Regional risk assessment method for circuit fault of wind disaster

Abstract

The invention relates to the technical field of power grids, and particularly provides a regional risk assessment method for circuit faults of wind disasters, which comprises the following steps: calculating predicted fault rates and actual fault rates of various circuit devices in various power regions under the wind disasters; according to the predicted failure rate and the actual failure rate, risk assessment indexes corresponding to the various circuit devices are calculated; calculating comprehensive weights of the risk assessment indexes of the various circuit devices according to the risk assessment indexes; calculating an error correction coefficient and a corresponding error correction weight; and calculating a risk assessment value of each power region based on the risk assessment indexes of various circuit devices, the corresponding comprehensive weights, the error correction coefficients and the corresponding error correction weights. Therefore, the problem of poor power supply stability of a power grid caused by difficult accurate prediction of faults of power equipment under wind disasters in related technologies is solved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power grids, in particular to a regional risk assessment method for circuit faults in wind disasters. BACKGROUND

[0002] In a power system, the influence of wind disasters on power transmission lines and equipment is increasingly concerned. Strong wind disasters can cause tower damage, line tripping, and even large-scale power outage accidents, causing serious social and economic losses. With the increase of climate change and extreme weather events, traditional power system wind disaster protection measures are facing greater challenges, and new evaluation and protection methods need to be developed to improve the disaster resistance and recovery ability of the system. The existing wind disaster evaluation methods mostly focus on the calculation of wind speed and the analysis of actual damage to towers, and it is difficult to accurately predict the failure of power equipment under wind disasters, thereby causing poor power supply stability of the power grid.

[0003] In view of the problem that the failure of power equipment under wind disasters is difficult to accurately predict in the related art, thereby causing poor power supply stability of the power grid, no effective solution has been proposed at present. SUMMARY

[0004] The regional risk assessment method for circuit faults in wind disasters provided by the embodiments of the application at least solves the problem that the failure of power equipment under wind disasters is difficult to accurately predict in the related art, thereby causing poor power supply stability of the power grid.

[0005] According to an aspect of an embodiment of the application, a regional risk assessment method for circuit faults in wind disasters is provided, including: calculating the predicted failure rate of various circuit equipment of each power region according to the meteorological information of wind disasters, and statistically analyzing the actual failure rate of various circuit equipment in the current time period according to the circuit failure data of wind disasters; calculating the risk assessment index corresponding to various circuit equipment according to the predicted failure rate and the actual failure rate; calculating the comprehensive weight value of the risk assessment index of various circuit equipment according to the risk assessment index of various circuit equipment at different times in the current time period, wherein the comprehensive weight value is calculated based on fluctuation trend and distance matching principle according to subjective weight value and objective weight value; calculating the error correction coefficient and the corresponding error correction weight value based on the total number of various circuit equipment, and the error of the predicted failure rate and the actual failure rate; and calculating the risk assessment value of each power region based on the risk assessment index of various circuit equipment and the corresponding comprehensive weight value, and the error correction coefficient and the corresponding error correction weight value.

[0006] As an option, according to the predicted failure rate and the actual failure rate, a risk assessment index corresponding to each circuit equipment is calculated, including: according to the predicted failure rate and the actual failure rate of each circuit equipment of the target type, a prediction error is calculated; based on the total number of circuit equipment of the target type and the prediction error of each circuit equipment, a risk assessment index corresponding to the circuit equipment of the target type is calculated.

[0007] As an option, the various circuit equipment includes towers and lines, and based on the total number of circuit equipment of the target type and the prediction error of each circuit equipment, the risk assessment index corresponding to the circuit equipment of the target type is calculated, including: based on the total number of towers and the square of the prediction error of each tower, the tower overturning rate is calculated as the corresponding risk assessment index, and the calculation formula is as follows: , wherein, is the tower overturning rate, is the total number of towers, and respectively represent the actual failure rate and the predicted failure rate of the i-th tower; based on the total number of lines and the absolute value of the prediction error of each line, the line tripping rate is calculated as the corresponding risk assessment index, and the calculation formula is as follows: , wherein, is the line tripping rate, is the total number of lines, and respectively represent the actual failure rate and the predicted failure rate of the j-th line.

[0008] As an option, according to the risk assessment index of each circuit equipment at different times of the current time period, a comprehensive weight of the risk assessment index of each circuit equipment is calculated, including: through expert evaluation, a subjective weight of the risk assessment index of each circuit equipment is determined; through the risk assessment index of each circuit equipment at different times of the current time period, an objective weight of the risk assessment index of each circuit equipment is calculated; based on the fluctuation trend and distance matching principle, an equation group of the proportion of subjective weight and the proportion of objective weight is created, and the proportion of subjective weight and the proportion of objective weight are solved; based on the subjective weight and the corresponding proportion of subjective weight, and the objective weight and the corresponding proportion of objective weight, the comprehensive weight of the risk assessment index of each circuit equipment is calculated.

[0009] As an option, the equation group is as follows: , wherein, and are the subjective weight and the objective weight, is the proportion of subjective weight, is the proportion of objective weight, The comprehensive weight result matched based on the fluctuation trend and distance matching principle, The matching proportion coefficient.

[0010] As an optional solution, the objective weight of the risk assessment index of each circuit equipment is calculated through the risk assessment index of each circuit equipment at different times in the current time period, including: creating a decision matrix through the risk assessment index of each circuit equipment at different times in the current time period; normalizing the decision matrix; calculating the entropy weight redundancy of the risk assessment index of each circuit equipment at different times based on the normalized decision matrix; and calculating the objective weight of the current time based on the entropy weight redundancy.

[0011] As an optional solution, the decision matrix is as follows: , wherein, represents the tower overturning rate corresponding to the kth time , represents the tripping rate of the line corresponding to the kth time , k = 1, 2, 3…t; the entropy weight redundancy of the risk assessment index of each circuit equipment at different times is calculated based on the normalized decision matrix by the following formula: , wherein, is the entropy weight redundancy, is the proportion of the element in the mth column and the nth row in the X decision matrix; m = 1, 2, n = 1, 2, 3,…t; , wherein, is the element in the mth column and the nth row in the normalized X decision matrix; the objective weight of the current time is calculated based on the entropy weight redundancy by the following formula: , wherein, is the objective weight of the risk assessment index of the nth circuit equipment.

