Power distribution network lightning arrester fault risk index estimation system and method

Abstract

The invention discloses a power distribution network lightning arrester fault risk index estimation system and method, and belongs to the technical field of lightning arrester fault detection, and the system comprises a lightning arrester parameter obtaining module which obtains the related parameter information of a lightning arrester; the environment parameter acquisition module is used for acquiring environment information of a lightning arrester mounting area; the service management end is used for processing and analyzing the acquired information and then generating a risk evaluation value so as to judge whether the hidden fault risk exists in the lightning arrester or not; the risk index determination module is used for determining the size of a lightning arrester fault risk index when the lightning arrester has a hidden fault risk; the service life prediction module is used for predicting the service life of the lightning arrester when the hidden fault risk does not exist in the lightning arrester; according to the invention, when no obvious fault risk occurs in the lightning arrester, the hidden fault risk of the lightning arrester which still works normally can be judged, and once the hidden fault risk is found, early warning and reminding are carried out in time, so that safe and stable operation of power equipment is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of lightning arrester fault detection, and in particular relates to a distribution network lightning arrester fault risk index prediction system and method. Background Art

[0002] The lightning arrester of the distribution network is a common protective device in the power system, which is used to protect the power equipment of the distribution network from overvoltage such as lightning. There are special discharge elements inside the lightning arrester. When overvoltage appears in the system, it will cause discharge, shunt the overvoltage to the ground wire, and protect the load equipment. Therefore, detecting the quality of the lightning arrester is an important measure to ensure the safe and stable operation of electricity.

[0003] The existing detection of lightning arrester faults is mostly done by testing the arrester's electrical parameters such as leakage current and breakdown voltage with electrical equipment such as a multimeter to observe whether they are normal, or by conducting manual inspections to observe whether the arrester is damaged or deformed. This method can only detect faults that have already occurred in the arrester, but cannot judge the potential fault risks of the arrester that has not failed. In this way, it is impossible to foresee the arrester's fault problems in time, thereby affecting the safe and stable operation of the power equipment. Summary of the invention

[0004] The purpose of the present invention is to provide a distribution network lightning arrester fault risk index prediction system and method to solve the problems faced in the above-mentioned background technology.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A distribution network arrester fault risk index prediction system, the system comprising:

[0007] A lightning arrester parameter acquisition module, which is used to obtain associated parameter information of the lightning arrester when it is working;

[0008] An environmental parameter acquisition module, which is used to obtain environmental information of the lightning arrester installation area;

[0009] A service management terminal, which is used to receive the acquired associated parameter information and environmental information, generate a risk assessment value after corresponding processing and analysis, and judge whether there is a hidden fault risk of the lightning arrester according to the risk assessment value;

[0010] The risk index determination module determines the arrester fault risk index size according to the risk assessment value when it is determined that the arrester has a hidden fault risk;

[0011] The life prediction module evaluates and predicts the service life of the arrester based on the acquired environmental information and risk assessment value when it is determined that the arrester does not have a hidden fault risk.

[0012] The display reminder module is connected to the corresponding terminal to transmit the real-time status of the lightning arrester and the fault risk index to the corresponding terminal for reminder.

[0013] Furthermore, the associated parameter information includes the detected temperature, leakage current, operating voltage, response time of the arrester to overvoltage each time it is subjected to overvoltage, and residual pressure at both ends of the arrester when the arrester is working, and the environmental information includes ambient temperature information, light intensity information, humidity information and abnormal weather information.

[0014] Furthermore, the method for obtaining the risk assessment value is:

[0015] When the arrester is monitored to be operating normally, the temperature information value T, the leakage current information value LC and the working voltage information value U of the arrester are obtained within a predetermined period ΔT. w , and respectively propose the arrester temperature variation curve T(t), leakage current variation curve LC(t) and working voltage variation curve U w (t);

[0016] At the same time, within the predetermined period ΔT, the response time Rt of the arrester to the overvoltage is obtained each time the arrester is subjected to the overvoltage. i , and the residual voltage Uv at both ends of the arrester i ;

[0017] By formula The risk assessment value Dr is obtained;

[0018] When Dr>Dr0, it is judged that the arrester has a hidden fault risk;

