Fault prediction method and device for power grid equipment, equipment, storage medium and program product

Abstract

The invention relates to a fault prediction method and device for power grid equipment, equipment, a storage medium and a program product, and the method comprises the steps: obtaining the current actual operation data, historical actual operation data and historical fault data of all power grid equipment in a power system, and then carrying out the fault prediction of all power grid equipment based on the current actual operation data and the historical actual operation data; simulating the current operation state of each power grid device according to the target simulation model, obtaining the simulation operation state data of all power grid devices in the simulation process, and finally carrying out the fault prediction of all power grid devices according to the target prediction model based on the simulation operation state data and the historical fault data, and obtaining a prediction result. According to the method, the current actual operation data and the historical operation data are combined for simulation, so that the operation state of the power grid equipment can be more accurately reproduced, the actual fault state and the operation trend of the power grid equipment can be captured, and the accuracy of subsequent fault prediction is further improved.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a fault prediction method, apparatus, device, storage medium and program product for power grid equipment. Background Art

[0002] With the continuous development and complexity of the power system, the importance of power grid transmission and distribution equipment in the operation of power supply enterprises has become increasingly prominent. Power grid transmission and distribution equipment is the core link of power transmission and distribution, and its stability and reliability are directly related to the safe operation of the entire power grid. However, during the long-term operation of power grid transmission and distribution equipment, it is easily affected by many factors, such as environmental conditions, climate change, equipment quality, etc. These problems may cause various equipment failures, such as aging, corrosion, electrical failure, etc. Therefore, it is particularly important to predict the failure of power grid equipment, detect potential problems in advance and take corresponding measures.

[0003] At present, valuable information is usually extracted by analyzing and processing the data of power grid equipment, and fault prediction is performed based on this information. However, the fault prediction method of the above power grid equipment has the problem of inaccurate prediction. Summary of the invention

[0004] Based on this, it is necessary to provide a fault prediction method, device, equipment, storage medium and program product for power grid equipment that can improve the accuracy of fault prediction in response to the above technical problems.

[0005] In a first aspect, the present application provides a fault prediction method for a power grid device, the method comprising:

[0006] Obtain the current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system;

[0007] Based on the current actual operation data and historical actual operation data, the current operation status of each power grid device is simulated according to the target simulation model, and the simulated operation status data of all power grid devices in the simulation process are obtained;

[0008] Based on the simulated operating status data and historical fault data, fault prediction is performed on all power grid equipment according to the target prediction model to obtain prediction results.

[0009] In one embodiment, the target simulation model includes an outage state simulation model, a load simulation model, and an iterative calculation model. Based on the current actual operation data and the historical actual operation data, the current operation state of each power grid device is simulated according to the target simulation model, and the simulated operation state data of all power grid devices in the simulation process are obtained, including:

[0010] Input the current actual operation data and the historical actual operation data into the outage state simulation model for simulation, and obtain the outage state simulation data of all power grid equipment during the simulation process;

[0011] Input the current actual operation data, historical actual operation data and outage state simulation data into the load simulation model for simulation, and obtain the load simulation data of all power grid equipment during the simulation process;

[0012] The outage state simulation data and load simulation data are input into the iterative calculation model for iterative calculation to obtain the simulated operation state data of all power grid equipment during the simulation process.

[0013] In one embodiment, the load simulation model includes a horizontal load sub-model and a minimum load shedding sub-model. Current actual operation data, historical actual operation data, and shutdown state simulation data are input into the load simulation model for simulation, and load simulation data of all power grid equipment in the simulation process is obtained, including:

[0014] Input the current actual operation data and the historical actual operation data into the horizontal load sub-model for simulation, and obtain the horizontal load simulation data of all power grid equipment during the simulation process;

[0015] The horizontal load simulation data and the outage state simulation data are input into the minimum load shedding sub-model for simulation, and the minimum load shedding simulation data of all power grid equipment in the simulation process are obtained.

[0016] In one embodiment, the target prediction model includes a failure state prediction model, a state association prediction model and a fault prediction model. Based on the simulated operation state data and the historical fault data, the target prediction model is used to perform fault prediction on all power grid equipment to obtain prediction results, including:

[0017] Input the simulated operation status data and historical fault data into the failure status prediction model for prediction, and obtain the failure status data of all power grid equipment;

[0018] Input the failure state data of all power grid equipment into the state association prediction model to perform state association and obtain state association data;

[0019] The state-related data of all power grid equipment are input into the fault prediction model to perform fault prediction and obtain the prediction results.

[0020] In one embodiment, the failure status data includes aging failure probability, aging failure unavailability rate and repairable failure unavailability rate. The simulated operation status data and historical fault data are input into the failure status prediction model for prediction to obtain the failure status data of all power grid equipment, including:

[0021] Input the simulated operation status data and historical fault data into the first prediction sub-model for prediction to obtain the aging failure probability of all power grid equipment;

[0022] The simulated operation status data, historical fault data and aging failure probability are input into the second prediction sub-model for prediction to obtain the aging failure unavailability rate of all power grid equipment;

[0023] The simulated operation status data and historical fault data are input into the third prediction sub-model for prediction to obtain the repairable failure unavailability rate of all power grid equipment.

[0024] In one embodiment, the method further comprises:

[0025] The actual current operation data and historical prediction results of all power grid equipment in the power system are stored in the blockchain;

[0026] Obtain the current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system, including:

[0027] Obtain current actual operation data, historical actual operation data, and historical fault data from the blockchain.

