Offshore wind plant lightning arrester annual failure rate evaluation method considering lightning randomness

Abstract

The invention discloses an offshore wind plant lightning arrester annual failure rate evaluation method considering lightning randomness, and belongs to the field of offshore wind plant lightning protection, and the method comprises the following steps: S1, determining easy-to-out-of-limit electrical parameters; s2, based on the easy-to-out-of-limit electrical parameters, calculating the failure probability of the lightning arrester under single lightning stroke; s3, obtaining a lightning current amplitude cumulative probability; s4, obtaining a lightning arrester interval failure probability considering the lightning current amplitude cumulative probability; and S5, calculating the annual lightning stroke failure rate of the lightning arrester of the offshore wind plant based on the interval failure probability of the lightning arrester by considering the lightning inducing area of the fan of the offshore wind plant. According to the offshore wind plant lightning arrester annual failure rate evaluation method considering lightning randomness, lightning randomness is introduced, failure quantitative calculation and multi-parameter comprehensive analysis are achieved, lightning arrester model selection is supported, and technical upgrading of offshore wind plant lightning arrester reliability analysis from qualitative analysis to quantitative analysis and from theory to application is completed.

Description

Technical Field

[0001] The present invention relates to the technical field of lightning protection for offshore wind farms, and particularly to a method for evaluating the annual failure rate of arresters in offshore wind farms considering the randomness of lightning. Background Art

[0002] Offshore wind power has been developing rapidly as an important part of clean energy. Among them, arresters, as the core lightning protection equipment of the electrical system of offshore wind turbines, their operating reliability directly affects the safe and stable operation of wind farms.

[0003] To determine its stability, the prior art needs to analyze the electrical stress of the arrester, and the analysis means are as follows: (1) Analysis object: a single arrester under specific working conditions; (2) Evaluation index: whether the voltage, current, and energy exceed the limit; (3) Simulation means: using electromagnetic transient software such as PSCAD / EMTP; (4) Failure criterion: deterministic analysis based on threshold comparison.

[0004] Since the construction of offshore wind farms changes the local atmospheric electric field and affects the characteristics of cloud-to-ground lightning activity, and the lightning parameters (lightning current amplitude, lightning return stroke type) have significant randomness, the existing analysis means have the following defects: 1. Evaluation limitation: Only for a single arrester, it cannot reflect the overall lightning protection performance of the wind farm; 2. Defect of calculation model: Ignoring the probability distribution characteristics of lightning parameters and lacking differential analysis of different return stroke orders; 3. Insufficient engineering application: Unable to quantify the annual failure rate index of arresters in offshore wind farms, making it difficult to support the optimization decision of arrester selection. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for evaluating the annual failure rate of arresters in offshore wind farms considering the randomness of lightning to solve the above technical problems.

[0006] To achieve the above purpose, the present invention provides a method for evaluating the annual failure rate of arresters in offshore wind farms considering the randomness of lightning, including the following steps: S1. Build a lightning electromagnetic transient simulation model of an offshore wind turbine, and conduct lightning simulations under multiple return stroke orders to obtain the change results of the electrical stress parameters of the arrester in the offshore wind turbine, and based on the change results of the electrical stress parameters of the arrester, confirm the electrical parameters prone to exceeding the limit; S2. Based on the electrical parameters prone to exceeding the limit, use the Weibull distribution to calculate the failure probability of the arrester for a single lightning strike; S3. Based on the data of the lightning location system, obtain the statistical results of offshore lightning parameters, and obtain the cumulative probability of lightning current amplitude based on the statistical results of offshore lightning parameters; S4. Divide the lightning current into intervals, and based on the median lightning current of the divided intervals, combine the probability of a single lightning strike arrester failure described in step S2 and the cumulative probability of lightning current amplitude described in step S3 to obtain the arrester interval failure probability considering the cumulative probability of lightning current amplitude; S5. Considering the lightning attraction area of the wind turbines in the offshore wind farm, calculate the annual lightning strike failure rate of the arresters in the offshore wind farm based on the arrester interval failure probability described in step S4.