[0019] Where D T is the temperature deviation value, and D LC is the leakage current deviation value, and is the working voltage deviation value, and t1 is the start time of the scheduled cycle, t2 is the end time of the scheduled cycle, T0(t) is the curve of the standard temperature change of the arrester over time, The standard working voltage of the arrester changes with time;

[0020] Wherein, m is the total number of times the arrester withstands overvoltage within a predetermined period, and i∈[1, m], maxRt is the maximum response time within ΔT, minRt is the minimum response time within ΔT, ΔRt is the reference value of the response time, maxUv is the maximum residual voltage within ΔT, minUv is the minimum residual voltage within ΔT, ΔUv is the reference value of the residual voltage, and Dr0 is the preset risk assessment threshold.

[0021] Furthermore, the temperature information value T is obtained by:

[0022] The temperature sensor installed on the arrester is used to obtain the detection temperature T of the arrester. G At the same time, by obtaining the ambient temperature T near the arrester E And the light intensity L G ;

[0023] Thus, through the formula T = T G -(g(L G )+δ*T E ) obtains the temperature information value T;

[0024] Wherein, g is the preset light intensity conversion temperature function, and δ is the preset temperature influence coefficient of the arrester.

[0025] Furthermore, the method by which the risk index determination module determines the magnitude of the arrester failure risk index is:

[0026] A large amount of sample data is collected and trained through a neural network model to obtain a fault risk index set, wherein the fault risk index set corresponds to risk index levels of one, two, three, four, five, and five, and the risk index levels are gradually serious;

[0027] Each risk index level corresponds to a corresponding risk assessment value interval. The obtained risk assessment value is input into the corresponding fault risk index set. When the risk assessment value falls into the corresponding risk assessment value interval, the risk index level corresponding to the risk assessment value area is determined as the current fault risk index.

[0028] Furthermore, the method by which the life prediction module evaluates and predicts the service life of the arrester is:

[0029] Get the number of acid rain occurrences S in the lightning arrester installation area in the historical period AR , the number of strong winds S WD , the number of high temperature occurrences S HT 、Number of low temperatures S LT And the number of high humidity occurrences S HS ;

[0030] At the same time, in the historical time, according to the risk assessment value of each collection, Dr j , formulate the curve equation Dr(x) of risk assessment value changing with the number of times;

[0031] By formula Obtain the health status value B;

[0032] Based on the material properties and operating environment of the arrester, sample data is collected to establish a life prediction model for the arrester. The acquired health status value is input into the life prediction model to obtain the current estimated life;

[0033] Where n is the total number of times risk assessment values ​​are collected in the historical time, and j∈[1,n], x1 is the first collection in the historical time, and x2 is the last collection in the historical time.

[0034] A method for estimating a distribution network arrester fault risk index, wherein the method is executed by the distribution network arrester fault risk index estimating system.

[0035] Beneficial effects of the present invention:

[0036] When the arrester has no obvious failure risk, the present invention can judge the hidden failure risk of the arrester that is still working normally. Once the hidden failure risk is found, timely warning reminder is given, so that the arrester can be processed accordingly in time, thereby ensuring the safe and stable operation of the power equipment; in addition, when it is judged that the arrester has hidden failure risk, the arrester failure risk index can be determined according to the risk assessment value through the risk index determination module, so that the management personnel can timely understand the potential failure risk level of the arrester, thereby performing corresponding management operations;

[0037] When it can be determined that there is no hidden fault risk in the lightning arrester, the present invention uses the life prediction module to evaluate and predict the service life of the lightning arrester according to the acquired environmental information and the risk assessment value. In this way, a more reasonable maintenance plan can be formulated for the lightning arrester that is operating normally to avoid unnecessary frequent maintenance or premature replacement. By evaluating and predicting the remaining life of the lightning arrester, potential fault risks can be discovered in advance, and corresponding preventive measures can be taken to improve the reliability of the power system.

