Power System Asset Risk Prediction Method and System Based on Big Data Analysis

Abstract

The present invention provides a power system asset risk prediction method and system based on big data analysis. The method includes: performing a correlation analysis on various data in the power dataset to determine the potential correlation relationship between the equipment operation status and environmental meteorological data; analyzing the degree of risk impact of environmental meteorological factors on power system assets according to the potential correlation relationship to determine the key risk factors; constructing risk scenarios based on the key risk factors; simulating the operation of power system assets under each risk scenario, and determining the risk level of each risk scenario based on the probability of power system asset failures occurring under each risk scenario and the degree of interference impact of power system asset failures on the power grid system; matching the real-time monitored environmental meteorological data of power system assets with the risk scenarios to determine the risk level of power system asset failures. The present invention improves the comprehensiveness and accuracy of asset risk prediction.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular, to a method and system for predicting power system asset risks based on big data analysis. Background Art

[0002] In the operation and maintenance of power systems, accurately predicting asset risks is crucial for ensuring the safe and stable operation of the power grid. Currently, common methods for predicting power system asset risks are based on the threshold values of equipment operation parameters for risk assessment. Mainly, fixed threshold values are set for the key operation parameters of equipment, and when the equipment operation parameters exceed these threshold values, it is determined that the equipment has risks.

[0003] Existing methods can, to a certain extent, conduct a preliminary assessment of the risks of power system assets and help operation and maintenance personnel promptly discover some obvious equipment anomalies. However, they ignore the impact of equipment operation environment factors and power grid historical fault records on asset risks. The operating state of power equipment not only depends on its own operation parameters but is also closely related to the surrounding environmental factors (such as humidity, air pressure, pollution degree, etc.) and the fault conditions that have occurred in the power grid in the past. For example, in an environment with high humidity, the insulation performance of electrical equipment may decline, and even if its operation parameters do not exceed the threshold values, there may still be potential fault risks. At the same time, a large amount of useful information is contained in the power grid historical fault records, such as the occurrence rules of certain faults and the weak links of related equipment. Existing methods fail to fully utilize this information, resulting in an incomplete and inaccurate prediction of asset risks and preventing power operation and maintenance personnel from clearly grasping the asset risk situation. Summary of the Invention

[0004] The present invention provides a method and system for predicting power system asset risks based on big data analysis to improve the comprehensiveness and accuracy of asset risk prediction, enabling power operation and maintenance personnel to more clearly grasp the power system asset risk situation and achieve more effective operation and maintenance and management of the power system.

[0005] In a first aspect, the method for predicting power system asset risks based on big data analysis according to the present invention includes:

[0006] Conduct a correlation analysis on each item of data in the power dataset to determine the potential correlation relationship between the equipment operating state and environmental meteorological data;

[0007] Analyze the degree of risk impact of environmental meteorological factors on power system assets according to the potential correlation relationship to determine key risk factors;

[0008] Construct risk scenarios based on the key risk factors; each risk scenario represents a combination of key risk factors at different levels;

[0009] Simulate the operation of power system assets under each risk scenario, and determine the risk level of each risk scenario based on the probability of power system asset failures and the degree of interference and impact of power system asset failures on the power grid system under each risk scenario;

[0010] Match the real-time monitored environmental meteorological data of power system assets with the risk scenarios, and determine the risk level of power system asset failures as the risk level of the matched target risk scenario.

[0011] In a second aspect, the present invention also provides a power system asset risk prediction system based on big data analysis, which is applied to the power system asset risk prediction method based on big data analysis as described in the first aspect; the power system asset risk prediction system based on big data analysis includes:

[0012] A correlation analysis module for performing correlation analysis on various data in the power dataset to determine the potential correlation relationship between the equipment operation status and the environmental meteorological data;

[0013] A risk factor screening module for analyzing the risk impact degree of environmental meteorological factors on power system assets according to the potential correlation relationship to determine the key risk factors;

[0014] A risk scenario construction module for constructing risk scenarios based on the key risk factors; each risk scenario represents a combination of key risk factors at different levels;

[0015] A risk scenario simulation module for simulating the operation of power system assets under each risk scenario, and determining the risk level of each risk scenario based on the probability of power system asset failures and the degree of interference and impact of power system asset failures on the power grid system under each risk scenario;

[0016] A risk prediction module for matching the real-time monitored environmental meteorological data of power system assets with the risk scenarios, and determining the risk level of the matched target risk scenario as the risk level of power system asset failures.

[0017] In a third aspect, the present invention also provides an electronic device, including: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby implementing the power system asset risk prediction method based on big data analysis as described in any one of the above.

[0018] In a fourth aspect, the present invention also provides a non-transitory computer-readable storage medium, in which a computer software program is stored, and when the computer software program is executed by a processor, it implements the power system asset risk prediction method based on big data analysis as described in any one of the above.

[0019] Fifth aspect, the present invention also provides a computer program product, including a computer program which, when executed by a processor, implements the power system asset risk prediction method based on big data analysis as described in any one of the above.

[0020] The power system asset risk prediction method based on big data analysis provided by the embodiments of the present invention analyzes the potential correlation relationship between the equipment operation state, environmental factors, and meteorological factors through correlation analysis, enabling the key risk factors with greater risk impact on power system assets to be screened out by making full use of the potential correlation relationship, and avoiding the interference of irrelevant factors. Further, the operation of power system assets is simulated under each risk scenario, and the risk level of each risk scenario is accurately obtained. During the subsequent risk prediction process of power system assets, the risk level of power system assets failing can be accurately predicted according to the matching result between the environmental meteorological data of power system assets and the risk scenario. Therefore, the embodiments of the present invention can comprehensively consider various risk factors affecting power system assets for risk prediction, improve the comprehensiveness and accuracy of asset risk prediction, enable power operation and maintenance personnel to more clearly grasp the risk situation of power system assets, and achieve more effective operation and maintenance and management of power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic flowchart of the power system asset risk prediction method based on big data analysis provided by the embodiments of the present invention;

[0022] Figure 2 is a schematic structural diagram of the power system asset risk prediction system based on big data analysis provided by the embodiments of the present invention;

[0023] Figure 3 is an embodiment diagram of the electronic device provided by the embodiments of the present invention;

[0024] Figure 4 is an embodiment diagram of the computer-readable storage medium provided by the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention. In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, the meaning of "a plurality" is two or more, unless otherwise specifically defined.

