodiment of the state prediction method in the rule-based model-based substation equipment operation state analysis method provided by the present invention.

[0059] Figure 4 This is a schematic diagram of one embodiment of the rule-based model-based substation equipment operation status analysis device provided by the present invention.

[0060] Figure 5This is a schematic diagram of the structure of an embodiment of the electronic device provided by the present invention.

[0061] Reference numerals: 1-processor, 2-storage device, 101-acquisition module, 102-set building module, 103-model building module, 104-prediction module. Detailed Implementation

[0062] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0063] refer to Figure 1 In some embodiments, a method for analyzing the operating status of substation equipment based on a rule model is provided, including:

[0064] S1. Collect key operating characteristic information of substation equipment;

[0065] S2. Based on the key operational characteristic information, establish a finite set of device states;

[0066] S3. Establish a rule model based on the key operational characteristic information and the finite set of equipment states;

[0067] S4. Based on the rule model, predict the operating status of substation equipment.

[0068] Specifically, the key operational characteristic information includes control signals of the substation equipment and sensor data of the substation equipment in various states. The control signals are used to control the switching logic between various operating states; the sensors include sound sensors, photoelectric sensors, and electrical sensors, used to collect sound, light, and electrical characteristic information, and the sensor data differs under different operating conditions or in different operating states. The finite set of equipment states includes, but is not limited to, the set of normal operation states, stopped operation states, fault states, hot standby states, and cold standby states of the substation equipment.

[0069] In some embodiments, to address the issues of physical model accuracy constraints and error accumulation, a digital twin physical model corresponding to the three-dimensional digital model is established by combining key operational characteristic information obtained from the primary equipment of the substation and its sensors. In the digital twin physical model, based on finite state machine theory, the various operating states of the primary equipment of the substation are abstracted into a set of states of a finite state machine, and the switching logic between the various operating states of the primary equipment is used as the connection relationship between the states of the finite state machine. Specifically, the current operating state of the substation equipment is comprehensively determined by combining key operational characteristic information obtained from data from sensors such as sound, light, electricity, and heat, as well as control signal commands from the station control equipment.

[0070] refer toFigure 2 In step S3, a rule model is established based on the key operational characteristic information and the finite set of device states, including:

[0071] S31. Define the factors that affect the status of substation equipment;

[0072] S32. Based on the finite state set of the equipment and the factors affecting the state of the substation equipment, establish the real-time state function of the substation equipment;

[0073] S33. Based on the real-time state function, the factors affecting the state of the substation equipment, the control signals of the substation equipment, and the sensor data of the substation equipment in each state, establish the operating state transition rule function to obtain the rule model.

[0074] In step S32, the real-time state function is expressed by the following formula:

[0075]

[0076] Among them, S t Let X(t) represent the state of the substation equipment at time t, X(t) be the control action of the current control signal on the substation equipment, flow(U,I,P,Q,f,……) be the factors affecting the state of the substation equipment, and S be the control action of the current control signal on the substation equipment. t-1 To represent the state of the substation equipment at time t-1, C(t) represents the flag bit of the equipment maintenance command. The flag bit is used to reflect the state. For example, if the flag bit is 1, it means that the equipment is under maintenance, and if the flag bit is 0, it means that the equipment is not under maintenance.

[0077] The symbol “∧” represents the “AND” operation in logical operations.

[0078] As a preferred implementation method, the establishment of the finite state set and rule model are both carried out within the digital twin. Specifically, a digital twin physical model corresponding to the three-dimensional model of the substation equipment is established by combining the acquired primary equipment and its sensor data. Attributes such as the finite state set of the primary equipment, the set of working functions for each state, the set of initial states, clock synchronization tags, and the set of interaction ports are defined to construct a finite state machine model applicable to various types of substation primary equipment. Furthermore, based on finite state machine theory, the primary equipment and its key operating characteristics are abstracted into the states of the finite state machine, and the switching logic between the primary equipment and various operating states is used as the connection relationship between the states of the finite state machine to establish behavioral and rule models.