[0028] In a second aspect, the present application further provides a fault prediction device for a power grid device, the device comprising:

[0029] An acquisition module is used to acquire current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system;

[0030] The simulation module is used to simulate the current operating status of each power grid device according to the target simulation model based on the current actual operating data and the historical actual operating data, and obtain the simulated operating status data of all power grid devices during the simulation process;

[0031] The prediction module is used to predict the faults of all power grid equipment based on the simulated operating status data and historical fault data according to the target prediction model to obtain the prediction results.

[0032] In a third aspect, the present application further provides a computer device, the computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program:

[0033] Obtain the current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system;

[0034] Based on the current actual operation data and historical actual operation data, the current operation status of each power grid device is simulated according to the target simulation model, and the simulated operation status data of all power grid devices in the simulation process are obtained;

[0035] Based on the simulated operating status data and historical fault data, fault prediction is performed on all power grid equipment according to the target prediction model to obtain prediction results.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0037] Obtain the current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system;

[0038] Based on the current actual operation data and historical actual operation data, the current operation status of each power grid device is simulated according to the target simulation model, and the simulated operation status data of all power grid devices in the simulation process are obtained;

[0039] Based on the simulated operating status data and historical fault data, fault prediction is performed on all power grid equipment according to the target prediction model to obtain prediction results.

[0040] In a fifth aspect, the present application further provides a computer program product, the computer program product comprising a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0041] Obtain the current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system;

[0042] Based on the current actual operation data and historical actual operation data, the current operation status of each power grid device is simulated according to the target simulation model, and the simulated operation status data of all power grid devices in the simulation process are obtained;

[0043] Based on the simulated operating status data and historical fault data, fault prediction is performed on all power grid equipment according to the target prediction model to obtain prediction results.

[0044] The above-mentioned fault prediction method, device, equipment, storage medium and program product of the power grid equipment obtains the current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system, and then simulates the current operation state of each power grid equipment according to the target simulation model based on the current actual operation data and the historical actual operation data, obtains the simulated operation state data of all power grid equipment during the simulation process, and finally predicts the faults of all power grid equipment according to the target prediction model based on the simulated operation state data and the historical fault data, and obtains the prediction results. In the above-mentioned method, by combining the current actual operation data and the historical operation data for simulation, the operation state of the power grid equipment can be reproduced more accurately, and the actual fault state and operation trend of the power grid equipment can be captured, thereby improving the accuracy of subsequent fault prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is an internal structure diagram of a computer device in one embodiment;

[0046] Figure 2 A schematic diagram of a flow chart of a fault prediction method for power grid equipment in one embodiment;

[0047] Figure 3 A schematic flow chart of a method for predicting a fault of a power grid device in another embodiment;

[0048] Figure 4 A schematic flow chart of a method for predicting a fault of a power grid device in another embodiment;

[0049] Figure 5 A schematic flow chart of a method for predicting a fault of a power grid device in another embodiment;

[0050] Figure 6 A schematic flow chart of a method for predicting a fault of a power grid device in another embodiment;

[0051] Figure 7 A schematic flow chart of a method for predicting a fault of a power grid device in another embodiment;

[0052] Figure 8 It is a flowchart of a fault prediction method for power grid equipment in another embodiment. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0054] With the continuous development and complexity of the power system, the importance of power grid transmission and distribution equipment in the operation of power supply enterprises has become increasingly prominent. Power grid transmission and distribution equipment is the core link of power transmission and distribution, and its stability and reliability are directly related to the safe operation of the entire power grid. However, during the long-term operation, power grid transmission and distribution equipment is easily affected by various factors, such as environmental conditions, climate change, equipment quality, etc. These problems may cause various equipment failures, such as aging, corrosion, electrical failures, etc. Therefore, it is particularly important to predict faults of power grid equipment, detect potential problems in advance and take corresponding measures. At present, valuable information is usually extracted by analyzing and processing the data of power grid equipment, and fault prediction is performed based on this information. However, the fault prediction method of the above-mentioned power grid equipment has the problem of inaccurate prediction.

[0055] The present application provides a fault prediction method for power grid equipment, aiming to solve the above-mentioned technical problems. The following embodiments will specifically illustrate the fault prediction method for power grid equipment described in the present application.

[0056] The fault prediction method for power grid equipment provided in the embodiment of the present application can be applied to Figure 1 In the computer device shown in the figure, the computer device can be a terminal or a server, and its internal structure diagram can be as shown in FIG. Figure 1 As shown, the computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a fault prediction method for power grid equipment is realized. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0057] Those skilled in the art will understand that Figure 1The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0058] In one embodiment, Figure 2 As shown, a fault prediction method for power grid equipment is provided, and the method is applied to Figure 1 The computer device in the example is used to illustrate, including the following steps:

[0059] S201, obtaining current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system.

[0060] Among them, the current actual operation data includes at least one of the information such as current, voltage, temperature, vibration, humidity, power, frequency, sound, etc.