[0026] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or having more advantages than other embodiments. The following description is provided to enable any person skilled in the art to make and use the present invention. In the following description, details are set forth for the purpose of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be practiced without these specific details. In other instances, well-known structures and processes are not described in detail to avoid unnecessary details from obscuring the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0027] Optionally, referring to Figure 1 , Figure 1 is a schematic flowchart of the power system asset risk prediction method based on big data analysis provided by the present invention. In the embodiments of the present invention, the execution subject of the power system asset risk prediction method based on big data analysis is a risk prediction system. Therefore, the power system asset risk prediction method based on big data analysis includes:

[0028] Step 10: Perform a correlation analysis on each item of data in the power dataset to determine the potential correlation relationship between the equipment operation status and the environmental meteorological data.

[0029] Optionally, the power system assets in the embodiments of the present invention are various power resources that constitute the power system in the power grid system and provide support for its operation, power supply, etc. Therefore, the power system assets can include power generation assets, transmission assets, distribution assets, etc. Power generation assets such as power generation equipment, transmission assets such as transmission substations, and distribution assets such as distribution equipment.

[0030] Optionally, the power data set in the embodiments of the present invention includes device operation status information, grid historical fault data, environmental factor data, and meteorological factor data. The device operation status information includes operation parameters, start-stop status, etc. of power system assets; the grid historical fault data includes the time, location, fault type, etc. of the fault occurrence; the environmental factor data includes the geographical environment, pollution degree, etc. of the area where the power system assets are located; the meteorological factor data includes temperature, humidity, wind speed, etc.

[0031] Therefore, the risk prediction system obtains the power data set, conducts a correlation analysis on the data in the power data set, and determines the potential correlation relationship between the device operation status of the power system assets, environmental factors, and meteorological factors, as specifically described in steps 101 to 104. In one embodiment, under the conditions that the humidity exceeds 80%, the temperature exceeds 30°C, and the wind speed is greater than 15 m / s, the probability of a certain type of transformer failing significantly increases, obtaining the potential correlation relationship between the transformer and humidity and temperature.

[0032] Step 20: Analyze the risk impact degree of environmental and meteorological factors on the power system assets according to the potential correlation relationship, and determine the key risk factors.

[0033] Furthermore, the risk prediction system analyzes the target environmental factors and target meteorological factors that affect the device operation status of the power system assets according to the potential correlation relationship, and combines the target environmental factors and target meteorological factors into different factor combinations. Continuing with the above embodiment, the potential correlation relationship is that under the conditions that the humidity exceeds 80%, the temperature exceeds 30°C, and the wind speed is greater than 15 m / s, the probability of the transformer failing significantly increases. Therefore, the target environmental factors are temperature and humidity, the target meteorological factor is wind speed, and the factor combinations can be {(temperature); (humidity); (wind speed); (humidity, temperature); (humidity, wind speed); (temperature, wind speed); (humidity, temperature, wind speed)}.

[0034] Furthermore, the risk prediction system analyzes the risk impact degree of the power system assets under different factor combinations, that is, the risk impact degree of different factor combinations on the device operation status of the power system assets, and determines the factors in the factor combination with a greater risk impact degree as the key risk factors, as specifically described in steps 201 to 205.

[0035] Step 30: Construct a risk scenario based on the key risk factors.

[0036] Further, the risk prediction system classifies key risk factors into different levels according to the stored historical experience, and combines the key risk factors at different levels to obtain risk scenarios. In one embodiment, temperature is divided into high temperature and low temperature, humidity is divided into high humidity and low humidity, wind speed is divided into high wind speed and low wind speed, and risk scenarios include high temperature high humidity high wind speed, high temperature low humidity high wind speed, low temperature high humidity low wind speed scenarios, etc.

[0037] Step 40, simulate the operation of power system assets under each risk scenario, and determine the risk level of each risk scenario based on the probability of power system asset failures occurring under each risk scenario and the degree of interference and impact of power system asset failures on the power grid system.

[0038] Further, when simulating the operation of power system assets under each risk scenario, the risk prediction system analyzes the probability of power system asset failures occurring under each risk scenario based on the historical power grid failure data of the power system assets, where the historical power grid failure data is summarized according to historical experience.

[0039] Further, the risk prediction system determines the power outage range and duration of the power grid system when power system assets fail, and determines the degree of interference and impact of power system asset failures on the power grid system according to preset rules in combination with the power outage range and duration of the power grid system. The preset rules are such that the larger the power outage range, the greater the degree of interference and impact, and the longer the duration, the greater the degree of interference and impact. Specifically, it can be calculated through a weighted algorithm and will not be elaborated here.

[0040] Further, the risk prediction system determines the risk level of each risk scenario based on the probability of power system asset failures occurring under each risk scenario and the degree of interference and impact of power system asset failures on the power grid system, as specifically described in steps 401 to 404.

[0041] Step 50, match the environmental meteorological data of the power system assets monitored in real time with the risk scenarios, and determine the risk level of the target risk scenario that is matched as the risk level of power system asset failures.

[0042] Further, the risk prediction system monitors the environmental meteorological data of the power system assets in real time. The environmental meteorological data is the environmental factor data and meteorological factor data, and matches the real-time monitored environmental factor data and meteorological factor data of the power system assets with the risk scenarios to obtain the target risk scenario in the risk scenarios that matches the environmental factor data and meteorological factor data.

[0043] Further, the risk prediction system determines the risk level of the target risk scenario that is matched as the risk level of power system asset failures.

[0044] In an embodiment of the present invention, potential correlation relationships between the operating states of devices, environmental factors, and meteorological factors are analyzed through correlation analysis, enabling the full utilization of the potential correlation relationships to screen out key risk factors that have a greater impact on the risks of power system assets and avoiding interference from irrelevant factors. Further, the operation of power system assets is simulated under each risk scenario, and the risk level of each risk scenario is accurately obtained. During the subsequent risk prediction process of power system assets, the risk level of power system asset failures can be accurately predicted based on the matching results between the environmental meteorological data of power system assets and risk scenarios. Therefore, the embodiments of the present invention can comprehensively consider various risk factors affecting power system assets for risk prediction, improving the comprehensiveness and accuracy of asset risk prediction, enabling power operation and maintenance personnel to more clearly grasp the risk situation of power system assets, and realizing more effective operation and maintenance and management of power systems.