[0079] In a specific application scenario, a real-time state function is established for a circuit breaker in a substation. The key operating characteristics of the circuit breaker include its on / off state, where 0 indicates the circuit breaker is open and 1 indicates it is closed. The finite set of circuit breaker states includes normal operating state (Srun), fault state (Sstp), and maintenance state (Srst). Referring to Table 1, the state transition rules of the circuit breaker satisfy a Markov process with probability p. During the circuit breaker's operating state transition process, the current operating state of the circuit breaker can be determined through sensing measurements of acoustic, optical, and electrical sensor data, control commands from substation control equipment, and subsequent state transitions can be predicted using an event-driven approach.

[0080] Table 1

[0081]

[0082] In the real-time on / off state function of the circuit breaker, the circuit breaker receives control signals (C(t), X(t)), where C(t) = {0, 1} represents the flag bit for the circuit breaker equipment maintenance command, 0 indicates no maintenance, and 1 indicates immediate maintenance; X(t) = {0, 1} represents the control action of the control signal on the circuit breaker equipment, 0 indicates the circuit breaker is opened, and 1 indicates the circuit breaker is closed. The current equipment status information in the circuit breaker is S(t) = {0, 1}, where 0 indicates the circuit breaker is open and 1 indicates the circuit breaker is closed. An embodiment of the operation process of the real-time on / off state function of the circuit breaker is shown in Table 2.

[0083] Table 2

[0084]

[0085] In step S33, the operating state transition rule is determined by multiple factors, including equipment-side factors and substation-side factors. Equipment-side factors include various factors such as reliability and unexpected events, including normal factors like equipment voltage, current, active power, reactive power, and frequency, as well as factors like equipment overheating, aging, water accumulation, ice accumulation, and fire. Substation-side factors include various factors that change the substation's operating mode, such as substation dispatching and changes in the operating state of adjacent equipment. The operating state transition rule is used to formulate the transition rules for the operating state of primary equipment under different external stimuli. This rule can comprehensively consider multiple factors, such as equipment status inspection results represented by sound, light, and electrical sensor data, and substation operating conditions.

[0086] The rule model is represented by the following formula:

[0087]

[0088] f(t) = λ1·f1(X)t )+λ2·f2(Y t );

[0089] Among them, S t+1 S represents the state of the substation equipment at time t+1. t Let f(t) be the state of the substation equipment at time t, f1(X) be the operating state transition rule function, and f1(X) be the state of the substation equipment at time t. t f2(Y) is the influence function of the control signal. t Y is the influence function of the sensor data, where λ1 and λ2 are the coefficients of the influence function of the control signal and the influence function of the sensor data, respectively. t Let be the sensor data at time t. This is data from a specific sensor.

[0090] function f1(X) t f2(Y) t ) is used as the offset for the state transition of substation equipment, where f1(X) t f2(Y) is the control signal influence function, constructed based on the control signal received by the device. The coefficient λ1 is 0 or 1. When λ1 is 0, it indicates that the device is no longer under the control of the control signal command. t The influence function of other external factors is constructed based on sensor signals monitoring the equipment status. It is obtained through machine learning or solving a system of high-order differential equations using a large amount of historical data of the equipment. The historical data consists of several pairs of historical data samples composed of a sensing data map and the equipment status. The coefficient λ2 can be initialized to 0, that is, it is assumed that the initial operating state of the equipment is only affected by the control signal, and the influence of other external factors is not considered. As the historical data of the inspection is accumulated, it is gradually updated, thereby realizing the self-learning of the operating state transition rule function. After improving the accuracy of state prediction through this rule model, the coefficient λ2 can approach 1.

[0091] refer to Figure 3 In step S4, the operating status of substation equipment is predicted based on the rule model, including:

[0092] S41. Calculate the current status of the substation equipment;

[0093] S42. Set a threshold value that allows control signals to cause state transitions in substation equipment;

[0094] S43. Based on the influence function of the control signal, a threshold judgment is made. When the influence function value of the control signal does not reach the threshold, the state of the substation equipment at the next moment is predicted based on the operating state transition rule function.

[0095] S44. When the influence function value of the control signal reaches the threshold, determine the state of the substation equipment at the next moment based on the influence function of the control signal.