[0061] In the embodiment of the present application, an information collection device can be pre-installed on all power grid equipment in the power system, and the information collection device can be connected to the computer device. Specifically, various sensors can be installed to obtain, such as current, voltage, temperature, vibration, sound, etc., to collect the operating status, working parameters, fault information, etc. of the equipment in real time; smart meters, smart transformers, smart switches, etc. are deployed in the power network to achieve accurate measurement, quality monitoring, and remote control of electric energy; smart gateways, smart terminals, smart controllers, etc. are deployed in the power network to centrally manage, coordinate and control, and optimize the dispatch of power equipment, and use satellites, drones, ground radars, etc. to achieve aerial monitoring, inspection, and maintenance of power equipment and networks. When predicting faults in the power system, the computer device can collect the current actual operation data of each power grid device in real time through the information collection device. Optionally, the information collection device can upload the collected current actual operation data of each power grid device to the computer device in real time. The computer device can store the data after each fault analysis to a preset storage location. When it is necessary to predict faults in the power system, the historical actual operation data and historical fault data of all power grid devices can be obtained from the preset storage location.

[0062] S202, based on the current actual operation data and the historical actual operation data, the current operation status of each power grid device is simulated according to the target simulation model, and the simulated operation status data of all power grid devices in the simulation process are obtained.

[0063] The target simulation model is a model constructed based on the Monte Carlo simulation method. The simulated operation status data includes at least one of shutdown state simulation data, horizontal load simulation data and minimum load shedding simulation data.

[0064] In the embodiment of the present application, the computer device can construct a target simulation model in advance according to the structural information of the power system, the coupling relationship between each power grid device and the Monte Carlo simulation method. After the computer device obtains the current actual operation data, historical actual operation data and historical fault data of all power grid devices based on the above steps, the current actual operation data and the historical actual operation data can be input into the target simulation model, and the current operation state of each power grid device is simulated according to the target simulation model to obtain the simulated operation state data of all power grid devices during the simulation process. Optionally, after obtaining the current actual operation data, historical actual operation data and historical fault data of all power grid devices, the computer device can pre-process the current actual operation data, such as performing data cleaning and data standardization and other processing operations, and then obtain the pre-processed actual operation data, and then input the pre-processed actual operation data and the historical actual operation data into the target simulation model for simulation, and obtain the simulated operation state data of all power grid devices during the simulation process.

[0065] S203, based on the simulated operation status data and historical fault data, fault prediction is performed on all power grid equipment according to the target prediction model to obtain prediction results.

[0066] Among them, the prediction result indicates that the power system is in a normal operating state or the power system is in an abnormal operating state.

[0067] In the embodiment of the present application, the computer device can construct a target prediction model in advance based on the insulation state, insulation age, dimensional parameters, scale parameters, coupling relationship between each power grid device, etc. After the computer device obtains the simulated operation state data based on the above steps, the simulated operation state data and historical fault data can be input into the target prediction model, and then the fault prediction of all power grid devices is performed according to the target prediction model to obtain the prediction result, and when the prediction result indicates that the power system is in an abnormal operation state, an alarm prompt is issued.

[0068] The fault prediction method of the power grid equipment provided in the embodiment of the present application obtains the current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system, and then simulates the current operation state of each power grid equipment according to the target simulation model based on the current actual operation data and the historical actual operation data, obtains the simulated operation state data of all power grid equipment during the simulation process, and finally predicts the faults of all power grid equipment according to the target prediction model based on the simulated operation state data and the historical fault data, and obtains the prediction results. In the above method, by combining the current actual operation data and the historical operation data for simulation, the operation state of the power grid equipment can be reproduced more accurately, and the actual fault state and operation trend of the power grid equipment can be captured, thereby improving the accuracy of subsequent fault prediction.

[0069] In one embodiment, the target simulation model includes an outage state simulation model, a load simulation model, and an iterative calculation model. On this basis, a specific implementation method for simulating the current operating state of each power grid device according to the target simulation model is also provided, such as Figure 3 As shown, the above step S202 of "based on the current actual operation data and the historical actual operation data, simulating the current operation state of each power grid device according to the target simulation model, and obtaining the simulated operation state data of all power grid devices during the simulation process" includes:

[0070] S301, inputting current actual operation data and historical actual operation data into an outage state simulation model for simulation, and obtaining outage state simulation data of all power grid devices during the simulation process.

[0071] The target simulation model includes an outage state simulation model, a load simulation model, and an iterative calculation model. The outage state simulation data is used to reflect the outage state of the power grid equipment.

[0072] In an embodiment of the present application, the computer device can pre-construct an outage state simulation model based on the full-rated operating state, outage state, and derating state of each power grid device. After the computer device obtains the current actual operating data and the historical actual operating data, the current actual operating data and the historical actual operating data can be input into the outage state simulation model for simulation, and the outage state simulation data of all power grid devices in the simulation process can be obtained. Specifically, the outage state simulation process can be: generate a state curve based on the current actual operating data and the historical actual operating data, and then generate a random number Ri uniformly distributed in the interval [0,1] for the i-th power grid device based on the state curve, and determine the data corresponding to Ri in the state curve as the data to be tested, and then input the data to be tested into the target simulation model, and the outage state simulation data of all power grid devices in the simulation process can be obtained. Among them, the target simulation model can be represented by the following relationship, and the schematic diagram can be seen in Figure 4 :

[0073]

[0074] Where Ii is the indicator variable of the sampled state of the ith device; PFi and PPi are the probabilities of the forced outage and derated states of the ith device, respectively. For the two-state model without derated states, only the PFi value is required, and PPi is set to zero. Separate random number sampling can be used for planned outages and common cause outages. All the sampled device states form a system state for system analysis.

[0075] S302, inputting the current actual operation data, the historical actual operation data and the outage state simulation data into the load simulation model for simulation, and obtaining the load simulation data of all the power grid equipment during the simulation process.

[0076] Among them, load simulation data is used to reflect the current load status of power grid equipment.