[0045] In one embodiment, the descriptions of steps 101 to 104 are as follows:

[0046] Step 101: Perform multi-dimensional space mapping on the device operating state information, environmental factor data, and meteorological factor data in the power dataset to construct an initial three-dimensional correlation matrix.

[0047] Optionally, the risk prediction system performs multi-dimensional space mapping on the device operating state information, environmental factor data, and meteorological factor data in the power dataset. The multi-dimensional space mapping in the embodiments of the present invention is three-dimensional space mapping, so an initial three-dimensional correlation matrix is constructed. Among them, each matrix element in the initial three-dimensional correlation matrix represents the initial correlation strength of the device operating state information, environmental factor data, and meteorological factor data. The initial correlation strength represents the ratio between the number of data instances that simultaneously include the device operating state information, environmental factor data, and meteorological factor data and the total number of data instances in the power dataset.

[0048] Therefore, for the device operating state information , the environmental factor data , and the meteorological factor data , each matrix element of them in the initial three-dimensional correlation matrix can be expressed as:

[0049] . Among them, represents the power dataset, represents the th data instance in the power dataset, represents the th data instance in the power dataset, represents the th data instance in the power dataset, Indicates that it simultaneously includes device operation status information , environmental factor data and meteorological factor data The number of data instances, Indicates the total number of data instances in the power data set.

[0050] Step 102: Based on a preset threshold, screen each matrix element in the initial three-dimensional correlation matrix to obtain a screened three-dimensional correlation matrix.

[0051] Furthermore, in the embodiment of the present invention, a preset threshold is set in advance , and each matrix element in the initial three-dimensional correlation matrix is screened through the preset threshold . If , the risk prediction system will ignore the correlation relationship corresponding to this matrix element to obtain a screened three-dimensional correlation matrix. Therefore, each matrix element in the screened three-dimensional correlation matrix can be expressed as:

[0052]

[0053] .

[0054] Step 103: Update each matrix element in the screened three-dimensional correlation matrix based on a preset time window to obtain an updated three-dimensional correlation matrix.

[0055] Furthermore, since power data has time series characteristics, therefore, analyze the data correlation of device operation status information, environmental factor data, and meteorological factor data within the time window. Therefore, the risk prediction system calculates the updated matrix element according to the ratio between the number of data instances that simultaneously include device operation status information, environmental factor data, and meteorological factor data within the preset time window and the total number of data instances within the preset time window , and the specific formula is as follows:

[0056] .

[0057] Among them, Indicates the preset time window within which it simultaneously includes device operation status information , environmental factor data and meteorological factor data The number of data instances, Indicates the total number of data instances within the preset time window .

[0058] Furthermore, the risk prediction system is based on the updated matrix element Update each matrix element in the filtered three-dimensional correlation matrix to obtain the updated three-dimensional correlation matrix.

[0059] Step 104: Based on the filtered three-dimensional correlation matrix and the updated three-dimensional correlation matrix, determine the potential correlation relationships between the device operating state, environmental factors, and meteorological factors.

[0060] Furthermore, the risk prediction system determines the potential correlation relationships between the device operating state, environmental factors, and meteorological factors according to each matrix element in the filtered three-dimensional correlation matrix and each matrix element in the updated three-dimensional correlation matrix, as specifically described in Steps 1041 to 1044.

[0061] In the embodiments of the present invention, the potential correlation relationships between the device operating state, environmental factors, and meteorological factors are analyzed through correlation analysis, enabling the key risk factors with greater risk impacts on the power system assets to be screened out by making full use of the potential correlation relationships, avoiding the interference of irrelevant factors. Therefore, subsequent risk scenarios can be accurately constructed based on the key risk factors to predict the risk factors affecting the power system assets, improving the comprehensiveness and accuracy of the asset risk prediction.

[0062] In one embodiment, the descriptions of Steps 1041 to 1044 are as follows:

[0063] Step 1041: Screen in the power dataset based on each matrix element in the filtered three-dimensional correlation matrix to obtain a preliminary correlation candidate set.

[0064] Optionally, the risk prediction system screens in the power dataset according to each matrix element in the filtered three-dimensional correlation matrix. Among them, if the matrix element in the filtered three-dimensional correlation matrix is not 0, it indicates that there is a correlation relationship between the data; if the matrix element in the filtered three-dimensional correlation matrix is 0, it indicates that there is no correlation relationship between the data or there is a negligible correlation relationship. Therefore, the data with corresponding matrix elements in the filtered three-dimensional correlation matrix are screened out in the power dataset to obtain a preliminary correlation candidate set.

[0065] Step 1042: For each item set in the preliminary correlation candidate set, obtain the frequency score of each item set based on the occurrence times of the item set, combined with the first matrix element of the item set in the filtered three-dimensional correlation matrix and the second matrix element of the item set in the updated three-dimensional correlation matrix.

[0066] Furthermore, for each item set in the preliminary correlation candidate set , the risk prediction system obtains the occurrence times of each item set The occurrence times of and the size of the dataset in the preliminary association candidate set are used to calculate the occurrence frequency of each item set.

[0067] Furthermore, the risk prediction system calculates based on the occurrence frequency of each item set combined with the first matrix element of each item set in the filtered three-dimensional association matrix and the second matrix element of each item set in the updated three-dimensional association matrix to obtain the frequency score of each item set. The specific formula is as follows:

[0068] .

[0069] Among them, represents the occurrence times of the item set in the preliminary association candidate set, represents the size of the dataset in the preliminary association candidate set, represents the first matrix element of the item set in the filtered three-dimensional association matrix, represents the item set size, represents the second matrix element of the item set in the updated three-dimensional association matrix.

[0070] Step 1043: Determine the item sets with frequency scores greater than or equal to the preset minimum support threshold as frequent item sets.