[0007] Preferably, in step S1, use PSCAD electromagnetic transient simulation software for segmented modeling to obtain the lightning electromagnetic transient simulation model of the offshore wind turbine: Establish a chain equivalent circuit for the blade downlead, nacelle, tower barrel, and cable of the offshore wind turbine respectively; Model the arrester and surge protector using nonlinear resistors; Model the transformer using a high-frequency model; Use the ground capacitance to equivalent the high-frequency characteristics during the lightning strike of the switchgear at the bottom of the tower barrel; Use Comsol finite element software to calculate the coupling capacitance between the tower barrel and the cable armor layer, between the cable armor layer and the shielding layer, between the cable shielding layers, and between the cable shielding layer and the core wire; Use the Heidler function to simulate the lightning current waveform, and set the lightning current waveform corresponding to a single positive lightning strike to 22 / 230 μs, the lightning current waveform corresponding to a single negative lightning strike to 2.6 / 50 μs, the lightning current waveform corresponding to the first positive return stroke to 10 / 350 μs, the lightning current waveform corresponding to the first negative return stroke to 1 / 200 μs, and the lightning current waveforms corresponding to both positive and negative subsequent return strokes to 0.25 / 100 μs; The electrical stress parameters described in step S1 include voltage, current, and energy; the electrical parameters prone to exceeding the limit are determined based on the critical values of voltage, current, and energy, including voltage and current.

[0008] Preferably, in step S2, set the divided interval of the lightning current to 5 kA, and divide it into intervals. At this time, conduct lightning strike simulations for the th interval and the th return stroke order, and use the Weibull distribution to calculate the probability of a single lightning strike arrester failure for the th interval and the th return stroke order , (1); Wherein, (2); (3); In the formula, For the th interval and the th lightning arrester voltage amplitude under the th lightning strike sequence, when the lightning arrester voltage amplitude exceeds the residual voltage under the nominal discharge current of the lightning arrester, the voltage failure probability of the lightning arrester for a single lightning strike; For the th interval and the th lightning strike sequence, when the lightning current amplitude exceeds the nominal discharge current of the lightning arrester , the current failure probability of the lightning arrester for a single lightning strike; is an intermediate quantity.

[0009] Preferably, step S3 specifically includes the following steps: S31. Use the Arcgis geographic software to screen the lightning current data in the sea area where the offshore wind farm is located from the lightning location system data, record each return stroke as a single lightning strike, and count the return stroke density in the corresponding area, the proportion of various return stroke sequences, and the lightning current amplitude characteristics corresponding to each return stroke sequence; Among them, the proportion of each return stroke sequence includes the proportion of positive single lightning strikes , the proportion of negative single lightning strikes , the proportion of positive first return strokes , the proportion of negative first return strokes , the proportion of positive subsequent return strokes , the proportion of negative subsequent return strokes ; S32. Use the cumulative probability function of lightning current amplitude in IEEE to fit the lightning current amplitude corresponding to each return stroke sequence to obtain the cumulative probability of the lightning current amplitude, where .

[0010] Preferably, the expression of the cumulative probability function of the lightning current amplitude described in step S32 is as follows: (4); In the formula, represents the probability that the lightning current amplitude in the th return stroke sequence exceeds the critical lightning current ; is the lightning current amplitude parameter; is the shape parameter; Thus, the proportion of positive single lightning strikes , the proportion of negative single lightning strikes , the proportion of positive first return strokes , the proportion of negative first return strokes , the proportion of positive subsequent return strokes 、 Cumulative probability of subsequent negative lightning strikes under the lightning current amplitude 、 、 、 、 、 。

[0011] Preferably, in step S4, based on step S2, the proportion of positive single lightning strikes 、 the proportion of negative single lightning strikes 、 the proportion of positive first return strokes 、 the proportion of negative first return strokes 、 the proportion of positive subsequent return strokes 、 the proportion of negative subsequent return strokes under 、 、 、 、 、 ; combined with the cumulative probability of lightning current amplitude described in step S3, calculate the probability of the arrester failure interval in the th interval and the th return stroke order considering the cumulative probability of lightning current amplitude : (5); In the formula, and are respectively the upper and lower limits of the lightning current in the th interval.

[0012] Preferably, step S5 specifically includes the following steps: S51. Set the lightning attracting area of a single wind turbine as the circular area with the wind turbine as the center and three times the height of the wind turbine as the radius. Obtain the lightning attracting area of a single wind turbine based on the circular area calculation formula, and then multiply it by the number of wind turbines in the offshore wind farm to get the total lightning attracting area ; S52. Based on the return stroke density and the total lightning attracting area , calculate the annual lightning strike times of the wind turbines in the offshore wind farm: (6); S53. Combined with the probability of the arrester failure interval , calculate the annual lightning strike failure rate of the arresters in the offshore wind farm: (7).