[0077] In the embodiment of the present application, the computer device can construct a load simulation model in advance according to the load conditions that each power grid device can bear. After the computer device obtains the outage state simulation data based on the above steps, the current actual operation data, the historical actual operation data and the outage state simulation data can be input into the load simulation model for simulation, and the load simulation data of all power grid devices in the simulation process can be obtained.

[0078] Specifically, the load simulation model includes a horizontal load sub-model and a minimum load shedding sub-model. On this basis, Figure 5 As shown, the above step S302 of "inputting the current actual operation data, the historical actual operation data and the outage state simulation data into the load simulation model for simulation to obtain the load simulation data of all power grid equipment during the simulation process" includes:

[0079] S3021, input the current actual operation data and the historical actual operation data into the horizontal load sub-model for simulation, and obtain the horizontal load simulation data of all power grid equipment during the simulation process.

[0080] In the embodiment of the present application, the computer device can construct a load simulation model in advance according to the load conditions that each power grid device can bear. Specifically, the K-means clustering method can be used to create a multi-level load model, and the load uncertainty of each device load level can be represented by a normally distributed random variable:

[0081]

[0082] in, is the i-th load level on the j-th curve obtained by K-means clustering method in the multi-level load model; yes Random sample values ​​nearby; is a standard normally distributed random variable corresponding to the i-th load level on the j-th curve; Is a representative The standard deviation of the uncertainty.

[0083] After the computer device obtains the current actual operation data and the historical actual operation data based on the above steps, the current actual operation data and the historical actual operation data can be input into the horizontal load sub-model for simulation to obtain the horizontal load simulation data of all power grid equipment during the simulation process.

[0084] S3022, input the horizontal load simulation data and the outage state simulation data into the minimum load shedding sub-model for simulation, and obtain the minimum load shedding simulation data of all power grid equipment during the simulation process.

[0085] In the embodiment of the present application, the computer device can construct a minimum load shedding sub-model in advance according to the load conditions that each power grid device can bear. Specifically, the power flow calculation can be performed according to the outage state simulation model and the horizontal load sub-model. If a power flow exceeds the limit, the optimal power flow calculation is performed to obtain the minimum load shedding model. After the computer device obtains the horizontal load simulation data and the outage state simulation data based on the above steps, the horizontal load simulation data and the outage state simulation data can be input into the minimum load shedding sub-model for simulation to obtain the minimum load shedding simulation data of all power grid devices during the simulation process.

[0086] S303, inputting the outage state simulation data and the load simulation data into an iterative calculation model for iterative calculation to obtain the simulated operation state data of all power grid equipment during the simulation process.

[0087] In the embodiment of the present application, after the computer device obtains the outage state simulation data and the load simulation data based on the above steps, the outage state simulation data and the load simulation data can be input into the iterative calculation model for iterative calculation to obtain the simulated operation state data of all power grid devices in the simulation process. Specifically, the computer device can run the iterative calculation model for thousands of iterative calculations, and the final algorithm will converge to a theoretical result, and the outage state simulation data, horizontal load simulation data, and minimum load shedding simulation data in the theoretical result are used as simulated operation state data.

[0088] In one embodiment, the target prediction model includes a failure state prediction model, a state association prediction model and a fault prediction model, and also provides a specific implementation method for performing fault prediction on all power grid devices according to the target prediction model, such as Figure 6 As shown, the above step S203 of "based on the simulated operation status data and the historical fault data, predicting the faults of all power grid devices according to the target prediction model to obtain the prediction results" includes:

[0089] S401, inputting simulated operation status data and historical fault data into a failure status prediction model for prediction, and obtaining failure status data of all power grid equipment.

[0090] The target prediction model includes a failure state prediction model, a state association prediction model and a fault prediction model. The failure state prediction model includes an aging failure probability prediction sub-model, an aging failure unavailability rate prediction sub-model and a repairable failure unavailability rate prediction sub-model.

[0091] In the embodiment of the present application, the computer device can construct a failure state prediction model, a state association prediction model and a fault prediction model in advance according to the insulation state, insulation age, dimensional parameters, scale parameters, coupling relationship between each power grid device and other data of the power grid device. The specific construction process is as follows:

[0092] (1) According to the insulation age, scale parameter and size parameter, a sub-model for predicting the aging failure probability of power grid equipment is established based on the Weibull distribution:

[0093]

[0094]

[0095] Among them, the formula calculates the insulation age When the device is in The probability of aging failure within a time interval . Indicates the length of the time interval.

[0096] Among them, I DP It represents the current parameter related to the insulation of the equipment, L1 represents the insulation age, α is the scale parameter, and β is the size parameter. The scale parameter and size parameter of the Weibull distribution can be fitted according to the historical fault information and the simulated operation status data. By fitting the historical fault data, the scale parameter α and the scale parameter β of the distribution can be determined.

[0097] (2) The aging failure unavailability rate prediction sub-model can be expressed by the following relationship:

[0098]

[0099] (3) The repairable failure unavailability rate prediction sub-model can be expressed by the following relationship:

[0100]

[0101] in, is the frequency of repairable failures, is the mean time to repair and 8760 is the total number of hours in a year.