[0071] Furthermore, a preset minimum support threshold is preset. The risk prediction system compares the frequency score of each item set with the preset minimum support threshold and determines the item sets with frequency scores greater than or equal to the preset minimum support threshold as frequent item sets.

[0072] Step 1044: For any two target frequent item sets in the frequent item sets, generate association rules according to the item set causal relationship and item set influence degree between the target frequent item sets, and determine the association rules as the potential association relationship between the device operating state and environmental factors and meteorological factors.

[0073] Furthermore, in the embodiments of the present invention, association rules are generated by analyzing the occurrence order and mutual influence degree of any two target frequent item sets in the frequent item sets in the dataset. Therefore, for any two target frequent item sets in the frequent item sets, the risk prediction system obtains the item set causal relationship and item set influence degree between the target frequent item sets, where the item set causal relationship represents the conditional probability of the target frequent item set.

[0074] Furthermore, the risk prediction system generates association rules according to the item set causal relationship and item set influence degree between target frequent item sets, specifically as follows:

[0075] 。

[0076] 。

[0077] Among them, represents the association rule, represents the strength of the association rule, represents the target frequent item set and the target frequent item set of the item set causal relationship, represents the target frequent item set on the target frequent item set of the item set influence degree; is the time interval, indicating the time delay of observing the appearance of the target frequent item set after the appearance of the target frequent item set ; represents that after each appearance of the target frequent item set , after , the sum of the conditional probabilities of the appearance of the target frequent item set ;

[0078] Furthermore, the risk prediction system determines the association rule as the potential association relationship between the device operation state, environmental factors, and meteorological factors.

[0079] In the embodiment of the present invention, the potential association relationship is accurately obtained through the item set causal relationship and item set influence degree between frequent item sets, so that the key risk factors that have a greater risk impact on the power system assets can be screened out by making full use of the potential association relationship, avoiding the interference of irrelevant factors. Therefore, subsequent risk scenarios can be accurately constructed according to the key risk factors to predict the risk factors affecting the power system assets, improving the comprehensiveness and accuracy of the asset risk prediction.

[0080] In one embodiment, the descriptions of steps 201 to 205 are as follows:

[0081] Step 201, determine the target environmental factors and target meteorological factors that affect the device operation state of the power system assets based on the potential association relationship.

[0082] Optionally, the risk prediction system analyzes the target environmental factors and target meteorological factors that affect the operating status of the power system assets based on the potential correlation relationship. In one embodiment, the potential correlation relationship is that the probability of the transformer malfunctioning increases significantly when the humidity exceeds 80%, the temperature exceeds 30 °C, and the wind speed is greater than 15 m / s. Therefore, the target environmental factors are temperature and humidity, and the target meteorological factor is wind speed.

[0083] Step 202, for each factor combination of the target environmental factors or / and target meteorological factors, when each factor combination occurs, if the power system assets are disturbed, then determine the disturbance response intensity when the power system assets are disturbed according to the change amplitude of the operating status of the power system assets under the action of the factor combination.

[0084] Further, the risk prediction system performs permutations and combinations on the target environmental factors and target meteorological factors to obtain the corresponding factor combinations. Continuing the above embodiment, the factor combinations can be {(temperature); (humidity); (wind speed); (humidity, temperature); (humidity, wind speed); (temperature, wind speed); (humidity, temperature, wind speed)}.

[0085] Further, for each factor combination, when each factor combination occurs, the risk prediction system determines whether the power system assets are disturbed, that is, whether the operating status of the power system assets has changed.

[0086] Further, if it is determined that the power system assets are not disturbed, it means that when this factor combination occurs, there is no risk to the power system assets, and this factor combination is excluded from all factor combinations.

[0087] Further, if it is determined that the power system assets are disturbed, it means that when this factor combination occurs, there is a risk to the power system assets. Therefore, the risk prediction system obtains the change amplitude of the operating status of the power system assets under the action of the factor combination.

[0088] Further, the risk prediction system analyzes the change rate of the operating status variables of the power system assets according to the change amplitude of the operating status of the power system assets under the action of the factor combination, and analyzes the influence intensity of the disturbance on the power system assets according to the change rate and the change amplitude, and obtains the disturbance response intensity when the power system assets are disturbed. The specific formula is as follows: .

[0089] Wherein, represents the disturbance response intensity when the power system assets are disturbed, represents the change amplitude of the operating status of the power system assets under the action of the factor combination.

[0090] Step 203: Based on the network topology of the power grid system, determine the number of nodes, the number of edges, the coupling relationship between nodes, and the coupling relationship between edges in the path through which the disturbance propagates, and determine the complexity of the propagation path when a disturbance occurs in the power system assets.

[0091] Furthermore, the risk prediction system determines the number of nodes, the number of edges, the coupling relationship between nodes, and the coupling relationship between edges in the path through which the disturbance propagates when a disturbance occurs in the power system assets according to the network topology of the power grid system. Among them, the network topology is pre-constructed based on experience, and the network topology includes the coupling relationship between nodes and the coupling relationship between edges, and the coupling relationship represents the interaction strength.

[0092] Furthermore, the risk prediction system evaluates the complexity of the propagation path according to the number of nodes, the number of edges, the coupling relationship between nodes, and the coupling relationship between edges in the path through which the disturbance propagates, and obtains the complexity of the propagation path when a disturbance occurs in the power system assets. The specific formula is as follows:

[0093] 。

[0094] Among them, represents the complexity of the propagation path when a disturbance occurs in the power system assets, represents the number of nodes in the path, represents the number of edges in the path, represents node and node The coupling relationship between them, represents node and node The coupling relationship of the edges; represents the direction weight, which characterizes the influence of the disturbance propagation direction.

[0095] Step 204: Based on the change gradient of the operating state of the power system assets in each local area of the power grid system, determine the disturbance fluctuation degree when a disturbance occurs in the power system assets.

[0096] Furthermore, the risk prediction system obtains the change gradient of the operating state of the power system assets in each local area of the power grid system, and makes local change differences according to the change gradient of the operating state of the power system assets in each local area, and obtains the disturbance fluctuation degree when a disturbance occurs in the power system assets. The specific formula is:

[0097] 。

[0098] Among them, represents the disturbance fluctuation degree when a disturbance occurs in the power system assets, represents the number of local areas, represents the local area The change gradient of the operating state of the internal power system assets, represents the number of sub-regions within a local area, represents the difference coefficient between a sub-region and the average state of the local area, represents the minimum value function.