[0038] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0040] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] In one embodiment, a distribution network arrester fault risk index prediction system is disclosed, such as Figure 1 As shown, the estimation system includes:

[0043] Arrester parameter acquisition module, the arrester parameter acquisition module is used to obtain the associated parameter information of the arrester when it is working, the associated parameter information includes the detection temperature, leakage current, working voltage, the response time of the arrester to the overvoltage each time it is subjected to overvoltage, and the residual pressure at both ends of the arrester;

[0044] Environmental parameter acquisition module, which is used to obtain environmental information of the lightning arrester installation area, including ambient temperature information, light intensity information, humidity information and abnormal weather information;

[0045] The service management end is used to receive the associated parameter information and environmental information, generate a risk assessment value after corresponding processing and analysis, and judge whether there is a hidden fault risk of the lightning arrester according to the risk assessment value;

[0046] The risk index determination module determines the arrester fault risk index size according to the risk assessment value when it is determined that the arrester has a hidden fault risk;

[0047] The life prediction module evaluates and predicts the service life of the arrester based on the acquired environmental information and risk assessment value when it is determined that the arrester does not have a hidden fault risk.

[0048] The display reminder module is connected to the corresponding terminal to transmit the real-time status of the lightning arrester and the fault risk index to the corresponding terminal for reminder.

[0049] Through the above technical scheme, the present application obtains the associated parameter information of the arrester when it is working, including the detection temperature, leakage current, working voltage, response time of the arrester to overvoltage each time it is subjected to overvoltage, and residual pressure at both ends of the arrester, etc., and then obtains the environmental information of the installation area of ​​the arrester through the environmental parameter acquisition module, including ambient temperature information, light intensity information, humidity information, abnormal weather information, etc. After processing and analysis by the service management end, a risk assessment value is generated, and the risk of hidden fault risk of the arrester is judged according to the size of the risk assessment value. In this way, when there is no obvious fault risk in the arrester, the hidden fault risk of the arrester that is still working normally can be judged. Once the hidden fault risk is found, a timely early warning reminder is issued, so that the arrester can be timely operated. Corresponding processing is carried out to ensure the safe and stable operation of the power equipment; in addition, when it is judged that the lightning arrester has a hidden fault risk, the risk index determination module can also be used to determine the size of the lightning arrester fault risk index according to the size of the risk assessment value, so that the management personnel can timely understand the potential fault hazard level of the lightning arrester, and thus carry out corresponding management operations; when it can be judged that the lightning arrester does not have a hidden fault risk, the life prediction module is used to evaluate and predict the service life of the lightning arrester according to the obtained environmental information and the risk assessment value, so that a more reasonable maintenance plan can be formulated for the lightning arrester that is operating normally, avoiding unnecessary frequent maintenance or premature replacement, and by evaluating and predicting the remaining life of the lightning arrester, potential failure risks can be discovered in advance, and corresponding preventive measures can be taken to improve the reliability of the power system.

[0050] As an embodiment of the present invention, the method for obtaining the risk assessment value is: when the arrester is monitored to be operating normally, the temperature information value T, the leakage current information value LC and the working voltage information value U of the arrester are obtained within a predetermined period ΔT. w , and respectively propose the arrester temperature variation curve T(t), leakage current variation curve LC(t) and working voltage variation curve U w (t);

[0051] At the same time, within the predetermined period ΔT, the response time Rt of the arrester to the overvoltage is obtained each time the arrester is subjected to the overvoltage. i , and the residual voltage Uv at both ends of the arrester i ;

[0052] By formula The risk assessment value Dr is obtained;

[0053] When Dr>Dr0, it is judged that the arrester has a hidden fault risk;

[0054] Where D Tis the temperature deviation value, and D LC is the leakage current deviation value, and is the working voltage deviation value, and t1 is the start time of the scheduled cycle, t2 is the end time of the scheduled cycle, T0(t) is the curve of the standard temperature change of the arrester over time, The standard working voltage of the arrester changes with time;

[0055] Wherein, m is the total number of times the arrester withstands overvoltage within a predetermined period, and i∈[1, m], maxRt is the maximum response time within ΔT, minRt is the minimum response time within ΔT, ΔRt is the reference value of the response time, maxUv is the maximum residual voltage within ΔT, minUv is the minimum residual voltage within ΔT, ΔUv is the reference value of the residual voltage, and Dr0 is the preset risk assessment threshold.