[0096] Specifically, in step S43, if the influence function of the control signal does not reach the threshold, it means that the state transition time has not yet been reached. The state represented by the sensor data needs to be considered. Therefore, at this time, the operating state transition rule function that includes the influence function of the control signal and the influence function of the sensor data can be used to predict the state of the substation equipment at the next moment.

[0097] In step S44, when the time-varying function of the control signal reaches the threshold, it indicates that a state switch is about to occur. Therefore, the state of the substation equipment at the next moment can be determined based on the influence function of the control signal.

[0098] In a specific application scenario, the current operating state S of the digital twin model of the primary equipment in the substation is combined with... t0 , t0 to t ε At time t ε Given continuous sensor observations approaching time t1, and without knowing the sensor observations at time t1, infer the operating state S of the device. t1 First, the current state of the substation equipment is calculated based on key operating characteristic information, and then the influence function f1(X) is set. t The control signal threshold that can trigger a state transition of substation equipment is determined. The time-varying function of the input control signal X(t) is then calculated and compared with the control signal threshold. When the time-varying function of the control signal reaches the control signal threshold, the state of the substation equipment at the next moment is determined according to the influence function of the control signal. When the influence function of the control signal does not reach the control signal threshold, the state of the substation equipment at the next moment is predicted using the operating state transition rule function.

[0099] refer to Figure 4 In some embodiments, a rule-based model-based substation equipment operation status analysis device is provided, comprising:

[0100] The data acquisition module 101 is used to acquire key operating characteristic information of substation equipment;

[0101] The set establishment module 102 is used to establish a set of finite states of the device based on the key operating characteristic information;

[0102] The model building module 103 is used to build a rule model based on the key operating characteristic information and the finite set of equipment states;

[0103] The prediction module 104 is used to predict the operating status of substation equipment based on the rule model.

[0104] Specifically, in the acquisition module 101, the key operating characteristic information includes the control signals of the substation equipment and the sensor data of the substation equipment in various states.

[0105] In the set establishment module 102, the equipment finite state set includes a set of normal operation state, stop operation state, fault state, hot standby state and cold standby state of substation equipment.

[0106] The model building module 103 is also used for:

[0107] Define the factors that affect the status of substation equipment;

[0108] Based on the finite state set of the equipment and the factors affecting the state of the substation equipment, a real-time state function for the substation equipment is established.

[0109] Based on the real-time state function, the factors affecting the state of substation equipment, the control signals of substation equipment, and the sensor data of substation equipment in various states, an operating state transition rule function is established to obtain a rule model.

[0110] Furthermore, the real-time state function is expressed by the following formula:

[0111]

[0112] Among them, S t Let X(t) represent the state of the substation equipment at time t, X(t) be the control action of the current control signal on the substation equipment, flow(U,I,P,Q,f,……) be the factors affecting the state of the substation equipment, and S be the control action of the current control signal on the substation equipment. t-1 To represent the state of the substation equipment at time t-1, C(t) represents the flag bit of the equipment maintenance command.

[0113] Furthermore, the rule model is represented by the following formula:

[0114]

[0115] f(t) = λ1·f1(X) t )+λ2·f2(Y t );

[0116] Among them, S t+1 S represents the state of the substation equipment at time t+1. t Let f(t) be the state of the substation equipment at time t, f1(X) be the operating state transition rule function, and f1(X) be the state of the substation equipment at time t. tf2(Y) is the influence function of the control signal. t Y is the influence function of the sensor data, λ1 and λ2 are the coefficients of the influence function of the control signal and the influence function of the sensor data, respectively. t Let t be the sensor data at time t.

[0117] The prediction module 104 is also used for:

[0118] Calculate the current status of substation equipment;

[0119] Set a threshold value that allows control signals to cause state transitions in substation equipment;

[0120] Based on the influence function of the control signal, a threshold judgment is made. When the influence function value of the control signal does not reach the threshold, the state of the substation equipment at the next moment is predicted according to the operating state transition rule function.