[0099] Step 205: Based on the disturbance response intensity, propagation path complexity, and disturbance fluctuation degree when the power system assets are disturbed under each factor combination, determine the key risk factors.

[0100] Furthermore, the risk prediction system determines the key risk factors according to the disturbance response intensity, propagation path complexity, and disturbance fluctuation degree when the power system assets are disturbed under each factor combination, as specifically described in Steps 2051 to 2055.

[0101] In the embodiment of the present invention, the key risk factors are accurately determined according to the disturbance response intensity, propagation path complexity, and disturbance fluctuation degree when the power system assets are disturbed under each factor combination, avoiding the interference of irrelevant factors. Therefore, subsequent risk scenarios can be accurately constructed based on the key risk factors to predict the risk factors affecting the power system assets, improving the comprehensiveness and accuracy of the asset risk prediction.

[0102] In an embodiment, the descriptions of Steps 2051 to 2055 are as follows:

[0103] Step 2051: For each factor combination, simulate the recovery of the power system assets from the disturbance, and obtain the lag recovery time for the power system assets to recover to the original operating state.

[0104] Optionally, for each factor combination, the risk prediction system simulates the recovery of the power system assets from the disturbance. When the power system assets recover to a quarter of the original operating state, that is, when the performance of the power system assets reaches 25% of the original normal operation as a whole, obtain the first lag time. Continue to detect. When the power system assets recover to half of the original operating state, that is, when the performance of the power system assets reaches 50% of the original normal operation as a whole, obtain the second lag time. Continue to detect. When the power system assets recover to three-quarters of the original operating state, that is, when the performance of the power system assets reaches 75% of the original normal operation as a whole, obtain the third lag time. Continue to detect. When the power system assets recover to the original operating state, obtain the fourth lag time.

[0105] Further, the risk prediction system calculates the mean value based on the first lag time, the second lag time, the third lag time, and the fourth lag time to obtain the lag recovery time for the power system assets to return to the original operating state.

[0106] Step 2052: Determine the risk vulnerability degree of the power system assets when each factor combination occurs based on the disturbance response intensity, propagation path complexity, and disturbance fluctuation degree of the power system assets.

[0107] Further, the risk prediction system determines the risk vulnerability degree of the power system assets when each factor combination occurs according to the disturbance response intensity, propagation path complexity, and disturbance fluctuation degree of the power system assets , where the risk vulnerability degree has the following specific formula:

[0108] .

[0109] Step 2053: Determine the risk sensitivity degree of the power system assets when each factor combination occurs based on the risk vulnerability degree and lag recovery time of the power system assets when each factor combination occurs.

[0110] Further, the risk prediction system determines the risk sensitivity degree of the power system assets when each factor combination occurs according to the risk vulnerability degree and lag recovery time of the power system assets when each factor combination occurs , where the risk sensitivity degree has the following specific formula:

[0111] .

[0112] Among them, represents the partial derivative of the risk vulnerability degree with respect to the factor combination , and the sensitivity degree of the risk vulnerability degree changing with the factor combination is obtained by taking the partial derivative; represents the lag recovery time.

[0113] Step 2054: Determine the comprehensive risk index of each factor combination based on the risk vulnerability degree and risk sensitivity degree of each factor combination.

[0114] Further, the risk prediction system determines the comprehensive risk index of each factor combination according to the risk vulnerability degree and risk sensitivity degree of each factor combination , and the specific formula is as follows:

[0115] .

[0116] Step 2055: Determine the factor combinations with the comprehensive risk index greater than or equal to the preset index threshold as risk factor combinations, and determine the factors in the risk factor combinations as key risk factors.

[0117] Further, the risk prediction system determines the factor combinations with the comprehensive risk index greater than or equal to the preset index threshold as risk factor combinations, and determines the factors in the risk factor combinations as key risk factors, where the preset index threshold is set according to the actual situation.

[0118] The embodiment of the present invention accurately determines the key risk factors, avoiding the interference of irrelevant factors. Therefore, subsequently, risk scenarios can be accurately constructed based on the key risk factors to predict the risk factors affecting the power system assets, improving the comprehensiveness and accuracy of the asset risk prediction.

[0119] In one embodiment, the descriptions of steps 401 to 404 are as follows:

[0120] Step 401: For each risk scenario, determine the repair and adjustment ability of the power grid system based on the historical fault data of the power grid of the power system assets.

[0121] Optionally, the risk prediction system collects the historical fault data of the power grid of the power system assets. The historical fault data of the power grid includes the fault type, fault repair time, time and location of the fault occurrence, types of power system assets involved (such as transformers, lines, switches, etc.), fault causes, and the impacts on the power grid operation caused by the fault (such as power outage range, voltage fluctuation, etc.).

[0122] Further, for each risk scenario, the risk prediction system analyzes the historical fault data of the power grid to determine the repair and adjustment ability of the power grid system when facing faults in each risk scenario.

[0123] In one embodiment, the repair and adjustment ability can be the average time from the occurrence of the fault to the repair, or can be an ability index for the power grid system to maintain a certain power supply level during the repair process. Therefore, the risk prediction system analyzes the historical fault data of the power grid, and statistically calculates the average time from the occurrence of different types of faults to the repair, or the ability index for the system to maintain a certain power supply level during the repair process.

[0124] Further, for each risk scenario, the risk prediction system analyzes the historical fault data of the power grid to obtain the number of times the power system assets have failed in the historical data under each risk scenario, and the total operating time of the power system assets under each risk scenario.

[0125] Further, the risk prediction system calculates the probability of a power system asset failure under each risk scenario based on the number of failures of the power system assets and the total operating time under each risk scenario, that is, the probability of failure = the number of failures / the total operating time.

[0126] Further, for each risk scenario, the risk prediction system analyzes the historical fault data of the power grid to obtain the interference indicators of the power grid system, such as the power loss due to power outage, the number of power outage users, the voltage over-limit amplitude, and the power flow overload degree, when the power system assets fail. According to each interference indicator and its corresponding influence weight, the interference influence degree of the power system asset failure on the power grid system is calculated, which will not be elaborated here.