[0012] As an optional solution, the predicted failure rate of various circuit equipment in each power region is calculated according to the meteorological information of wind disaster, including: collecting wind disaster meteorological information in the current time period under wind disaster meteorological conditions, wherein the wind disaster meteorological information includes wind speed data of different power regions at different times; performing standardization processing on the wind speed data, and calculating the wind load of various circuit equipment according to the standardized wind speed data; calculating the failure probability of various circuit equipment according to the wind load; based on the distribution curve of the failure probability relative to the wind speed data, and the probability distribution type of different circuit equipment failures, data comparison is performed; in the case of comparison passing, the failure probability is determined as the failure prediction value of the corresponding circuit equipment.

[0013] As an alternative, the wind speed data is normalized, including: converting the wind speed data by the following formula: , wherein, is the converted wind speed value; h is the height of the tower from the ground; the local wind speed value is detected according to the height of 10 m from the ground, and V represents the maximum wind speed value; represents the ground friction coefficient; the converted wind speed value is normalized by the following formula: , wherein, is the normalized wind speed data of the u-th power area, is the converted wind speed value of the v-th wind speed data detected in the u-th power area, a represents the total number of data measured in the u-th power area, and v = 1, 2, 3…a.

[0014] As an alternative, the various circuit devices include towers and lines, and the wind load of the various circuit devices is calculated according to the normalized wind speed data, including: the wind load of the line in the u-th power area is calculated by the following formula: , wherein, is the wind pressure uneven coefficient; is the wind pressure height change coefficient; is the body shape coefficient of the conductor; is the outer diameter / m of the conductor; is the number of conductor sections; is the horizontal span / m; the wind load of the tower in the u-th power area is calculated by the following formula: , wherein, are the windward areas of the tower in the current wind direction, respectively; is the body shape coefficient of the tower; is the wind vibration coefficient.

[0015] As an alternative, the failure probability of the various circuit devices is calculated according to the wind load, including: the failure rate of the tower is calculated by the following formula: , wherein, is the failure rate of the tower, is the function function of the tower failure model; is the design load of the tower; is the failure probability of the broken line, is the probability when the function function of the tower failure model is greater than 0; the failure rate of the line is calculated by the following formula: , wherein, is the failure rate of the line, is the function function of the line failure model; is the design load of the line; is the failure probability of the line.

[0016] As an optional solution, based on the total number of various circuit devices, and the error of the predicted failure rate and the actual failure rate, the error correction coefficient and the corresponding error correction weight are calculated, including: calculating the error correction coefficient by the following formula: , wherein, is the error correction coefficient, m= ; the error correction weight is calculated by the following formula: , wherein, is the error correction weight.

[0017] As an optional solution, the method further comprises: according to the predicted failure rate of each circuit device, marking the circuit device whose predicted failure rate exceeds the preset failure rate threshold as a risk device; according to the risk assessment value of each power area, marking the power area whose risk assessment value is higher than the preset assessment threshold as a risk area; in the case of current time change, updating the current time period, and the corresponding weather information and circuit failure data of the wind disaster, calculating the predicted failure probability of various circuit devices at the updated current time, and the risk assessment value of each power area.

[0018] According to another aspect of the embodiments of the present application, an electronic device is also provided, comprising: a processor, and a memory storing a program, characterized in that the program comprises instructions which, when executed by the processor, cause the processor to perform any one of the above methods.

[0019] According to another aspect of the embodiments of the present application, a non-transitory machine-readable medium storing computer instructions is also provided, characterized in that the computer instructions are used to cause the computer to perform any one of the above methods.

[0020] According to another aspect of the embodiments of the present application, a computer program product is also provided, comprising computer programs / instructions, characterized in that the computer programs / instructions are executed by a processor to implement any one of the above methods.

[0021] The wind disaster circuit fault area risk assessment method provided by the embodiment of the application creates, through wind disaster meteorological information, calculates the predicted failure rate of various circuit devices in each power area under wind disaster, combines the actual failure rate to calculate the risk assessment index corresponding to various circuit devices, and calculates the comprehensive weight of the risk assessment index of various circuit devices as a factor for accurate risk assessment of the power area, and calculates the error correction coefficient and the corresponding error correction weight, and finally calculates the risk assessment value of each power area based on the risk assessment index of various circuit devices and the corresponding comprehensive weight, and the error correction coefficient and the corresponding error correction weight, so as to accurately determine the risk assessment value of the power area as a scientific basis for disaster prevention and mitigation decision of the power system. The problem that the failure of power equipment under wind disaster is difficult to accurately predict in the related art, and thus the power grid power supply stability is poor, is solved. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings needed to be used in the embodiment or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other embodiments can also be obtained from these drawings without creative labor.

[0023] Figure 1 is a flow chart of a wind disaster circuit fault area risk assessment method of an embodiment of the application.

[0024] Figure 2 is a schematic diagram of risk assessment result visualization of an embodiment of the application.

[0025] Figure 3 is a structural schematic diagram of an electronic device of the application. DETAILED DESCRIPTION

[0026] The embodiments of the application will be described in more detail below with reference to the drawings. Although some embodiments of the application are shown in the drawings, it should be understood that the application can be implemented in various forms, and should not be interpreted as being limited to the embodiments set forth herein, on the contrary, these embodiments are provided to more thoroughly and completely understand the application. It should be understood that the drawings and embodiments of the application are only for exemplary purposes, and are not intended to limit the scope of protection of the application.

[0027] The influence of wind disaster on power system mainly manifests in two aspects: one is the physical damage of wind to power transmission line and tower, and the other is the failure or trip of power equipment caused by wind disaster. Wind may cause the inclination or collapse of tower, and even the failure of the whole line in severe cases. In addition, wind disaster may also cause the overload, short circuit or electrical failure of power equipment, thereby causing the action of protection device, leading to line trip and affecting the reliability and stability of power supply.

[0028] Therefore, the research on the evaluation method of tower damage and line trip risk under wind disaster has become an important content for improving the disaster resistance of power system.

[0029] In order to improve the accuracy and operability of risk assessment, a more refined risk assessment model needs to be constructed based on the meteorological characteristics of wind disaster and combined with the specific operation conditions of power system. The model can not only quantitatively evaluate the damage degree of various types of equipment under wind disaster, but also analyze the possible trip events under different wind disaster situations, thereby providing a scientific basis for the disaster prevention and mitigation decision of power system.

[0030] In order to solve the above technical problems, the embodiments of the present application provide a regional risk assessment method for circuit failure of wind disaster, as shown in Figure 1 The method comprises the following steps.