[0056] Through the above technical solution, this embodiment provides a method for obtaining a risk assessment value and a method for assessing whether a lightning arrester has a hidden fault risk. First, when the lightning arrester is monitored to be operating normally, a predetermined detection period ΔT is manually set. Within the predetermined period ΔT, the temperature information value T, the leakage current information value LC, and the working voltage information value U of the lightning arrester at both ends are obtained. w , and respectively propose the arrester temperature variation curve T(t), leakage current variation curve LC(t) and working voltage variation curve U w (t), and obtain the response time Rt of the arrester to the overvoltage each time it is subjected to the overvoltage within the predetermined period ΔT i , and the residual voltage Uv at both ends of the arrester i ; Then through the formula

[0057] The risk assessment value Dr is obtained, where D T is the temperature deviation value, and D LC is the leakage current deviation value, and is the working voltage deviation value, and Generally speaking, a stable operating arrester has small fluctuations in operating temperature, small leakage current, and small fluctuations in operating voltage. Therefore, the formula It indicates the difference between the arrester temperature and the standard temperature. The larger the value, the greater the temperature fluctuation. The formula It shows the leakage current of the arrester. The larger the value, the higher the formula It indicates the difference between the arrester working voltage and the standard voltage. The larger the value, the greater the fluctuation of the working voltage. So when the formula The larger the value, the greater the risk of failure of the arrester. It indicates the response time and residual voltage of the arrester within a predetermined period. The longer the response time or the more residual voltage is left, the greater the possibility of failure risk of the arrester. Therefore, the risk assessment value is obtained through comprehensive analysis through the formula. The obtained risk assessment value is compared with the preset risk assessment threshold in the system. When Dr>Dr0, it is judged that the arrester has hidden fault risk. In this way, a comprehensive analysis can be conducted based on the temperature fluctuation, working voltage fluctuation, leakage current, residual voltage and each response time of the arrester to determine whether the arrester has hidden fault risk. The working condition of the arrester can be understood more accurately, so that corresponding measures can be managed to effectively ensure the safe and stable operation of power equipment.

[0058] In the above technical solution, the standard temperature variation curve of the arrester T0(t) and the standard working voltage variation curve of the arrester The response time reference value ΔRt, the residual voltage reference value ΔUv, and the preset risk assessment threshold Dr0 can all be determined based on the historical operation data of the lightning arrester combined with empirical data, and will not be described in detail here.

[0059] As an implementation of the present invention, a method for obtaining the temperature information value T is:

[0060] The temperature sensor installed on the arrester is used to obtain the detection temperature T of the arrester. G At the same time, by obtaining the ambient temperature T near the arrester E And the light intensity L G ;

[0061] Thus, through the formula T = T G -(g(L G )+δ*T E ) obtains the temperature information value T;

[0062] Wherein, g is the preset light intensity conversion temperature function, and δ is the preset temperature influence coefficient of the arrester.

[0063] Through the above technical solution, this embodiment provides a specific method for obtaining the temperature information value. Since the detected arrester temperature is the body surface temperature, the actual temperature of the arrester is affected by the light intensity and the ambient temperature. The parameter error, in order to more accurately obtain the actual working heating temperature of the arrester, this embodiment first obtains the detection temperature of the arrester through the temperature sensor installed on the arrester, and then obtains the ambient temperature T near the arrester. E And the light intensity L GAccording to the relevant historical data in the big database and the material selection of the lightning arrester, a light intensity conversion temperature function and temperature influence coefficient that are consistent with the lightning arrester are preset, and then the formula T = T G -(g(L G )+δ*T E ) to obtain the temperature information value T; in this way, the influence of the heat generated by the ambient temperature and light intensity on the heating temperature of the arrester can be eliminated, making the detection temperature more accurate, so that the arrester can be detected more accurately.

[0064] As an implementation mode of the present invention, the method for the risk index determination module to determine the magnitude of the arrester failure risk index is:

[0065] A large amount of sample data is collected and trained through a neural network model to obtain a fault risk index set, which corresponds to five levels of risk index: one, two, three, four, five, and the risk index levels gradually become more serious;

[0066] Each risk index level corresponds to a corresponding risk assessment value interval. The obtained risk assessment value is input into the corresponding fault risk index set. When the risk assessment value falls into the corresponding risk assessment value interval, the risk index level corresponding to the risk assessment value area is determined as the current fault risk index.