[0013] Therefore, the annual failure rate evaluation method of the arrester for the offshore wind farm considering the randomness of lightning adopted by the present invention has the following beneficial effects: 1. Considering the randomness of lightning: Breaking through the traditional single operating condition analysis, integrating the random characteristics such as lightning current amplitude and stroke order, which is closer to the actual lightning strike scenario; 2. Quantitative evaluation of failure: Quantifying the failure rate with the help of models such as Weibull distribution, carrying out quantitative failure evaluation for the arresters of the entire wind farm, changing the traditional local and qualitative evaluation mode, and improving the analysis accuracy; 3. Comprehensive analysis of multiple parameters: Combining the over-limit parameters of voltage and current to calculate the failure probability, comprehensively covering the influencing factors of arrester failure, and making the evaluation more scientific.

[0014] The technical solution of the present invention will be further described in detail below with reference to the drawings and embodiments. Description of the Drawings

[0015] Figure 1 It is a flowchart of an annual failure rate evaluation method of an arrester for an offshore wind farm considering the randomness of lightning according to the present invention; Figure 2 It is an equivalent circuit diagram of a lightning strike electromagnetic transient simulation model of an offshore wind turbine for the simulation experiment of the present invention; Figure 3 It is a curve graph of the change of electrical stress parameters for the simulation experiment of the present invention, where (a) is the curve graph of the arrester voltage change, (b) is the curve graph of the arrester current change, and (c) is the curve graph of the arrester energy change. Detailed Embodiments

[0016] In order to make the purpose, technical solution and advantages of the embodiments disclosed by the present invention clearer, the following further details the embodiments of the present invention with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of this application. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout.

[0017] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0018] The following will describe the embodiments of the present invention in detail with reference to the accompanying drawings.

[0019] As Figure 1 shown, a method for evaluating the annual failure rate of lightning arresters in an offshore wind farm considering the randomness of lightning includes the following steps: S1. Build a lightning electromagnetic transient simulation model for an offshore wind turbine, conduct lightning simulations under various stroke sequences, obtain the variation results of the electrical stress parameters of the lightning arrester in the offshore wind turbine, and based on the variation results of the electrical stress parameters of the lightning arrester, confirm the electrical parameters prone to exceeding the limit; In step S1, use PSCAD electromagnetic transient simulation software to perform segmented modeling to obtain a lightning electromagnetic transient simulation model for an offshore wind turbine: Establish a chain equivalent circuit for the blade downlead, nacelle, tower barrel, and cable of the offshore wind turbine respectively; Model the lightning arrester and surge protector using non-linear resistors; Model the transformer using a high-frequency model; Use the ground capacitance to equivalent the high-frequency characteristics during the lightning strike of the switchgear at the bottom of the tower barrel; Use Comsol finite element software to calculate the coupling capacitance between the tower barrel and the cable armor layer, between the cable armor layer and the shielding layer, between the cable shielding layers, and between the cable shielding layer and the core wire; Use the Heidler function to simulate the lightning current waveform, and set the lightning current waveform corresponding to a single positive lightning strike to 22 / 230 μs, the lightning current waveform corresponding to a single negative lightning strike to 2.6 / 50 μs, the lightning current waveform corresponding to the first positive return stroke to 10 / 350 μs, the lightning current waveform corresponding to the first negative return stroke to 1 / 200 μs, and the lightning current waveforms corresponding to subsequent positive and negative return strokes to 0.25 / 100 μs; The electrical stress parameters described in step S1 include voltage, current, and energy; determining the electrical parameters prone to exceeding the limit based on the critical values of voltage, current, and energy includes voltage and current.

[0020] S2. Based on the electrical parameters prone to exceeding the limit, use the Weibull distribution to calculate the failure probability of the lightning arrester for a single lightning strike; In step S2, set the division interval of the lightning current to 5 kA, and divide it into a total of intervals. At this time, conduct a lightning simulation for the th interval and the th stroke sequence, and use the Weibull distribution to calculate the failure probability of the lightning arrester for a single lightning strike in the th interval and the th stroke sequence , (1); Wherein, (2); (3); Wherein, is the -th interval and the -th lightning arrester voltage amplitude under the -th lightning strike sequence, which exceeds the residual voltage under the rated discharge current of the lightning arrester (i.e., the critical lightning voltage) single lightning strike lightning arrester voltage failure probability; is the -th interval and the -th lightning arrester current amplitude under the -th lightning strike sequence, which exceeds the rated discharge current of the lightning arrester (i.e., the critical lightning current) single lightning strike lightning arrester current failure probability; is an intermediate quantity.