[0127] Step 402: Based on the repair and adjustment ability, correct the interference influence degree of the power system asset failure on the power grid system to obtain the corrected interference degree.

[0128] Further, the repair and adjustment ability in the embodiment of the present invention is the average time from the occurrence of a fault to its repair. Therefore, the risk prediction system corrects the interference influence degree of the power system asset failure on the power grid system through the average time from the occurrence of a fault to its repair to obtain the corrected interference degree. The specific formula is as follows:

[0129] 。

[0130] Among them, represents the corrected interference degree, represents the interference influence degree, represents the average time from the occurrence of a fault to its repair, and the unit is milliseconds; represents the hyperbolic tangent function. When is close to 0.1, the function value is close to 0, and the attenuation effect on is small; when deviates greatly from 0.1, the function value approaches , and the attenuation effect on is enhanced, realizing reasonable correction of different interference influence degrees.

[0131] Step 403: Based on the corrected interference degree and the probability of the power system asset failure, determine the initial risk intensity under each risk scenario.

[0132] Further, the risk prediction system determines the initial risk intensity under each risk scenario according to the corrected interference degree and the probability of the power system asset failure. The specific formula is as follows:

[0133] 。

[0134] Among them, represents the initial risk intensity, Indicates the probability of a failure of the power system assets.

[0135] Step 404: Based on the change trend indicators of each risk scenario over time and combined with the initial risk intensity, evaluate each risk scenario to obtain the risk level of each risk scenario.

[0136] Furthermore, for each risk scenario, the risk prediction system monitors the changes in the operating parameters of the power grid system over time, and determines the change trend indicators of each risk scenario over time according to the changes in the operating parameters of the power grid system over time. In one embodiment, for each risk scenario over time The change trend indicator is with a value range of .

[0137] Furthermore, the risk prediction system evaluates each risk scenario based on the change trend indicators of each risk scenario over time and combined with the initial risk intensity to obtain the risk level of each risk scenario, specifically as in steps 4041 to 4043.

[0138] In the embodiment of the present invention, the operation of the power system assets in each risk scenario is simulated, and the risk level of each risk scenario is accurately obtained. Therefore, according to the matching result of the environmental meteorological data of the power system assets and the risk scenario, the risk level of the failure of the power system assets can be accurately predicted, improving the comprehensiveness and accuracy of the prediction of asset risks, enabling power operation and maintenance personnel to more clearly grasp the risk situation of power system assets, and realizing more effective operation and maintenance and management of the power system.

[0139] In one embodiment, the descriptions of steps 4041 to 4043 are as follows:

[0140] Step 4041: Based on the change trend indicators of each risk scenario over time, determine the risk adjustment factor.

[0141] Optionally, considering that the change of the risk scenario over time has a certain impact on the risk intensity, therefore, the risk prediction system determines the risk adjustment factor according to the change trend indicators of each risk scenario over time, where the specific formula of the risk adjustment factor is:

[0142] .

[0143] Where represents the risk adjustment factor, represents adjusting the value of the cosine function according to the change trend indicator. When , the value is -1, indicating that the risk scenario deteriorates. When = -1, the value is 1, indicating that the risk scenario improves; Over time Gradually adjust the dynamic adjustment range of the risk intensity to reflect the cumulative impact of time on the change of the risk scenario.

[0144] Step 4042: Based on the risk intensity adjustment factor and the initial risk intensity of each risk scenario, perform a comprehensive calculation of the risk intensity to obtain the target risk intensity of each risk scenario.

[0145] Furthermore, the risk prediction system performs a comprehensive calculation of the risk intensity based on the risk intensity adjustment factor and the initial risk intensity of each risk scenario to obtain the target risk intensity of each risk scenario where the target risk intensity The specific formula is:

[0146] .

[0147] Step 4043: Based on the target risk intensity of each risk scenario, perform a level mapping to obtain the risk level of each risk scenario.

[0148] Optionally, a series of risk level interval boundary values are preset in the embodiments of the present invention. The risk level interval boundary values are such as , such as etc. The specific risk level interval boundary values are determined according to the actual situation and experience of the system.

[0149] Therefore, the risk prediction system performs a level mapping on the target risk intensity of each risk scenario according to the risk level interval boundary values to obtain the risk level of each risk scenario , and the specific formula is:

[0150] . .

[0151] denotes rounding down, denotes the parameter variable of, denotes the number of risk level interval boundary values, denotes the first risk level interval boundary value, denotes the th risk level interval boundary value.

[0152] The embodiments of the present invention accurately obtain the risk level of each risk scenario. Therefore, according to the matching result of the environmental meteorological data of the power system assets and the risk scenario, the risk level of the power system assets failing can be accurately predicted, improving the comprehensiveness and accuracy of the asset risk prediction, enabling the power operation and maintenance personnel to more clearly master the risk situation of the power system assets, and realizing more effective operation and management.

[0153] Furthermore, the power system asset risk prediction system based on big data analysis provided by the present invention will be described below. The power system asset risk prediction system based on big data analysis described below can be referred to in correspondence with the power system asset risk prediction method based on big data analysis described above.

[0154] Referring to Figure 2 , Figure 2 is a schematic structural diagram of the power system asset risk prediction system based on big data analysis provided by the present invention. The power system asset risk prediction system based on big data analysis includes.

[0155] The correlation analysis module 210 is configured to perform correlation analysis on various data in the power dataset to determine the potential correlation relationship between the equipment operation status and the environmental meteorological data;

[0156] The risk factor screening module 220 is configured to analyze the risk impact degree of environmental meteorological factors on the power system assets according to the potential correlation relationship to determine the key risk factors;

[0157] The risk scenario construction module 230 is configured to construct risk scenarios based on the key risk factors; each risk scenario represents a combination of key risk factors at different levels;

[0158] The risk scenario simulation module 240 is configured to simulate the operation of the power system assets under each risk scenario, and determine the risk level of each risk scenario based on the probability of the power system assets failing under each risk scenario and the interference impact degree of the power system asset failure on the power grid system;

[0159] The risk prediction module 250 is configured to match the environmental meteorological data of the power system assets monitored in real time with the risk scenarios, and determine the risk level of the power system assets failing as the risk level of the target risk scenario that is matched.