[0031] Step S101, according to the meteorological information of wind disaster, the predicted failure rate of various circuit equipment of each power region is calculated, and according to the circuit failure data of wind disaster, the actual failure rate of various circuit equipment in the current time period is counted;

[0032] Step S102, according to the predicted failure rate and the actual failure rate, the risk assessment index corresponding to various circuit equipment is calculated;

[0033] Step S103, according to the risk assessment index of various circuit equipment at different times in the current time period, the comprehensive weight value of the risk assessment index of various circuit equipment is calculated, wherein the comprehensive weight value is calculated based on the fluctuation trend and distance matching principle according to the subjective weight value and the objective weight value;

[0034] Step S104, based on the total number of various circuit equipment and the error of predicted failure rate and actual failure rate, the error correction coefficient and the corresponding error correction weight value are calculated;

[0035] Step S105, based on the risk assessment index of various circuit equipment and the corresponding comprehensive weight value, and the error correction coefficient and the corresponding error correction weight value, the risk assessment value of each power region is calculated.

[0036] The area risk assessment method for circuit failure in wind disaster provided by the embodiment of the application, by means of meteorological information of wind disaster, calculates the predicted failure rate of various circuit devices in each power area under wind disaster, combines the actual failure rate to calculate the risk assessment index corresponding to various circuit devices, and calculates the comprehensive weight of the risk assessment index of various circuit devices as a factor for accurate risk assessment of the power area, and calculates the error correction coefficient and the corresponding error correction weight, and finally calculates the risk assessment value of each power area based on the risk assessment index of various circuit devices and the corresponding comprehensive weight, and the error correction coefficient and the corresponding error correction weight, so as to accurately determine the risk assessment value of the power area as a scientific basis for disaster prevention and mitigation decision of the power system.

[0037] The execution subject of the above-mentioned step can be a server of the power system, which communicates with the detection device and power equipment of each power area through communication to obtain data.

[0038] The meteorological information of wind disaster can be data of the meteorological department or measured data of the sensor of the power equipment. Considering that the data of the meteorological department is relatively macro and difficult to achieve high precision, the embodiment adopts the mode of setting sensors on power equipment to detect actual meteorological data to improve the accuracy of meteorological information.

[0039] The meteorological information can include rainfall, snowfall, temperature, wind power, wind speed and other data. Generally, in the wind disaster scene, the main data is wind speed data. The wind speed data is mainly considered in the calculation of the predicted failure rate in the embodiment.

[0040] According to the actually measured meteorological information of wind disaster, the stress of various power equipment can be calculated, and the predicted failure rate of various circuit devices can be judged. The predicted failure rate is combined with the accurate meteorological data actually detected, so it has high accuracy.

[0041] Then, according to the circuit failure data of wind disaster, which is also obtained by the power system in actual wind disaster, the actual failure rate of various circuit devices in the current time period is calculated through actual sensors or communication equipment.

[0042] The circuit device can be a tower, a line, a case, a cabinet, etc. In the wind disaster scene, the main failure is the tower and the overhead cable line, which is easy to bear large wind power and cause failure. Therefore, the embodiment can mainly consider the conditions of the tower and the line.

[0043] It should be noted that the risk assessment method of the embodiment is real-time dynamic assessment. For wind disasters, when the actual measured wind speed exceeds the threshold, the meteorological information and circuit failure data are detected, the predicted failure rate and the actual failure are calculated, and then the calculation is performed according to the above steps S101-S105, and then visual output is performed. After the current time is updated, the above current time period, and the meteorological information and circuit failure data of the wind disaster are updated, so as to perform dynamic assessment.

[0044] In order to make the final evaluation result have a certain foresight, the risk assessment can be performed by combining the predicted failure rate and the actual failure rate.

[0045] According to the predicted failure rate and the actual failure rate, the failure rate prediction deviation can be determined, and combined with the actual situation of each power equipment of the same type, the risk assessment index corresponding to each circuit equipment can be calculated. The higher the risk assessment index, the higher the risk of the power equipment of the same type.

[0046] Considering the change of data over time, according to the risk assessment indexes of various circuit equipments at different times in the current time period, the comprehensive weight of the risk assessment indexes of various circuit equipments is calculated. As an accurate reference for subsequent power area risk assessment.

[0047] The above comprehensive weight is calculated based on the subjective weight and the objective weight based on the fluctuation trend and the distance matching principle, which will be described in detail later. The above subjective weight is based on expert experience or human judgment, reflecting the subjective cognition of decision makers on the importance of indexes. Usually determined by questionnaire survey, Delphi method, analytic hierarchy process (AHP) and other methods.

[0048] The above objective weight is based on the statistical characteristics or mathematical laws of data itself, which is completely data-driven and has no human intervention. Common methods: entropy weight method, coefficient of variation method, principal component analysis (PCA) and the like.

[0049] The accurate combination of subjective and objective weights can improve the accuracy of risk assessment. The key point is to accurately determine the proportion of subjective and objective weights, which will be described in detail later.

[0050] Based on the total number of various circuit equipments and the error of the predicted failure rate and the actual failure rate, the error correction coefficient and the corresponding error correction weight are calculated.

[0051] Through the error correction coefficient and the error correction weight, dynamic weight adjustment can be performed, realizing more accurate risk quantification, correcting prediction deviation, avoiding underestimation or overestimation of risk. And adaptive weight allocation, the weight of high-error equipment is suppressed, and the weight of reliable indicators is enhanced, improving the evaluation reliability.

[0052] Therefore, the accuracy, adaptability and operability of the power risk assessment are significantly improved, and the method is especially suitable for a complex scenario with rich data but imperfect prediction model.

[0053] Finally, the risk assessment value of each power area is calculated based on the risk assessment indexes of various circuit devices and corresponding comprehensive weights, as well as error correction coefficients and corresponding error correction weights.

[0054] By comprehensively considering the multi-faceted influence of wind disasters on various power devices, by accurately predicting possible power devices, and by taking timely protective measures such as reinforcing structures and adjusting loads, large-scale power outage accidents can be effectively avoided, and the stability and resilience of the power system under wind disaster weather conditions can be improved. Thus, the problem in the related art that power device failures under wind disasters are difficult to accurately predict, thereby leading to poor power grid power supply stability, is solved.

[0055] As an optional implementation, the risk assessment index corresponding to each circuit device is calculated according to the predicted failure rate and the actual failure rate, including: calculating the prediction error according to the predicted failure rate and the actual failure rate of each circuit device of the target type; and calculating the risk assessment index corresponding to the circuit device of the target type based on the total number of circuit devices of the target type and the prediction error of each circuit device.

[0056] The prediction accuracy can be improved in dynamic assessment by calculating the error. By combining the predicted failure rate and the actual failure rate to determine the risk assessment index, the needs for forward-looking assessment and actual objectivity can be met.