[0102] (4) The state association prediction model is used to calculate the comprehensive unavailability rate, which is the aging failure unavailability rate. and repairable failure unavailability rate Add them together and consider their mutual influence to get the comprehensive unavailability rate , can be expressed by the following relationship:

[0103]

[0104] (5) The fault prediction model is used to determine the relationship between the comprehensive unavailability rate and the preset threshold. If the comprehensive unavailability rate does not exceed the preset threshold, that is, the comprehensive unavailability rate ≤ the preset threshold, then it can be determined that the prediction result indicates that the power system is in a normal operating state. If the comprehensive unavailability rate exceeds the preset threshold, that is, the comprehensive unavailability rate > the preset threshold, then it can be determined that the prediction result indicates that the power system is in an abnormal operating state.

[0105] Specifically, the above failure status data include aging failure probability, aging failure unavailability rate and repairable failure unavailability rate. On this basis, Figure 7 As shown, the above step S401 of "inputting the simulated operation status data and the historical fault data into the failure status prediction model for prediction to obtain the failure status data of all power grid devices" includes:

[0106] S4011, input the simulated operation status data and historical fault data into the first prediction sub-model for prediction, and obtain the aging failure probability of all power grid equipment.

[0107] Among them, the first prediction sub-model is an aging failure probability prediction sub-model.

[0108] In an embodiment of the present application, after the computer device obtains the simulated operation status data and the historical fault data, the simulated operation status data and the historical fault data can be input into the first prediction sub-model for prediction to obtain the aging failure probability of all power grid equipment.

[0109] S4012, inputting the simulated operation status data, historical fault data and aging failure probability into the second prediction sub-model for prediction, and obtaining the aging failure unavailability rate of all power grid equipment.

[0110] Among them, the second prediction sub-model is the aging failure unavailability rate prediction sub-model.

[0111] In an embodiment of the present application, after the computer device obtains the simulated operating status data, historical fault data and aging failure probability, the simulated operating status data, historical fault data and aging failure probability can be input into the second prediction sub-model for prediction to obtain the aging failure unavailability rate of all power grid equipment.

[0112] S4013, inputting the simulated operation status data and the historical fault data into the third prediction sub-model for prediction, and obtaining the repairable failure unavailability rate of all power grid equipment.

[0113] Among them, the third prediction sub-model is the repairable failure unavailability rate prediction sub-model.

[0114] In an embodiment of the present application, after the computer device obtains the simulated operation status data and the historical fault data, the simulated operation status data and the historical fault data can be input into the third prediction sub-model for prediction to obtain the repairable failure unavailability rate of all power grid equipment.

[0115] S402, inputting the failure status data of all power grid devices into a status association prediction model to perform status association and obtain status association data.

[0116] Among them, the status-related data is the comprehensive unavailability rate.

[0117] In the embodiment of the present application, after the computer device obtains the failure status data of all power grid devices, the failure status data of all power grid devices can be input into the state association prediction model to perform state association to obtain state association data.

[0118] S403, inputting the state-related data of all power grid devices into a fault prediction model to perform fault prediction and obtain a prediction result.

[0119] In the embodiment of the present application, after the computer device obtains the status-related data of the power grid equipment, the status-related data of all the power grid equipment can be input into the fault prediction model to perform fault prediction and obtain a prediction result.

[0120] In one embodiment, Figure 8 As shown, the fault prediction of the above power grid equipment also includes:

[0121] S204, storing the current actual operation data and historical prediction results of all power grid equipment in the power system in the blockchain.

[0122] In the embodiment of the present application, after each fault prediction of the power system, the computer device stores the fault prediction result and the current actual operation data in the blockchain, so the blockchain stores each actual operation data (i.e., historical actual operation data) and historical prediction results (historical fault data). Specifically, after obtaining the current actual operation data, the computer device can transmit the data to the network through a wired or wireless network, and then integrate the data according to the edge integration technology, and then store the integrated data in the blockchain. Through the asymmetric encryption algorithm of the blockchain, the encrypted transmission of the power grid equipment information is guaranteed to prevent the information from being stolen or tampered with. Through the blockchain consensus mechanism, the distributed storage of the power grid equipment information is realized to avoid single point failure or loss of information. Through the blockchain smart contract, the automatic execution of the power grid equipment information is realized to reduce human intervention or errors. Through the blockchain information tracing, the whole process of the power grid equipment information is recorded, which is convenient for the information query and supervision of the power grid equipment.

[0123] Correspondingly, when the computer device executes S201 "obtaining the current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system", the specific execution steps are: obtaining the current actual operation data, historical actual operation data and historical fault data from the blockchain.

[0124] In an embodiment of the present application, a computer device can obtain current actual operation data, historical actual operation data and historical fault data from the blockchain.

[0125] The method described in the embodiments of the present application utilizes edge-cloud fusion technology and data, and integrates data into various enterprise platforms through certain data input specifications and unified standards, thereby solving the problem of inconsistent data formats and standards to a certain extent.

[0126] Based on all the above embodiments, a fault prediction method for power grid equipment is also provided, the method comprising:

[0127] S501, store the current actual operation data and historical prediction results of all power grid equipment in the power system in the blockchain.

[0128] S502, obtaining current actual operation data, historical actual operation data and historical fault data from the blockchain.

[0129] S503, inputting the current actual operation data and the historical actual operation data into the outage state simulation model for simulation, and obtaining the outage state simulation data of all power grid devices during the simulation process.

[0130] S504, inputting the current actual operation data and the historical actual operation data into the horizontal load sub-model for simulation, and obtaining the horizontal load simulation data of all power grid equipment during the simulation process.

[0131] S505, inputting the horizontal load simulation data and the outage state simulation data into the minimum load shedding sub-model for simulation, and obtaining the minimum load shedding simulation data of all power grid equipment during the simulation process.