[0160] In the embodiments of the present invention, potential correlation relationships between the operating state of equipment, environmental factors, and meteorological factors are analyzed through correlation analysis, enabling the full utilization of the potential correlation relationships to screen out key risk factors that have a greater risk impact on power system assets and avoiding interference from irrelevant factors. Further, the operation of power system assets is simulated under each risk scenario, and the risk level of each risk scenario is accurately obtained. In the subsequent risk prediction process of power system assets, the risk level of the occurrence of faults in power system assets can be accurately predicted based on the matching result between the environmental meteorological data of power system assets and the risk scenarios. Therefore, the embodiments of the present invention can comprehensively consider various risk factors affecting power system assets for risk prediction, improving the comprehensiveness and accuracy of asset risk prediction, enabling power operation and maintenance personnel to more clearly grasp the risk situation of power system assets, and realizing more effective operation and management of the power system.

[0161] Please refer to Figure 3 , Figure 3 which is the embodiment diagram of the electronic device provided by the embodiments of the present invention. As Figure 3 shown, the embodiments of the present invention provide an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored on the memory 310 and operable on the processor 320. When the processor 320 executes the computer program 311, the following steps are implemented:

[0162] Perform correlation analysis on the data in the power dataset to determine the potential correlation relationship between the operating state of the equipment and the environmental meteorological data;

[0163] Analyze the degree of risk impact of environmental meteorological factors on power system assets according to the potential correlation relationship to determine the key risk factors;

[0164] Construct risk scenarios based on the key risk factors; each risk scenario represents a combination of key risk factors at different levels;

[0165] Simulate the operation of power system assets under each risk scenario, and determine the risk level of each risk scenario based on the probability of the occurrence of faults in power system assets under each risk scenario and the degree of interference and impact of power system asset faults on the power grid system;

[0166] Match the real-time monitored environmental meteorological data of power system assets with the risk scenarios, and determine the risk level of the target risk scenario obtained by the matching as the risk level of the occurrence of faults in power system assets.

[0167] Please refer to Figure 4 , Figure 4 which is the embodiment diagram of the computer-readable storage medium provided by the embodiments of the present invention. As Figure 4As shown in the figure, this embodiment provides a computer-readable storage medium 400, on which a computer program 311 is stored. When the computer program 311 is executed by a processor, the following steps are implemented:

[0168] Perform a correlation analysis on the data in the power dataset to determine the potential correlation between the equipment operation status and the environmental meteorological data;

[0169] Analyze the risk impact degree of environmental meteorological factors on the power system assets according to the potential correlation, and determine the key risk factors;

[0170] Construct risk scenarios based on the key risk factors; each risk scenario represents a combination of key risk factors at different levels;

[0171] Simulate the operation of the power system assets under each risk scenario, and determine the risk level of each risk scenario based on the probability of the power system assets failing and the degree of interference impact of the power system assets' failures on the power grid system under each risk scenario;

[0172] Match the real-time monitored environmental meteorological data of the power system assets with the risk scenarios, and determine the risk level of the power system assets failing as the risk level of the target risk scenario that is matched.

[0173] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the following steps:

[0174] Perform a correlation analysis on the data in the power dataset to determine the potential correlation between the equipment operation status and the environmental meteorological data;

[0175] Analyze the risk impact degree of environmental meteorological factors on the power system assets according to the potential correlation, and determine the key risk factors;

[0176] Construct risk scenarios based on the key risk factors; each risk scenario represents a combination of key risk factors at different levels;

[0177] Simulate the operation of the power system assets under each risk scenario, and determine the risk level of each risk scenario based on the probability of the power system assets failing and the degree of interference impact of the power system assets' failures on the power grid system under each risk scenario;

[0178] Match the real-time monitored environmental meteorological data of the power system assets with the risk scenarios, and determine the risk level of the power system assets failing as the risk level of the target risk scenario that is matched.

[0179] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0180] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power system asset risk prediction method based on big data analysis, characterized in that, Including: Conduct a correlation analysis on the various data in the power dataset to determine the potential correlation relationship between the equipment operating status and environmental meteorological data; For each factor combination of the target environmental factor or / and the target meteorological factor that determines the operating state of the equipment affecting the power system assets based on the potential correlation relationship, if a disturbance occurs, the disturbance response intensity is obtained according to the change amplitude and its change rate ; Based on the number of nodes, the number of edges, the coupling relationship between nodes and nodes, and the coupling relationship between edges in the path through which the perturbation propagates, determine the propagation path complexity; and the number of edges , nodes and nodes coupling relationship and the coupling relationship between edges , determine the propagation path complexity ; Indicates the direction weight; According to the gradient of the change in the operating state within each local area obtain the degree of disturbance fluctuation ; represents the number of local areas, represents the local area the number of sub-areas within, represents the sub-area and the local area the coefficient of difference from the average state, represents the minimum value function; Simulate the recovery of disturbances occurring in power system assets, and respectively obtain the first lag time, second lag time, third lag time, and fourth lag time for the power system assets to recover to one-fourth level, one-half level, three-fourths level, and the original operating state of the original operating state; perform mean calculation based on the first lag time, the second lag time, the third lag time, and the fourth lag time to obtain the lag recovery of the power system assets to the original operating state ; Determine the risk sensitivity according to the risk vulnerability and the lagged recovery time ; Indicates the risk vulnerability For the factor combination Partial derivative; Identify the factors in the risk factor combination with a comprehensive risk indicator greater than or equal to the preset indicator threshold as key risk factors; the comprehensive risk indicator , ; Construct risk scenarios based on key risk factors; Each risk scenario represents a combination of key risk factors at different levels; Simulate the operation of power system assets under each risk scenario, and determine the risk level of each risk scenario based on the probability of power system asset failures and the degree of interference impact of power system asset failures on the power grid system under each risk scenario; Match the environmental meteorological data of the power system assets monitored in real time with the risk scenarios, and determine the risk level of power system asset failures as the risk level of the matched target risk scenario.