[0067] Through the above technical scheme, this embodiment provides a method for determining the size of the arrester fault risk index. First, a large amount of sample data is collected and trained through a neural network model to obtain a fault risk index set. The fault risk index set corresponds to risk index levels of one, two, three, four, five, and five levels, and the risk index levels are gradually serious. Each risk index level corresponds to a corresponding risk assessment value interval. The obtained risk assessment value is input into the corresponding fault risk index set. When the risk assessment value falls into the corresponding risk assessment value interval, the risk index level corresponding to the risk assessment value area is determined as the current fault risk index. In this way, management personnel can timely understand the potential fault hazard level of the arrester according to the size of the fault risk index, and thus perform corresponding management operations.

[0068] As an implementation mode of the present invention, the method for evaluating and predicting the service life of the arrester by the life prediction module is:

[0069] Get the number of acid rain occurrences S in the lightning arrester installation area in the historical period AR , the number of strong winds S WD , the number of high temperature occurrences S HT 、Number of low temperatures S LT And the number of high humidity occurrences S HS ;

[0070] At the same time, in the historical time, according to the risk assessment value of each collection, Dr j , formulate the curve equation Dr(x) of risk assessment value changing with the number of times;

[0071] By formula Obtain the health status value B;

[0072] Based on the material properties and operating environment of the arrester, sample data is collected to establish a life prediction model for the arrester. The acquired health status value is input into the life prediction model to obtain the current estimated life;

[0073] Where n is the total number of times risk assessment values ​​are collected in the historical time, and j∈[1,n], x1 is the first collection in the historical time, and x2 is the last collection in the historical time.

[0074] Through the above technical solution, this embodiment provides a method for evaluating and predicting the service life of a lightning arrester by a life prediction module. First, when there is no potential failure risk of the lightning arrester, a historical time period is set, which can be a month, a quarter, or even a year, and the number of acid rain occurrences in the lightning arrester installation area in the historical time period is obtained. AR , the number of strong winds S WD , the number of high temperature occurrences S HT 、Number of low temperatures S LT And the number of high humidity occurrences S HS At the same time, in the historical time, according to the risk assessment value of each collection, Dr j , the risk assessment value versus frequency curve equation Dr(x) is proposed; since acid rain, strong wind, high and low temperature and humidity will affect the arrester, and the larger the value, the greater the loss of the arrester, so these parameter information is obtained through The health status value B is obtained. The formula represents the cumulative change of the risk assessment value during the historical time. The larger it is, the greater the impact on the arrester. The same formula It indicates the stability of the risk assessment value obtained in the historical time. The larger and more unstable its value is, the greater the impact on the service life of the arrester is. Therefore, a comprehensive analysis is conducted to obtain the health status value of the normally working arrester. Then, based on the material properties and operating environment of the arrester, sample data is collected to establish a life prediction model for the arrester. The obtained health status value is input into the life prediction model to obtain the current estimated life. In this way, by predicting and evaluating the life of the arrester, a more reasonable maintenance plan can be formulated for the normally operating arrester to avoid unnecessary frequent maintenance or premature replacement. By evaluating and predicting the remaining life of the arrester, potential failure risks can be discovered in advance, and corresponding preventive measures can be taken to improve the reliability of the power system.

[0075] A method for estimating a distribution network arrester fault risk index, the method being executed by the above-mentioned distribution network arrester fault risk index estimation system.

[0076] It should be noted that the calculation formulas and various parameters involved in the calculations in the present invention have been dimensionally processed in advance, and the process of dimensionless processing is well known in the industry and will not be described here.

[0077] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A distribution network arrester fault risk index prediction system, characterized in that: The system comprises: A lightning arrester parameter acquisition module, which is used to obtain associated parameter information of the lightning arrester when it is working; An environmental parameter acquisition module, which is used to obtain environmental information of the lightning arrester installation area; A service management terminal, which is used to receive the acquired associated parameter information and environmental information, generate a risk assessment value after corresponding processing and analysis, and judge whether there is a hidden fault risk of the lightning arrester according to the risk assessment value; The risk index determination module determines the arrester fault risk index size according to the risk assessment value when it is determined that the arrester has a hidden fault risk; The life prediction module evaluates and predicts the service life of the arrester based on the acquired environmental information and risk assessment value when it is determined that the arrester does not have a hidden fault risk. The display reminder module is connected to the corresponding terminal to transmit the real-time status of the lightning arrester and the fault risk index to the corresponding terminal for reminder.