[0057] By combining the total number of circuit devices of a single type and the prediction error, the cumulative risk of high-frequency low-loss devices, such as a large number of line cables, can be avoided.

[0058] As an optional implementation, the various circuit devices include towers and lines, and the risk assessment index corresponding to the circuit device of the target type is calculated based on the total number of circuit devices of the target type and the prediction error of each circuit device, including: calculating the tower overturning rate of the tower based on the total number of towers and the square of the prediction error of each tower, and taking the tower overturning rate as the corresponding risk assessment index, and the calculation formula is as follows:

[0059]

[0060] In the formula, is the tower overturning rate, is the total number of towers, and respectively represent the actual failure rate and the predicted failure rate of the i-th tower; the tripping rate of the line is calculated based on the total number of lines and the absolute value of the prediction error of each line, and is taken as the corresponding risk assessment indicator, and the calculation formula is as follows:

[0061]

[0062] In the formula, is the tripping rate of the line, is the total number of lines, and respectively represent the actual failure rate and the predicted failure rate of the j-th line.

[0063] The above formula compresses the multi-dimensional data of the power equipment, including the predicted failure rate, the actual failure rate, the quantity, and the prediction error, into a single operable indicator. Decision-making can be simplified, and resource allocation can be supported. More variables, such as environmental data and equipment age, can be added to improve accuracy.

[0064] As an optional implementation, according to the risk assessment indicators of various circuit devices at different times in the current time period, the comprehensive weight of the risk assessment indicators of various circuit devices is calculated, including: determining the subjective weight of the risk assessment indicators of each circuit device through expert evaluation; calculating the objective weight of the risk assessment indicators of each circuit device through the risk assessment indicators of each circuit device at different times in the current time period; based on the fluctuation trend and distance matching principle, an equation group of the subjective weight proportion and the objective weight proportion is created, and the subjective weight proportion and the objective weight proportion are solved; based on the subjective weight and the corresponding subjective weight proportion, and the objective weight and the corresponding objective weight proportion, the comprehensive weight of the risk assessment indicators of each circuit device is calculated.

[0065] The above determines the subjective weight of the risk assessment indicators of each circuit device through expert evaluation. An expert group can be constructed, including power engineers, operation and maintenance experts, safety analysts, etc. Anonymous analysis and multiple rounds of feedback are performed through the Delphi method until convergence, and the subjective weight is finally obtained.

[0066] The analytic hierarchy process can also be used to first construct a hierarchical structure, for example, the target layer: comprehensive risk assessment of circuit devices; the criterion layer: failure probability, consequence severity, maintenance difficulty, etc.; the scheme layer: specific equipment types, such as towers and lines. Then create a pairwise comparison matrix: experts compare the importance of failure probability between two indicators at the same level. After passing the consistency test, the characteristic vector method or the geometric mean method is used to solve the subjective weight.

[0067] The objective weight value is calculated by the risk evaluation indexes of each circuit equipment at different times in the current time period. Specifically, the risk evaluation indexes of circuit equipment at different times are considered, and the entropy weight method is used for calculation. Details are described later.

[0068] For the subjective weight proportion and the objective weight proportion, the embodiment considers that the data fluctuation characteristics will affect the calculation results in a certain time scale. The fluctuation trend and distance matching principle is used to match the difference between the subjective and objective weight values and the difference between the corresponding weight results, to obtain the subjective weight proportion and the objective weight proportion.

[0069] As an optional implementation, the equation set is as follows:

[0070]

[0071] In the formula, and are the subjective weight value and the objective weight value respectively, is the subjective weight proportion, is the objective weight proportion,

[0072] is the comprehensive weight result matched based on the fluctuation trend and distance matching principle, is the matching proportion coefficient.

[0073] The equation set eliminates the influence of data fluctuation, improves the accuracy of the subjective weight proportion and the objective weight proportion, and further improves the accuracy of the final comprehensive weight and risk evaluation.

[0074] As an optional implementation, the objective weight value of the risk evaluation index of each circuit equipment is calculated by the risk evaluation index of each circuit equipment at different times in the current time period, including: creating a decision matrix by the risk evaluation index of each circuit equipment at different times in the current time period; normalizing the decision matrix; calculating the entropy weight redundancy of the risk evaluation index of each circuit equipment at different times based on the normalized decision matrix; and calculating the objective weight value of the current time based on the entropy weight redundancy.

[0075] The decision matrix is constructed by the circuit equipment risk evaluation indexes of different time periods, which can realize multi-dimensional and dynamic risk management and resource optimization allocation. As an optional implementation, the decision matrix is as follows:

[0076]

[0077] In the formula, represents the tower overturning rate of the tower corresponding to the kth time , The tripping rate of the kth line corresponding to the first time , k = 1, 2, 3…t.

[0078] The normalized matrix elements can be calculated by the following formula:

[0079]

[0080] The entropy weight redundancy of the risk assessment index of each circuit device at different times is calculated based on the normalized decision matrix by the following formula:

[0081]

[0082] In the formula, is the entropy weight redundancy, is the proportion of the element in the mth column and the nth row of the X decision matrix; m = 1, 2, n = 1, 2, 3, … t;

[0083]

[0084] wherein, is the element in the mth column and the nth row of the normalized X decision matrix;

[0085] The objective weight of the current time is calculated based on the entropy weight redundancy by the following formula:

[0086]

[0087] In the formula, is the objective weight of the risk assessment index of the nth circuit device.

[0088] The entropy weight method of the above formula is used to quantify the amount of index information, and the redundancy analysis eliminates the interference of repeated information. The weight of the high correlation index can be automatically reduced, the entropy weight is calculated based on real-time data, the device risk weight is dynamically adjusted, and the subjective bias is reduced. Therefore, the objective weight of the risk assessment index of the circuit device can be accurately calculated.

[0089] As an optional implementation, according to the meteorological information of the wind disaster, the predicted failure rate of various circuit devices in each power area is calculated, including: collecting wind disaster meteorological information in the current time period under wind disaster meteorological conditions, wherein the wind disaster meteorological information includes wind speed data of different power areas at different times; standardizing the wind speed data, and calculating the wind load of various circuit devices according to the standardized wind speed data; calculating the failure probability of various circuit devices according to the wind load; comparing the distribution curve of the failure probability with respect to the wind speed data with the probability distribution type of different circuit device failures; in the case of passing the comparison, the failure probability is determined as the failure prediction value of the corresponding circuit device.

[0090] The wind speed data is standardized to eliminate the dimension, map the wind speed to the same scale, and facilitate subsequent calculation. The calculation of wind load can be combined with wind speed data and the design size of circuit equipment to calculate the actual wind load. The wind load can more realistically represent the stress condition of the circuit equipment and the failure probability.