2. The power system asset risk prediction method based on big data analysis according to claim 1, wherein The conducting a correlation analysis on the various data in the power dataset to determine the potential correlation relationship between the equipment operating status and environmental meteorological data includes: Perform multi-dimensional space mapping on the equipment operating status information, environmental factor data, and meteorological factor data in the power dataset to construct an initial three-dimensional correlation matrix; each matrix element in the three-dimensional correlation matrix represents the ratio between the number of data instances containing equipment operating status information, environmental factor data, and meteorological factor data simultaneously and the total number of data instances in the power dataset; Based on a preset threshold, screen the initial three-dimensional correlation matrix by combining each matrix element in the initial three-dimensional correlation matrix to obtain a screened three-dimensional correlation matrix; Update each matrix element in the screened three-dimensional correlation matrix based on a preset time window to obtain an updated three-dimensional correlation matrix; each matrix element in the updated three-dimensional correlation matrix represents the ratio between the number of data instances containing equipment operating status information, environmental factor data, and meteorological factor data simultaneously within the preset time window and the total number of data instances within the preset time window; Based on the screened three-dimensional correlation matrix and the updated three-dimensional correlation matrix, determine the potential correlation relationship between the equipment operating status and environmental factors as well as meteorological factors.

3. The power system asset risk prediction method based on big data analysis according to claim 2, characterized in that The determining the potential correlation relationship between the equipment operating status and environmental factors as well as meteorological factors based on the screened three-dimensional correlation matrix and the updated three-dimensional correlation matrix includes: Screen the power dataset based on each matrix element in the screened three-dimensional correlation matrix to obtain a preliminary correlation candidate set; For each item set in the preliminary correlation candidate set, obtain the frequency score of each item set based on the occurrence times of the item set, combined with the first matrix element of the item set in the screened three-dimensional correlation matrix and the second matrix element in the updated three-dimensional correlation matrix; Determine the item sets with frequency scores greater than or equal to the preset minimum support threshold as frequent item sets; For any two target frequent item sets in the frequent item sets, generate association rules based on the item set causal relationship and item set impact degree between the target frequent item sets, and determine the association rules as the potential correlation relationship between the equipment operating status and environmental factors as well as meteorological factors.

4. The power system asset risk prediction method based on big data analysis according to claim 1, characterized in that Based on the probability of power system assets failing under each risk scenario and the degree of interference and impact of power system asset failures on the power grid system, determine the risk level of each risk scenario, including: For each risk scenario, determine the repair and adjustment ability of the power grid system based on the historical power grid failure data of power system assets; Based on the repair and adjustment ability, correct the degree of interference and impact of power system asset failures on the power grid system to obtain the corrected degree of interference; Based on the corrected degree of interference and the probability of power system assets failing, determine the initial risk intensity under each risk scenario; Based on the change trend index of each risk scenario over time and the initial risk intensity, evaluate each risk scenario to obtain the risk level of each risk scenario.

5. The power system asset risk prediction method based on big data analysis according to claim 4, wherein, The evaluation of each risk scenario based on the change trend index of each risk scenario over time and the initial risk intensity to obtain the risk level of each risk scenario includes: Based on the change trend index of each risk scenario over time, determine the risk adjustment factor; Based on the risk intensity adjustment factor and the initial risk intensity of each risk scenario, perform a comprehensive calculation of risk intensity to obtain the target risk intensity of each risk scenario; Based on the target risk intensity of each risk scenario, perform a level mapping to obtain the risk level of each risk scenario.

6. A power system asset risk prediction system based on big data analysis, characterized in that, Applied to the power system asset risk prediction method based on big data analysis as described in any one of claims 1 to 5; The power system asset risk prediction system based on big data analysis includes: A correlation analysis module for performing a correlation analysis on the data in the power dataset to determine the potential correlation relationship between the equipment operation status and the environmental meteorological data; A risk factor screening module for: For each factor combination of the target environmental factor or / and target meteorological factor that determines the operating state of the equipment affecting the power system assets based on the potential association relationship, if a disturbance occurs, the disturbance response intensity is obtained according to the change amplitude and its change rate ; Based on the number of nodes, the number of edges, the coupling relationship between nodes and nodes, and the coupling relationship between edges in the path through which the perturbation propagates, determine the propagation path complexity; and the number of edges , nodes and nodes coupling relationship and the coupling relationship between edges , determine the propagation path complexity ; Indicates the direction weight; According to the change gradient of the operating state in each local area Obtain the disturbance fluctuation degree ; Represents the number of local areas, Represents the local area The number of sub-areas within, Represents the sub-area And the local area The coefficient of difference from the average state, Represents the function of taking the minimum value; Simulate the recovery of power system assets from disturbances, and respectively obtain the first lag time, the second lag time, the third lag time, and the fourth lag time for the power system assets to recover to one-quarter level, one-half level, three-quarters level, and the original operating state of the original operating state; perform mean calculation according to the first lag time, the second lag time, the third lag time, and the fourth lag time to obtain the lag recovery of the power system assets to the original operating state ; Determine the risk sensitivity according to the risk vulnerability and the lagged recovery time ; Indicates the risk vulnerability For the factor combination Partial derivative; Identify the factors in the risk factor combination with a comprehensive risk indicator greater than or equal to the preset indicator threshold as key risk factors; the comprehensive risk indicator , ; A risk scenario construction module for constructing risk scenarios based on the key risk factors; each risk scenario represents a combination of key risk factors at different levels; A risk scenario simulation module for simulating the operation of power system assets under each risk scenario, and determining the risk level of each risk scenario based on the probability of power system assets failing under each risk scenario and the degree of interference and impact of power system asset failures on the power grid system; A risk prediction module for matching the environmental meteorological data of the power system assets monitored in real time with the risk scenarios, and determining the risk level of the power system assets failing as the risk level of the target risk scenario that is matched.

7. An electronic device, comprising: A memory for storing computer software programs; A processor for reading and executing the computer software programs, wherein when the computer software programs are executed by the processor, the power system asset risk prediction method based on big data analysis as described in any one of claims 1 to 5 is implemented.

8. A non-transitory computer-readable storage medium storing a computer software program, characterized in that, When the computer software programs are executed by the processor, the power system asset risk prediction method based on big data analysis as described in any one of claims 1 to 5 is implemented.

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