[0121] Furthermore, when the influence function value of the control signal reaches the threshold, the state of the substation equipment at the next moment is determined based on the influence function of the control signal.

[0122] refer to Figure 5 In some embodiments, an electronic device is provided, including a processor 1 and a storage device 2, wherein the storage device 2 stores a plurality of instructions, and the processor 1 is used to read the plurality of instructions and execute the method described above.

[0123] The rule-based model-based substation equipment operation status analysis method and apparatus provided in this embodiment have at least the following beneficial effects:

[0124] (1) Based on the finite state machine theory, the various operating states of substation equipment are abstracted into a set of finite state machine states, which can more intuitively represent the different states of substation equipment.

[0125] (2) Based on the control signals of the station control equipment, the key operating characteristic information of the substation equipment, and the internal laws of state transition, establish the rules of state transition, so as to predict the state of the substation equipment before obtaining the operating characteristic information of the next moment.

[0126] (3) The coefficients and judgment thresholds of the operation state transition rule function are updated based on historical data to realize the continuous self-learning optimization of the rule function, so that the adaptability of the rule model to the substation equipment is gradually improved, and the accuracy of state prediction through this model is high.

[0127] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0128] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0129] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0130] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0131] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0132] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for analyzing the operating status of substation equipment based on a rule-based model, characterized in that, include: Collect key operating characteristic information of substation equipment; The key operational characteristic information includes the control signals of the substation equipment and the sensor data of the substation equipment in various states; Based on the aforementioned key operational characteristic information, a finite set of device states is established; Based on the key operational characteristic information and the finite state set of the equipment, a rule model is established: Factors affecting the state of substation equipment are defined; based on the finite state set of the equipment and the factors affecting the state of the substation equipment, a real-time state function for the substation equipment is established; based on the real-time state function, the control signals of the substation equipment, and the sensor data of the substation equipment in each state, an operational state transition rule function is established to obtain the rule model; the rule model is expressed by the following formula: ； in, This represents the state of the substation equipment at time t+1. Let f(t) represent the state of the substation equipment at time t, and let f(t) be the operating state transition rule function. Let be the influence function of the control signal. Y represents the influence function of the sensor data, where λ1 and λ2 are the coefficients of the influence function of the control signal and the influence function of the sensor data, respectively. t The sensor data at time t; Based on the rule model, the operating status of substation equipment is predicted.

2. The method according to claim 1, characterized in that, The set of finite states of the equipment includes the set of normal operation state, stop operation state, fault state, hot standby state and cold standby state of the substation equipment.

3. The method according to claim 1, characterized in that, The real-time state function is expressed by the following formula: ； Among them, S t Let X(t) represent the state of the substation equipment at time t, where X(t) is the control action of the current control signal on the substation equipment, and flow(U,I,P,Q,f,……) are the factors affecting the state of the substation equipment. t-1 To represent the state of the substation equipment at time t-1, C(t) represents the flag bit of the equipment maintenance command.

4. The method according to claim 1, characterized in that, Based on the aforementioned rule model, the operating status of substation equipment is predicted, including: Calculate the current status of substation equipment; Set a threshold value that allows control signals to cause state transitions in substation equipment; Based on the influence function of the control signal, a threshold judgment is made. When the influence function value of the control signal does not reach the threshold, the state of the substation equipment at the next moment is predicted according to the operating state transition rule function.

5. The method according to claim 4, characterized in that, When the influence function value of the control signal reaches the threshold, the state of the substation equipment at the next moment is determined according to the influence function of the control signal.

6. A substation equipment operation status analysis device based on a rule model applied to the method described in any one of claims 1-5, characterized in that, include: The data acquisition module is used to collect key operating characteristic information of substation equipment; The set establishment module is used to establish a set of finite states of the device based on the key operating characteristic information; The model building module is used to build a rule model based on the key operational characteristic information and the finite set of equipment states; The prediction module is used to predict the operating status of substation equipment based on the rule model.

7. An electronic device, characterized in that, The method includes a processor and a storage device, the storage device storing a plurality of instructions, and the processor being configured to read the plurality of instructions and execute the method as described in any one of claims 1-5.

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