[0091] The above distribution curve based on the failure probability with respect to the wind speed data is compared with the probability distribution type of different circuit equipment failures. The distribution curve can be generated first, and the curve of the failure probability changing with the wind speed is drawn. For example, Sigmoid shape (Logistic Logistic function curve) or exponential shape (Weibull). Then compare the theoretical distribution: check whether the actual data distribution matches the theoretical model, such as KS test (Kolmogorov-Smirnov test) or visual fitting degree.

[0092] In the case of KS test p>0.05 or residual sum of squares (RSS) less than the threshold, the comparison passes. If it does not pass, the model needs to be corrected, such as replacing the distribution type or refitting the parameters. Avoid the influence of abnormal data of wind speed data on the calculation result, and further improve the data accuracy.

[0093] As an optional implementation, the standardization processing of wind speed data includes: converting the wind speed data by the following formula:

[0094]

[0095] In the formula, is the converted wind speed size; h is the height of the tower from the ground; the local wind speed value is detected according to the height of 10m from the ground, and V represents the maximum wind speed value; is the ground friction coefficient; the converted wind speed size is standardized by the following formula:

[0096]

[0097] In the formula, is the standardized wind speed data of the u-th power area, is the converted wind speed size of the v-th wind speed data detected in the u-th power area, a represents the total number of data measured in the u-th power area, and v=1, 2, 3…a.

[0098] In the detection of wind speed, the detection equipment is arranged at a fixed relative height, and the local wind speed value is detected according to the height of 10 m from the ground as a standard, so as to eliminate the influence of the relative height on the wind force as much as possible. Generally speaking, the smaller the relative height, the closer to the ground, the smaller the wind speed, which is a typical feature in the case of wind disaster. Through the above setting, the wind speed data collected can have strong analysis performance and better represent the actual wind force.

[0099] As an optional implementation, the wind load of various circuit equipment including towers and lines is calculated according to the wind speed data processed according to the standard, including: the wind load of the line of the u-th power area is calculated by the following formula:

[0100]

[0101] In the formula, is a wind pressure uneven coefficient; is a wind pressure height change coefficient; is a body shape coefficient of the conductor; is an outer diameter / m of the conductor; is a conductor division number; is a horizontal span / m; the wind load of the tower of the u-th power area is calculated by the following formula:

[0102]

[0103] In the formula, are respectively the windward area of the tower in the current wind direction; is a body shape coefficient of the tower; is a wind vibration coefficient.

[0104] Through the above formula, the wind load of the tower and the line can be accurately calculated, which serves as the basis for predicting the failure rate in the subsequent calculation, and the accuracy of predicting the failure rate is improved.

[0105] As an optional implementation, the failure probability of various circuit equipment is calculated according to the wind load, including: the failure rate of the tower is calculated by the following formula:

[0106]

[0107] In the formula, is the failure rate of the tower, is a function function of the tower failure model; is the design load of the tower; is the failure probability of the broken line, is the probability when the function function of the tower failure model is greater than 0; the failure rate of the line is calculated by the following formula:

[0108]

[0109] wherein, is the failure rate of the line, is the limit state function of the line failure model; is the design load of the line; is the failure probability of the line.

[0110] The limit state function of the tower failure model is a mathematical expression for quantifying the structural reliability or failure probability of the tower under the action of external loads such as wind disasters. Its core idea is to compare the load effect and the structural resistance to determine whether the safety threshold is met.

[0111] The limit state function of the line failure model is a mathematical model for quantifying the failure probability of the transmission line under the action of external loads such as wind disasters. Its core is to compare the load effect and the resistance of the line components to determine whether the failure threshold is reached. Through the limit state function, the predicted failure rate can be calculated.

[0112] As an optional implementation, based on the total number of various circuit devices, and the error of the predicted failure rate and the actual failure rate, the error correction coefficient and the corresponding error correction weight are calculated, including: calculating the error correction coefficient by the following formula:

[0113]

[0114] wherein, is the error correction coefficient, m= ; the error correction weight is calculated by the following formula:

[0115]

[0116] wherein, is the error correction weight.

[0117] Through the above formula combined with the total number of towers and lines, error correction is performed to represent the error correction degree of risk assessment using the predicted failure rate of the tower and the predicted failure rate of the line, thereby improving the accuracy and rationality of the final comprehensive determination of the power region risk by combining the tower and the line.

[0118] As an optional embodiment, the method further comprises: according to the predicted failure rate of each circuit device, marking the circuit device with a predicted failure rate exceeding a preset failure rate threshold as a risk device; according to the risk assessment value of each power area, marking the power area with a risk assessment value higher than a preset assessment threshold as a risk area; in the case of current time change, updating the current time period, and the corresponding weather information and circuit failure data of the wind disaster, calculating the predicted failure probability of each circuit device and the risk assessment value of each power area in the updated current time.

[0119] It should be noted that the present embodiment also provides an optional embodiment, which will be described in detail below.

[0120] The present embodiment dynamically evaluates and predicts the tower predicted failure rate and the line predicted failure rate of different power areas and the risk assessment value of different areas according to the wind speed data. The specific scheme is as follows.

[0121]

[0122] In the formula, is the converted wind speed; h is the height of the tower from the ground; the local wind speed value is detected according to the height of 10 m from the ground, and V represents the maximum wind speed value; represents the ground friction coefficient;

[0123] The converted wind speed is standardized by the following formula:

[0124]

[0125] In the formula, is the standardized wind speed data of the u-th power area, is the converted wind speed of the v-th wind speed data detected by the u-th power area, a represents the total number of data measured by the u-th power area, and v=1, 2, 3…a.

[0126] The wind load of the line of the u-th power area is calculated by the following formula:

[0127]

[0128] In the formula, is the wind pressure uneven coefficient; is the wind pressure height change coefficient; is the body shape coefficient of the conductor; is the outer diameter of the conductor / m; is the number of conductor sections; is the horizontal span / m;

[0129] The wind load of the tower of the u-th power area is calculated by the following formula:

[0130]

[0131] wherein, are the windward areas of the tower in the current wind direction, respectively; is the shape coefficient of the tower; is the wind vibration coefficient.

[0132] The failure rate of the tower is calculated by the following formula:

[0133]

[0134] wherein, is the failure rate of the tower, is the function function of the tower failure model; is the design load of the tower; is the failure probability of the broken wire, is the probability when the function function of the tower failure model is greater than 0;

[0135] The failure rate of the line is calculated by the following formula:

[0136]

[0137] wherein, is the failure rate of the line, is the function function of the line failure model; is the design load of the line; is the failure probability of the line.