2. A distribution network arrester fault risk index prediction system according to claim 1, characterized in that: The associated parameter information includes the detection temperature, leakage current, operating voltage, response time of the arrester to overvoltage each time it is subjected to overvoltage, and residual pressure at both ends of the arrester when the arrester is working; the environmental information includes ambient temperature information, light intensity information, humidity information, and abnormal weather information.

3. A distribution network arrester fault risk index prediction system according to claim 2, characterized in that: The method for obtaining the risk assessment value is: When the arrester is monitored to be operating normally, the temperature information value T, the leakage current information value LC and the working voltage information value U of the arrester are obtained within a predetermined period ΔT. w , and respectively propose the arrester temperature variation curve T(t), leakage current variation curve LC(t) and working voltage variation curve U w (t); At the same time, within the predetermined period ΔT, the response time Rt of the arrester to the overvoltage is obtained each time the arrester is subjected to the overvoltage. i , and the residual voltage Uv at both ends of the arrester i ; By formula The risk assessment value Dr is obtained; When Dr>Dr0, it is judged that the arrester has a hidden fault risk; Where D T is the temperature deviation value, and D LC is the leakage current deviation value, and is the working voltage deviation value, and t1 is the start time of the scheduled cycle, t2 is the end time of the scheduled cycle, T0(t) is the curve of the standard temperature change of the arrester over time, The standard working voltage of the arrester changes with time; Wherein, m is the total number of times the arrester withstands overvoltage within a predetermined period, and i∈[1, m], maxRt is the maximum response time within ΔT, minRt is the minimum response time within ΔT, ΔRt is the reference value of the response time, maxUv is the maximum residual voltage within ΔT, minUv is the minimum residual voltage within ΔT, ΔUv is the reference value of the residual voltage, and Dr0 is the preset risk assessment threshold.

4. A distribution network arrester fault risk index prediction system according to claim 3, characterized in that: The method for obtaining the temperature information value T is: The temperature sensor installed on the arrester is used to obtain the detection temperature T of the arrester. G At the same time, by obtaining the ambient temperature T near the arrester E And the light intensity L G ; Thus, through the formula T = T G -(g(L G )+δ*T E ) obtains the temperature information value T; Wherein, g is the preset light intensity conversion temperature function, and δ is the preset temperature influence coefficient of the arrester.

5. A distribution network arrester fault risk index prediction system according to claim 3, characterized in that: The method for the risk index determination module to determine the arrester fault risk index is: A large amount of sample data is collected and trained through a neural network model to obtain a fault risk index set, wherein the fault risk index set corresponds to risk index levels of one, two, three, four, five, and five, and the risk index levels are gradually serious; Each risk index level corresponds to a corresponding risk assessment value interval. The obtained risk assessment value is input into the corresponding fault risk index set. When the risk assessment value falls into the corresponding risk assessment value interval, the risk index level corresponding to the risk assessment value area is determined as the current fault risk index.

6. A distribution network arrester fault risk index prediction system according to claim 3, characterized in that: The method for evaluating and predicting the service life of the arrester by the life prediction module is: Get the number of acid rain occurrences S in the lightning arrester installation area in the historical period AR , the number of strong winds S WD , the number of high temperature occurrences S HT 、Number of low temperatures S LT And the number of high humidity occurrences S HS ; At the same time, in the historical time, according to the risk assessment value of each collection, Dr j , formulate the curve equation Dr(x) of risk assessment value changing with the number of times; By formula Obtain the health status value B; Based on the material properties and operating environment of the arrester, sample data is collected to establish a life prediction model for the arrester. The acquired health status value is input into the life prediction model to obtain the current estimated life; Where n is the total number of times risk assessment values ​​are collected in the historical time, and j∈[1,n], x1 is the first collection in the historical time, and x2 is the last collection in the historical time.

7. A method for estimating the fault risk index of lightning arresters in a distribution network, characterized in that: The estimation method is executed by the distribution network lightning arrester fault risk index estimation system according to any one of claims 1 to 6.

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