[0021] S3. Based on the data of the lightning location system, obtain the statistical results of the offshore lightning parameters, and obtain the cumulative probability of the lightning current amplitude based on the statistical results of the offshore lightning parameters; Step S3 specifically includes the following steps: S31. Use the Arcgis geographic software to screen the lightning current data in the sea area where the offshore wind farm is located in the lightning location system data, and record each lightning strike as a single lightning strike, and count the strike density of the corresponding area, the proportion of various lightning strike sequences, and the lightning current amplitude characteristics corresponding to each lightning strike sequence; Among them, the proportion of each lightning strike sequence includes the proportion of positive single lightning strikes , the proportion of negative single lightning strikes , the proportion of positive first lightning strikes , the proportion of negative first lightning strikes , the proportion of positive subsequent lightning strikes , the proportion of negative subsequent lightning strikes ; S32. Use the cumulative probability function of the lightning current amplitude in IEEE to fit the lightning current amplitude corresponding to each lightning strike sequence to obtain the cumulative probability of the lightning current amplitude, where .

[0022] The expression of the cumulative probability function of the lightning current amplitude described in step S32 is as follows: (4); Wherein, represents the probability that the lightning current amplitude in the -th lightning strike sequence exceeds the critical lightning current ; is the lightning current amplitude parameter; is the shape parameter; Thus, the cumulative probabilities of positive single - strike ratio , negative single - strike ratio , positive first - return stroke ratio , negative first - return stroke ratio , positive subsequent - return stroke ratio , and negative subsequent - return stroke ratio are obtained at the lightning current amplitude, , , , , , .

[0023] S4. Divide the lightning current into intervals, and based on the median lightning current of the divided intervals, combine the single - strike arrester failure probability described in step S2 and the lightning current amplitude cumulative probability described in step S3 to obtain the arrester interval failure probability considering the lightning current amplitude cumulative probability; In step S4, based on step S2, the positive single - strike ratio , negative single - strike ratio , positive first - return stroke ratio , negative first - return stroke ratio , positive subsequent - return stroke ratio , and negative subsequent - return stroke ratio at , , , , , ; combined with the lightning current amplitude cumulative probability described in step S3, calculate the arrester failure interval probability considering the lightning current amplitude cumulative probability in the th interval and the th return - stroke order: (5); In the formula, and are the upper and lower limits of the lightning current in the th interval respectively.

[0024] S5. Considering the lightning - attracting area of the wind turbines in the offshore wind farm, calculate the annual lightning failure rate of the arresters in the offshore wind farm based on the arrester interval failure probability described in step S4.

[0025] Step S5 specifically includes the following steps: S51. Set the lightning attraction area of a single wind turbine as the circular area with the wind turbine as the center and three times the height of the wind turbine as the radius. Obtain the lightning attraction area of a single wind turbine based on the circular area calculation formula, and then multiply it by the number of wind turbines in the offshore wind farm to get the total lightning attraction area. ; S52. Based on the return stroke density and the total lightning attraction area , calculate the annual lightning strike times of the wind turbines in the offshore wind farm : (6); S53. Combine the probability of the failure interval of the arrester , and calculate the annual lightning strike failure rate of the arrester in the offshore wind farm : (7).

[0026] Simulation experiment Use the design of a certain 35kV typical wind turbine in the offshore wind farm to build a lightning electromagnetic transient simulation model of the offshore wind turbine as shown in Figure 2 . Under the working condition of a lightning current amplitude of 200kA, carry out lightning strike simulations under 6 return stroke sequences, and conduct maximum voltage, current, and energy analysis for arrester #1 and arrester #2. The results are shown in Table 1.

[0027] Table 1 Electrical stress of the arrester ;

[0028] Set the critical values of voltage, current, and energy of the 35kV arrester to 134kV, 5kA, and 80.4kJ respectively. According to the simulation results in Table 1, under the working condition of a lightning current amplitude of 200kA, the voltage and current of the arrester are likely to exceed the threshold, while the energy is much less than the threshold (reason: taking the voltage, current, and energy of arrester #1 under 0.25 / 100μs as shown in Figure 3 as an example, it can be seen that although the voltage and current exceed the design threshold, due to the short transient time, the energy does not exceed the limit), so the voltage and current are taken as the electrical parameters that are likely to exceed the limit.

[0029] At the same time, through statistics, the return stroke density in this area is 12.68 times / (km 2 • a), and use the geographic software Arcgis to screen all the data of the offshore wind farms in the lightning location system in this area from 2010 to 2023 for ten years, and obtain the proportion of the 6 return stroke sequences shown in Table 2.

[0030] Table 2 Proportion of 6 return stroke sequences ;

[0031] Furthermore, fitting is performed using the lightning current amplitude cumulative probability function shown in Table 3.