[0138] wherein , and are influenced by , which are probability distributions.

[0139] The obtained and are converted into and , i.e. the predicted failure rate:

[0140]

[0141]

[0142] is the tower failure rate of the u-th tower, is the line failure rate of the u-th line.

[0143] ​​The actual failure rate is obtained based on the circuit failure data of the actual current time period when a gale disaster occurs. The risk assessment index is calculated based on the actual failure rate and the predicted failure rate.

[0144] The tower overturning rate is obtained based on the total number of towers and the square of the prediction error of each tower, as the corresponding risk assessment index, and the calculation formula is as follows:

[0145]

[0146] In the formula, is the tower overturning rate, is the total number of towers, and respectively represent the actual failure rate and the predicted failure rate of the i-th tower.

[0147] The tripping rate of the line is obtained based on the total number of lines and the absolute value of the prediction error of each line, as the corresponding risk assessment index, and the calculation formula is as follows:

[0148]

[0149] In the formula, is the tripping rate of the line, is the total number of lines, and respectively represent the actual failure rate and the predicted failure rate of the j-th line.

[0150] The tower overturning rate index and represent the subjective weight of the line failure rate during the gale disaster and , both of which are 0.5 in the embodiment.

[0151] The objective weight of the tower overturning rate index and the line failure rate during the gale disaster and is determined:

[0152] The weight calculation is performed in a time sequence manner, and the number of time sections from the initial evaluation time section of the current time period to the current time section is , then the index and calculated at the t-th time section can form the original decision matrix :

[0153]

[0154] In the formula, represents the tower overturning rate corresponding to the k-th time , indicates the trip rate of the line corresponding to the kth time , k = 1, 2, 3…t;

[0155] The decision matrix X is normalized:

[0156]

[0157] m in the decision matrix can represent different time samples in the current time period, and n can represent different circuit devices, including towers and lines. The elements of the normalized decision matrix X are converted into the proportion of the mth sample on the nth risk assessment index:

[0158]

[0159] wherein, is the element of the mth column and the nth row in the normalized decision matrix X;

[0160] Further, based on the normalized decision matrix, the entropy weight redundancy of the risk assessment index of each circuit device at different times is calculated by the following formula:

[0161]

[0162] wherein, is the entropy weight redundancy, is the proportion of the element of the mth column and the nth row in the decision matrix X; m = 1, 2, n = 1, 2, 3,…t;

[0163] Based on the entropy weight redundancy, the objective weight value of the current time is calculated by the following formula:

[0164]

[0165] wherein, is the objective weight value of the nth circuit device risk assessment index.

[0166] Specifically, the objective weight value of the tower at the tth time is:

[0167]

[0168] The objective weight value of the line at the tth time is:

[0169]

[0170] The comprehensive weight values of the risk assessment indexes of the tower and the line, i.e., the tower overturning index and the line fault index, are calculated respectively and :

[0171]

[0172] In the formula, represents the comprehensive weight value after linear combination; represents the subjective weight proportion; represents the objective weight proportion; and correspond to the subjective and objective weight results, respectively.

[0173] wherein, in the calculation of and , the data fluctuation characteristics under a certain time scale will affect the calculation results, and based on the fluctuation trend and distance matching principle, the difference between the subjective and objective weights is matched with the difference between the corresponding weight results:

[0174]

[0175] The equation group composed of the above equations and is solved to obtain and .

[0176] In the formula, and represent the comprehensive weight value results and the proportion coefficient after matching, respectively; and represent the corresponding matching coefficients.

[0177] Finally, the comprehensive risk evaluation of each power area can be represented as:

[0178]

[0179]

[0180]

[0181] The risk evaluation method of the present embodiment can identify the risk of tower damage and line trip under strong wind disasters by comprehensively considering the multi-faceted impact of wind disasters on the power system. By accurately predicting possible risk points, protective measures such as reinforcing towers and adjusting loads can be taken in time, thereby effectively avoiding large-scale power outages and improving the stability and resilience of the power system under extreme weather conditions.

[0182] The present embodiment selects actual distribution network and its 2024 strong wind disaster data as an example for analysis.

[0183] The actual distribution network includes three areas A, B, and C, and the risk results of tower damage and line failure rate in each area are shown in Table 1.

[0184] Table 1 Tower damage and line fault rate risk results of each region

[0185] A region B region C region Risk rate of pole overturning accident in a region 0.7631 0.0658 0.1024 Risk rate of line tripping accident in a region 0.5489 0.1142 0.2145 Tower number with maximum risk 158 25 65 Tower number with actual fault 158 25 65 Line number with maximum risk 150 29 87 Line number with actual fault 150 29 87 Number of risky towers in a region 35 6 19 Number of actual fault towers in a region 33 6 15 Number of risky lines in a region 27 15 20 Number of actual fault lines in a region 28 14 19

[0186] In combination Figure 2 As shown, by the risk assessment method of the embodiment, the region with the maximum risk of tower collapse and line trip under strong wind disaster and the corresponding tower and line numbers in the region can be obtained. The obtained results are consistent with the actual situation. Not only is it helpful for risk warning before the wind disaster, but also provides important data support for the intelligent construction of the power system. With the rapid development of smart grid, by combining with real-time meteorological data and online monitoring system, the method can assess the disaster risk in real time and automatically adjust the system operation strategy. After the disaster, based on the assessment results of the method, the damaged region can be quickly identified, the recovery priority can be accurately judged, the speed and accuracy of post-disaster recovery can be greatly improved, and the impact on the social economy can be reduced.

[0187] Based on the above-mentioned regional risk assessment method of circuit fault in wind disaster provided by the embodiment of the application, the embodiment of the application further provides a regional risk assessment device of circuit fault in wind disaster, which is applied to the risk assessment of power system under wind disaster. The device comprises:

[0188] A fault rate calculation module is configured to calculate the predicted fault rates of various circuit devices in each power region according to the meteorological information of wind disaster, and to calculate the actual fault rates of various circuit devices in the current time period according to the circuit fault data of wind disaster;

[0189] An index determination module is configured to calculate the risk assessment indexes corresponding to various circuit devices according to the predicted fault rates and the actual fault rates;

[0190] An index weight module is configured to calculate the comprehensive weights of the risk assessment indexes of various circuit devices according to the risk assessment indexes of various circuit devices at different times in the current time period, wherein the comprehensive weights are calculated based on fluctuation trend and distance matching principle according to subjective weights and objective weights;

[0191] An error correction module is configured to calculate error correction coefficients and corresponding error correction weights based on the total number of various circuit devices and the errors of the predicted fault rates and the actual fault rates;

[0192] A risk assessment module is configured to calculate the risk assessment values of each power region based on the risk assessment indexes of various circuit devices and the corresponding comprehensive weights, and the error correction coefficients and the corresponding error correction weights.