[0132] S506, inputting the outage state simulation data and the load simulation data into the iterative calculation model for iterative calculation to obtain the simulated operation state data of all power grid equipment during the simulation process.

[0133] S507, inputting the simulated operation status data and the historical fault data into the first prediction sub-model for prediction, and obtaining the aging failure probability of all power grid equipment.

[0134] S508, inputting the simulated operation status data, historical fault data and aging failure probability into the second prediction sub-model for prediction, and obtaining the aging failure unavailability rate of all power grid equipment.

[0135] S509: Input the simulated operation status data and the historical fault data into the third prediction sub-model for prediction to obtain the repairable failure unavailability rate of all power grid equipment.

[0136] The method described in the embodiment of the present application uses the Internet of Things and blockchain technology to ensure the timely identification and acquisition of equipment data information of the power grid; through the Internet of Things technology, the point-to-point connection of power equipment data from the energy supply end to the energy consumption end is realized, and the full life cycle data of power equipment is provided to the power grid enterprises in real time; according to the immutability and security of blockchain technology, the safe and reliable transmission of power grid equipment data is guaranteed, and a trust relationship between devices is established. Through big data technology and artificial intelligence and other technologies, the implementation monitoring and prediction of equipment status is realized; the equipment data in various links such as power transmission, distribution, and substation are opened up, such as transformer data such as voltage, current, and temperature in the substation link, switch status and operation records in the transmission and distribution link, and line equipment data such as line temperature and line current. Through remote monitoring and deployment, intelligent monitoring and digital operation of power grid transmission and distribution management are realized. Using new generation digital technologies such as the Internet of Things and blockchain, comprehensive perception of power grid equipment, secure transmission and storage of data, and intelligent control and optimization of equipment are achieved. Realize the wide application and value creation of digital technology in the power field, and contribute to the safe, green and efficient development of the power industry. After collecting reliable power grid equipment data based on the Internet of Things and blockchain technology, the power grid equipment status and historical equipment operation data are analyzed through big data analysis technologies such as the Monte Carlo simulation method, and equipment failure modes and trends are identified for prediction and diagnosis, early warning and maintenance, reducing equipment downtime and maintenance costs.

[0137] The methods described in the above steps are all described in the above embodiments. Please refer to the above description for details and will not be repeated here.

[0138] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0139] Based on the same inventive concept, the embodiment of the present application also provides a fault prediction device for a power grid device for implementing the fault prediction method for the power grid device involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the fault prediction device for one or more power grid devices provided below can refer to the limitations of the fault prediction method for the power grid device above, and will not be repeated here.

[0140] In one embodiment, a fault prediction device for a power grid device is provided, comprising:

[0141] The acquisition module is used to obtain the current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system.

[0142] The simulation module is used to simulate the current operating status of each power grid device based on the current actual operating data and historical actual operating data according to the target simulation model, and obtain the simulated operating status data of all power grid devices during the simulation process.

[0143] The prediction module is used to predict the faults of all power grid equipment based on the simulated operating status data and historical fault data according to the target prediction model to obtain the prediction results.

[0144] In one embodiment, the above-mentioned simulation module is also used to input the current actual operation data and the historical actual operation data into the outage state simulation model for simulation, so as to obtain the outage state simulation data of all power grid equipment during the simulation process; input the current actual operation data, the historical actual operation data and the outage state simulation data into the load simulation model for simulation, so as to obtain the load simulation data of all power grid equipment during the simulation process; input the outage state simulation data and the load simulation data into the iterative calculation model for iterative calculation, so as to obtain the simulated operation state data of all power grid equipment during the simulation process.

[0145] In one embodiment, the above-mentioned simulation module is also used to input the current actual operation data and the historical actual operation data into the horizontal load sub-model for simulation, so as to obtain the horizontal load simulation data of all power grid equipment during the simulation process; and input the horizontal load simulation data and the outage status simulation data into the minimum load shedding sub-model for simulation, so as to obtain the minimum load shedding simulation data of all power grid equipment during the simulation process.

[0146] In one embodiment, the above-mentioned prediction module is also used to input the simulated operation status data and historical fault data into the failure status prediction model for prediction, so as to obtain the failure status data of all power grid equipment; input the failure status data of all power grid equipment into the state association prediction model for state association, so as to obtain state association data; input the state association data of all power grid equipment into the fault prediction model for fault prediction, so as to obtain the prediction result.

[0147] In one embodiment, the above-mentioned prediction module is also used to input the simulated operation status data and historical fault data into the first prediction sub-model for prediction, so as to obtain the aging failure probability of all power grid equipment; input the simulated operation status data, historical fault data and aging failure probability into the second prediction sub-model for prediction, so as to obtain the aging failure unavailability rate of all power grid equipment; input the simulated operation status data and historical fault data into the third prediction sub-model for prediction, so as to obtain the repairable failure unavailability rate of all power grid equipment.

[0148] In one embodiment, the fault prediction device for the power grid equipment further includes:

[0149] The storage module is used to store the current actual operation data and historical prediction results of all power grid equipment in the power system in the blockchain.

[0150] Correspondingly, the acquisition module is specifically used to obtain current actual operation data, historical actual operation data and historical fault data from the blockchain.