[0193] The device provided by the embodiment of the present application calculates the predicted failure rate of various circuit devices in each power area under the wind disaster through the meteorological information of the wind disaster, calculates the risk assessment index corresponding to the various circuit devices in combination with the actual failure rate, calculates the comprehensive weight of the risk assessment index of the various circuit devices as a factor for accurately assessing the risk of the power area, and finally calculates the risk assessment value of each power area based on the risk assessment index and the corresponding comprehensive weight of the various circuit devices, the error correction coefficient and the corresponding error correction weight, so as to accurately determine the risk assessment value of the power area as a scientific basis for the disaster prevention and mitigation decision of the power system.

[0194] The embodiment of the present application also provides a non-transient machine readable medium storing a computer program, wherein the computer program is used for making the computer execute the method of the embodiment of the present application when the computer program is executed by the processor of the computer.

[0195] The embodiment of the present application also provides a computer program product comprising a computer program, wherein the computer program is used for making the computer execute the method of the embodiment of the present application when the computer program is executed by the processor of the computer.

[0196] The embodiment of the present application also provides an electronic device comprising at least one processor and a memory connected with the at least one processor in communication. The memory stores a computer program capable of being executed by the at least one processor, and the computer program is used for making the electronic device execute the method of the embodiment of the present application when the computer program is executed by the at least one processor.

[0197] Reference Figure 3 The structure block diagram of the electronic device which can be the server or the client of the embodiment of the present application will be described, which is an example of the hardware device that can be applied to each aspect of the present application. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown in this paper, their connections and relationships, and their functions are only as examples, and are not intended to limit the implementation of the present application described and / or claimed herein.

[0198] As Figure 3As shown, the electronic device includes a computing unit 301 that can perform various appropriate actions and processes in accordance with a computer program stored in a read only memory (ROM) 302 or a computer program loaded into a random access memory (RAM) 303 from a storage unit 308. In the RAM 303, various programs and data required for operation of the electronic device can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0199] A plurality of components in the electronic device are connected to the I / O interface 305, including an input unit 306, an output unit 307, a storage unit 308, and a communication unit 309. The input unit 306 can be any type of device that can input information to the electronic device, and can receive inputted numerical or character information, and generate key signal inputs related to user settings and / or function controls of the electronic device. The output unit 307 can be any type of device that can present information, and can include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 308 can include, but is not limited to, a magnetic disk, an optical disk. The communication unit 309 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include, but is not limited to, a modem, a network card, an infrared communication device, and / or a wireless communication transceiver, such as a Bluetooth device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0200] The computing unit 301 can be various general and / or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a CPU, a graphics processing unit (GPU), various special purpose artificial intelligence (AI) computing units, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 301 performs various methods and processes described above. For example, in some embodiments, method embodiments of the present creation can be implemented as a computer program tangibly embodied in a machine readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device via the ROM 302 and / or the communication unit 309. In some embodiments, the computing unit 301 can be configured to perform the above-described methods by any other appropriate means, such as by means of firmware.

[0201] Computer programs used to implement embodiments of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program

[0202] In the context of embodiments of the present application, a machine-readable medium can be a tangible medium that can contain or store program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, or infrared signals, or any suitable combination thereof. More specific examples of a machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0203] It should be noted that the term "comprising" and variations thereof as used in embodiments of the present application are to be interpreted generically and do not exclude other steps. The term "based on" is to be interpreted as "based, at least in part, on". The term "one embodiment" means "at least one embodiment". The term "another embodiment" means "at least one additional embodiment". The term "some embodiments" means "at least some embodiments". The terms "a" or "an", as used herein, mean "one or more" unless explicitly stated otherwise.

[0204] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in embodiments of the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation portal for user to choose authorization or refusal.

[0205] The individual steps of the method embodiments provided by the embodiments of the present application can be performed in different orders and / or in parallel. Furthermore, the method embodiments can comprise additional steps and / or omit the performance of the steps shown. The scope of protection of the present application is not limited in this respect.

[0206] The word "comprise" in the specification means that the specific feature, structure or characteristic described in connection with an embodiment can be included in at least one embodiment of the present application. The use of this word in the specification does not mean that the same embodiment is necessarily included in all other embodiments or that the same embodiment is necessarily excluded from other embodiments. Each embodiment of the present application is described in relation to the relevant aspects of the application. Identical or similar parts of the various embodiments of the application are cross-referenced to each other. In particular, for the device, apparatus, system embodiments, the description is relatively brief as they are substantially similar to the method embodiments, and reference is made to the relevant parts of the description of the method embodiments.

[0207] The above-described embodiments are merely illustrative of the present application and do not limit the scope of the present application. The description is relatively specific and detailed, but it should not be understood as limiting the scope of protection. It should be noted that for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A method of regional risk assessment of windstorm-caused circuit failure, characterized by, The method comprises the following steps: According to the meteorological information of the wind disaster, the predicted failure rate of various circuit equipment in each power area is calculated, and the actual failure rate of various circuit equipment in the current time period is calculated according to the circuit failure data of the wind disaster; According to the predicted failure rate and the actual failure rate, the risk assessment index corresponding to various circuit equipment is calculated; According to the risk assessment index of various circuit equipment at different times in the current time period, the comprehensive weight of the risk assessment index of various circuit equipment is calculated, wherein the comprehensive weight is calculated based on fluctuation trend and distance matching principle according to subjective weight and objective weight; Based on the total number of various circuit equipment, and the error of predicted failure rate and actual failure rate, the error correction coefficient and the corresponding error correction weight are calculated; Based on the risk assessment index of various circuit equipment and the corresponding comprehensive weight, and the error correction coefficient and the corresponding error correction weight, the risk assessment value of each power area is calculated.

2. The method of claim 1, wherein, According to the predicted failure rate and the actual failure rate, the risk assessment index corresponding to various circuit equipment is calculated, including: According to the predicted failure rate and the actual failure rate of each circuit equipment of the target type, the prediction error is calculated; Based on the total number of circuit equipment of the target type, and the prediction error of each circuit equipment, the risk assessment index corresponding to the circuit equipment of the target type is calculated.