[0151] Each module in the fault prediction device of the above-mentioned power grid equipment can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0152] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0153] Obtain the current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system;

[0154] Based on the current actual operation data and historical actual operation data, the current operation status of each power grid device is simulated according to the target simulation model, and the simulated operation status data of all power grid devices in the simulation process are obtained;

[0155] Based on the simulated operating status data and historical fault data, fault prediction is performed on all power grid equipment according to the target prediction model to obtain prediction results.

[0156] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0157] Input the current actual operation data and the historical actual operation data into the outage state simulation model for simulation, and obtain the outage state simulation data of all power grid equipment during the simulation process;

[0158] Input the current actual operation data, historical actual operation data and outage state simulation data into the load simulation model for simulation, and obtain the load simulation data of all power grid equipment during the simulation process;

[0159] The outage state simulation data and load simulation data are input into the iterative calculation model for iterative calculation to obtain the simulated operation state data of all power grid equipment during the simulation process.

[0160] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0161] Input the current actual operation data and the historical actual operation data into the horizontal load sub-model for simulation, and obtain the horizontal load simulation data of all power grid equipment during the simulation process;

[0162] The horizontal load simulation data and the outage state simulation data are input into the minimum load shedding sub-model for simulation, and the minimum load shedding simulation data of all power grid equipment in the simulation process are obtained.

[0163] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0164] Input the simulated operation status data and historical fault data into the failure status prediction model for prediction, and obtain the failure status data of all power grid equipment;

[0165] Input the failure state data of all power grid equipment into the state association prediction model to perform state association and obtain state association data;

[0166] The state-related data of all power grid equipment are input into the fault prediction model to perform fault prediction and obtain the prediction results.

[0167] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0168] Input the simulated operation status data and historical fault data into the first prediction sub-model for prediction to obtain the aging failure probability of all power grid equipment;

[0169] The simulated operation status data, historical fault data and aging failure probability are input into the second prediction sub-model for prediction to obtain the aging failure unavailability rate of all power grid equipment;

[0170] The simulated operation status data and historical fault data are input into the third prediction sub-model for prediction to obtain the repairable failure unavailability rate of all power grid equipment.

[0171] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0172] The actual current operation data and historical prediction results of all power grid equipment in the power system are stored in the blockchain;

[0173] Obtain the current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system, including:

[0174] Obtain current actual operation data, historical actual operation data, and historical fault data from the blockchain.

[0175] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0176] Obtain the current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system;

[0177] Based on the current actual operation data and historical actual operation data, the current operation status of each power grid device is simulated according to the target simulation model, and the simulated operation status data of all power grid devices in the simulation process are obtained;

[0178] Based on the simulated operating status data and historical fault data, fault prediction is performed on all power grid equipment according to the target prediction model to obtain prediction results.

[0179] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0180] Input the current actual operation data and the historical actual operation data into the outage state simulation model for simulation, and obtain the outage state simulation data of all power grid equipment during the simulation process;

[0181] Input the current actual operation data, historical actual operation data and outage state simulation data into the load simulation model for simulation, and obtain the load simulation data of all power grid equipment during the simulation process;

[0182] The outage state simulation data and load simulation data are input into the iterative calculation model for iterative calculation to obtain the simulated operation state data of all power grid equipment during the simulation process.

[0183] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0184] Input the current actual operation data and the historical actual operation data into the horizontal load sub-model for simulation, and obtain the horizontal load simulation data of all power grid equipment during the simulation process;

[0185] The horizontal load simulation data and the outage state simulation data are input into the minimum load shedding sub-model for simulation, and the minimum load shedding simulation data of all power grid equipment in the simulation process are obtained.

[0186] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0187] Input the simulated operation status data and historical fault data into the failure status prediction model for prediction, and obtain the failure status data of all power grid equipment;

[0188] Input the failure state data of all power grid equipment into the state association prediction model to perform state association and obtain state association data;

[0189] The state-related data of all power grid equipment are input into the fault prediction model to perform fault prediction and obtain the prediction results.

[0190] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0191] Input the simulated operation status data and historical fault data into the first prediction sub-model for prediction to obtain the aging failure probability of all power grid equipment;

[0192] The simulated operation status data, historical fault data and aging failure probability are input into the second prediction sub-model for prediction to obtain the aging failure unavailability rate of all power grid equipment;

[0193] The simulated operation status data and historical fault data are input into the third prediction sub-model for prediction to obtain the repairable failure unavailability rate of all power grid equipment.

[0194] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0195] The actual current operation data and historical prediction results of all power grid equipment in the power system are stored in the blockchain;

[0196] Obtain the current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system, including:

[0197] Obtain current actual operation data, historical actual operation data, and historical fault data from the blockchain.

[0198] The above embodiment provides a computer-readable storage medium, whose implementation principle and technical effect are similar to those of the above method embodiment, and will not be repeated here.

[0199] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0200] Obtain the current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system;

[0201] Based on the current actual operation data and historical actual operation data, the current operation status of each power grid device is simulated according to the target simulation model, and the simulated operation status data of all power grid devices in the simulation process are obtained;

[0202] Based on the simulated operating status data and historical fault data, fault prediction is performed on all power grid equipment according to the target prediction model to obtain prediction results.

[0203] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0204] Input the current actual operation data and the historical actual operation data into the outage state simulation model for simulation, and obtain the outage state simulation data of all power grid equipment during the simulation process;

[0205] Input the current actual operation data, historical actual operation data and outage state simulation data into the load simulation model for simulation, and obtain the load simulation data of all power grid equipment during the simulation process;

[0206] The outage state simulation data and load simulation data are input into the iterative calculation model for iterative calculation to obtain the simulated operation state data of all power grid equipment during the simulation process.

[0207] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0208] Input the current actual operation data and the historical actual operation data into the horizontal load sub-model for simulation, and obtain the horizontal load simulation data of all power grid equipment during the simulation process;

[0209] The horizontal load simulation data and the outage state simulation data are input into the minimum load shedding sub-model for simulation, and the minimum load shedding simulation data of all power grid equipment in the simulation process are obtained.

[0210] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0211] Input the simulated operation status data and historical fault data into the failure status prediction model for prediction, and obtain the failure status data of all power grid equipment;

[0212] Input the failure state data of all power grid equipment into the state association prediction model to perform state association and obtain state association data;

[0213] The state-related data of all power grid equipment are input into the fault prediction model to perform fault prediction and obtain the prediction results.

[0214] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0215] Input the simulated operation status data and historical fault data into the first prediction sub-model for prediction to obtain the aging failure probability of all power grid equipment;

[0216] The simulated operation status data, historical fault data and aging failure probability are input into the second prediction sub-model for prediction to obtain the aging failure unavailability rate of all power grid equipment;

[0217] The simulated operation status data and historical fault data are input into the third prediction sub-model for prediction to obtain the repairable failure unavailability rate of all power grid equipment.

[0218] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0219] The actual current operation data and historical prediction results of all power grid equipment in the power system are stored in the blockchain;

[0220] Obtain the current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system, including:

[0221] Obtain current actual operation data, historical actual operation data, and historical fault data from the blockchain.

[0222] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0223] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0224] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A fault prediction method for power grid equipment, characterized in that: The method comprises: Obtain the current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system; Based on the current actual operation data and the historical actual operation data, simulating the current operation state of each of the power grid devices according to the target simulation model, and obtaining simulated operation state data of all the power grid devices during the simulation process; Based on the simulated operating status data and the historical fault data, fault prediction is performed on all the power grid equipment according to a target prediction model to obtain a prediction result.

2. The method according to claim 1, characterized in that The target simulation model includes an outage state simulation model, a load simulation model, and an iterative calculation model. Based on the current actual operation data and the historical actual operation data, the current operation state of each of the power grid devices is simulated according to the target simulation model, and the simulated operation state data of all the power grid devices in the simulation process are obtained, including: Inputting the current actual operation data and the historical actual operation data into the outage state simulation model for simulation, and obtaining outage state simulation data of all power grid devices during the simulation process; Input the current actual operation data, the historical actual operation data and the outage state simulation data into the load simulation model for simulation, and obtain the load simulation data of all the power grid equipment during the simulation process; The outage state simulation data and the load simulation data are input into the iterative calculation model for iterative calculation to obtain the simulated operation state data of all the power grid equipment during the simulation process.

3. The method according to claim 2, characterized in that The load simulation model includes a horizontal load sub-model and a minimum load shedding sub-model. The current actual operation data, the historical actual operation data and the outage state simulation data are input into the load simulation model for simulation to obtain the load simulation data of all power grid devices during the simulation process, including: Input the current actual operation data and the historical actual operation data into the horizontal load sub-model for simulation, and obtain horizontal load simulation data of all power grid devices during the simulation process; The horizontal load simulation data and the outage state simulation data are input into the minimum load shedding sub-model for simulation, so as to obtain the minimum load shedding simulation data of all the power grid equipment during the simulation process.

4. The method according to any one of claims 1 to 3, characterized in that: The target prediction model includes a failure state prediction model, a state association prediction model and a fault prediction model. Based on the simulated operation state data and the historical fault data, fault prediction is performed on all the power grid devices according to the target prediction model to obtain prediction results, including: Inputting the simulated operation status data and the historical fault data into the failure status prediction model for prediction to obtain the failure status data of all the power grid devices; Inputting the failure state data of all the power grid devices into the state association prediction model to perform state association to obtain state association data; The state-related data of all the power grid devices are input into the fault prediction model to perform fault prediction and obtain a prediction result.

5. The method according to claim 4, characterized in that The failure state data includes aging failure probability, aging failure unavailability rate and repairable failure unavailability rate. The simulated operation state data and the historical fault data are input into the failure state prediction model for prediction to obtain the failure state data of all power grid devices, including: Inputting the simulated operation status data and the historical fault data into the first prediction sub-model for prediction to obtain the aging failure probability of all the power grid equipment; Inputting the simulated operation status data, the historical fault data and the aging failure probability into a second prediction sub-model for prediction to obtain the aging failure unavailability rate of all power grid equipment; The simulated operation status data and the historical fault data are input into a third prediction sub-model for prediction to obtain the repairable failure unavailability rate of all power grid equipment.

6. The method according to claim 1, characterized in that The method further comprises: The current actual operation data and historical prediction results of all power grid equipment in the power system are stored in the blockchain; The obtaining of current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system includes: The current actual operation data, the historical actual operation data, and the historical fault data are obtained from the blockchain.

7. A fault prediction device for power grid equipment, characterized in that: The device comprises: An acquisition module is used to acquire current actual operation data, historical actual operation data and historical fault data of all power grid equipment in the power system; A simulation module, configured to simulate the current operating state of each of the power grid devices according to a target simulation model based on the current actual operating data and the historical actual operating data, and obtain simulated operating state data of all the power grid devices during the simulation process; The prediction module is used to perform fault prediction on all the power grid devices based on the simulated operation status data and the historical fault data according to a target prediction model to obtain a prediction result.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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