3. The method of claim 1, wherein, The various circuit equipment includes tower and line, based on the total number of circuit equipment of the target type, and the prediction error of each circuit equipment, the risk assessment index corresponding to the circuit equipment of the target type is calculated, including: Based on the total number of towers, and the square of the prediction error of each tower, the inverse tower rate of the tower is calculated as the corresponding risk assessment index, and the calculation formula is as follows: wherein, is the inverse tower ratio of the tower, is the total number of towers, and respectively represent the actual failure rate and the predicted failure rate of the i-th tower. Based on the total number of lines, and the absolute value of the prediction error of each line, the trip-out rate of the line is calculated as the corresponding risk assessment index, and the calculation formula is as follows: wherein is the trip rate of the line, is the total number of lines, and respectively represent the actual failure rate and the predicted failure rate of the jth line.

4. The method of claim 1, wherein, According to the risk assessment index of various circuit equipment at different times in the current time period, the comprehensive weight of the risk assessment index of various circuit equipment is calculated, including: The subjective weight of the risk assessment index of each circuit equipment is determined by expert evaluation; The objective weight of the risk assessment index of each circuit equipment is calculated through the risk assessment index of each circuit equipment at different times in the current time period; Based on the fluctuation trend and distance matching principle, an equation group of subjective weight proportion and objective weight proportion is created, and the subjective weight proportion and the objective weight proportion are solved; Based on the subjective weight and the corresponding subjective weight proportion, and the objective weight and the corresponding objective weight proportion, the comprehensive weight of the risk assessment index of each circuit equipment is calculated.

5. The method of claim 4, wherein, The equation group is as follows: wherein, and subjective weight value and objective weight value, respectively, subjective weight value ratio, objective weight value ratio, a comprehensive weight value result matched based on a fluctuation trend and a distance matching principle, a matching proportion coefficient.

6. The method of claim 4, wherein, The objective weight of the risk assessment index of each circuit equipment is calculated through the risk assessment index of each circuit equipment at different times in the current time period, including: A decision matrix is created through the risk assessment index of each circuit equipment at different times in the current time period; The decision matrix is normalized; Based on the normalized decision matrix, the entropy weight redundancy of the risk assessment index of each circuit equipment at different times is calculated. An objective weight value of the current time is calculated based on the entropy weight redundancy.

7. The method of claim 6, wherein, The decision matrix is as follows: In the formula, represents the inverted pole rate of the tower corresponding to the kth time , represents the tripping rate of the line corresponding to the kth time , k = 1, 2, 3…t; An entropy weight redundancy of a risk assessment index of each circuit equipment at different times is calculated based on the normalized decision matrix by the following formula: wherein is the entropy weight redundancy, is the proportion of the element in the mth column and nth row of the X decision matrix; m = 1, 2, n = 1, 2, 3, … t; wherein, is the element in the mth column and nth row of the normalized X decision matrix; An objective weight value of the current time is calculated based on the entropy weight redundancy by the following formula: In the formula, is the objective weight of the risk assessment index of the nth circuit device.

8. The method of claim 4, wherein, According to the meteorological information of the wind disaster, the predicted failure rates of various circuit equipment of each power region are calculated, including: Under the meteorological information of the wind disaster, the wind disaster meteorological information of the current time period is collected, wherein the wind disaster meteorological information includes wind speed data of different power regions at different times; The wind speed data is standardized, and the wind load of various circuit equipment is calculated according to the standardized wind speed data; According to the wind load, the failure probability of various circuit equipment is calculated; Based on the distribution curve of the failure probability with respect to the wind speed data, and the probability distribution type of the failure of different circuit equipment, data comparison is performed; In the case of comparison, the failure probability is determined as the failure prediction value of the corresponding circuit equipment.

9. The method of claim 8, wherein, The standardization processing of the wind speed data includes: The wind speed data is converted by the following formula: In the formula, is the converted wind speed; h is the height of the tower from the ground; the local wind speed value is detected according to the height of 10 m from the ground as a standard, and V represents the maximum wind speed value; represents the ground friction coefficient; Wind speed after halving Standardization is performed using the following formula: In the formula, is the standardized wind speed data of the u-th power area, is the converted wind speed of the v-th wind speed data detected by the u-th power area, a represents the total number of data measured by the u-th power area, and v = 1, 2, 3…a.

10. The method of claim 8, wherein, The various circuit equipment includes towers and lines, and the wind load of various circuit equipment is calculated according to the standardized wind speed data, including: The wind load of the line of the u-th power region is calculated by the following formula: wherein, is the wind pressure unevenness coefficient; is the wind pressure height variation coefficient; is the body shape coefficient of the conductor; is the outer diameter of the conductor / m; is the number of conductor sections; is the horizontal span / m; The wind load of the tower of the u-th power region is calculated by the following formula: wherein A, A respectively are the wind-attack area of the tower in the current wind direction; C is the shape coefficient of the tower; Cw is the wind vibration coefficient.

11. The method of claim 10, wherein, According to the wind load, the failure probability of various circuit equipment is calculated, including: The failure rate of the tower is calculated by the following formula: wherein is the failure rate of the tower, is the performance function of the tower failure model; is the design load of the tower; is the failure probability of the broken wire, is the probability that the performance function of the fallen tower failure model is greater than 0; The failure rate of the line is calculated by the following formula: wherein is the failure rate of the line, is a function of the line failure model; is the design load of the line; is the failure probability of the line.

12. The method of claim 1, wherein, Based on the total number of various circuit equipment, and the error between the predicted failure rate and the actual failure rate, an error correction coefficient and a corresponding error correction weight value are calculated, including: The error correction coefficient is calculated by the following formula: wherein is the error correction coefficient, m = 0 ; The error correction weight value is calculated by the following formula: In the formula, is an error correction weight.

13. The method according to any one of claims 1 to 12, characterized in that, The method further includes: According to the predicted failure rate of each circuit equipment, the circuit equipment whose predicted failure rate exceeds a preset failure rate threshold is marked as a risk equipment; According to the risk assessment value of each power region, the power region whose risk assessment value is higher than a preset assessment threshold is marked as a risk region; In the case of change of the current time, the current time period, and the corresponding meteorological information of the wind disaster and circuit failure data are updated, the predicted failure probability of various circuit equipment of the current time is calculated, and the risk assessment value of each power region is calculated.

14. An electronic device comprising: A processor and a memory storing programs, characterized in that the programs include instructions that, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 13.

15. A non-transitory machine-readable medium having stored thereon computer instructions, wherein: The computer instructions are used to make the computer execute the method according to any one of claims 1 to 13.

16. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instructions are executed by the processor to implement the method of any one of claims 1 to 13.

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