[0032] Table 3 Lightning current amplitude cumulative probability function parameters ;

[0033] Then, taking the lower limit of the lightning current amplitude as 0 kA and the upper limit as 250 kA, with each interval being 5 kA, there are a total of 50 intervals. Using the median of each interval as the lightning current amplitude to represent the interval, the failure probability of the lightning arrester for a single lightning strike is calculated using formula (1). For example, under the first negative return stroke, the failure probabilities of the lightning arrester in the lightning current amplitude intervals [90 kA, 95 kA], [95 kA, 100 kA], [100 kA, 105 kA], and [105 kA, 110 kA] are the corresponding values in the 19th, 20th, 21st, and 22nd intervals shown in Table 4. , , and .

[0034] Table 4 Failure probability of lightning arrester for a single lightning strike ;

[0035] Using formula (5), the interval probability of the lightning arrester failure shown in Table 5 is further calculated.

[0036] Table 5 Interval probability of lightning arrester failure ;

[0037] At this time, setting the calculated result of the lightning attracting area of the wind turbines in this offshore wind farm to be 27.71 km 2 , the return stroke density is 12.68 times / (km 2 •year). Using formula (6), the annual lightning strike times of the wind turbines in this wind farm are calculated to be 351.3628 times. Then, using formula (7), the annual lightning strike failure rate of the lightning arresters in the offshore wind farm shown in Table 6 is calculated.

[0038] Table 6 Annual lightning strike failure rate of lightning arresters in offshore wind farm ;

[0039] Setting the nominal current of arrester #1 and arrester #2 to be 5 kA or 10 kA, the annual failure rate of the lightning arresters in the offshore wind farm under different arrester selection types is further compared, as shown in Table 7 below.

[0040] Table 7 Failure rates of arresters under different selection types ;

[0041] As can be seen from Table 7, by only changing the nominal current of one lightning arrester, the annual failure rate of the lightning arresters in this wind farm can be reduced by 70%, which proves that the calculation of the annual failure rate proposed in the present invention can also provide a theoretical basis for the selection of lightning arresters for offshore wind farms.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for evaluating the annual failure rate of lightning arresters in offshore wind farms considering the randomness of lightning, characterized by: The following steps are involved: S1. Build an electromagnetic transient simulation model for lightning strikes on offshore wind turbines, and perform lightning strike simulations under various return stroke sequences to obtain the results of changes in electrical stress parameters of lightning arresters in offshore wind turbines, and confirm electrical parameters that are prone to exceeding limits based on the results of changes in electrical stress parameters of lightning arresters; S2. Based on the easily exceeded electrical parameters, the failure probability of the arrester under a single lightning strike is calculated using the Weibull distribution; S3. Based on the data of the lightning location system, obtain the statistical results of offshore lightning parameters, and obtain the cumulative probability of lightning current amplitude based on the statistical results of offshore lightning parameters; S4, dividing the lightning current into intervals, and based on the median of the lightning current in the divided intervals, combined with the single lightning strike arrester failure probability described in step S2 and the lightning current amplitude cumulative probability described in step S3, obtaining the arrester interval failure probability considering the lightning current amplitude cumulative probability; S5. Considering the lightning induction area of ​​wind turbines in the offshore wind farm, the annual lightning failure rate of the lightning arrester in the offshore wind farm is calculated based on the interval failure probability of the lightning arrester described in step S4.

2. The method for evaluating the annual failure rate of lightning arresters in offshore wind farms considering the randomness of lightning according to claim 1 is characterized in that: In step S1, PSCAD electromagnetic transient simulation software is used to perform segmented modeling to obtain an offshore wind turbine lightning electromagnetic transient simulation model: Establish chain equivalent circuits for the offshore wind turbine blade downconductors, nacelles, towers and cables respectively; Modeling lightning arresters and surge protectors using nonlinear resistors; Modeling transformers using high-frequency models; Utilize the high-frequency characteristics of the lightning strike during the equivalent tower bottom switchgear capacitance to ground; Comsol finite element software is used to calculate the coupling capacitance between the tower and the cable armor layer, between the cable armor layer and the shield layer, between the shield layers of the cable, and between the cable shield layer and the core wire; The Heidler function is used to simulate the lightning current waveform, and the lightning current waveform corresponding to a single positive lightning stroke is set to 22 / 230μs, the lightning current waveform corresponding to a single negative lightning stroke is set to 2.6 / 50μs, the lightning current waveform corresponding to the first positive return stroke is set to 10 / 350μs, the lightning current waveform corresponding to the first negative return stroke is set to 1 / 200μs, and the lightning current waveform corresponding to subsequent positive and negative return strokes is set to 0.25 / 100μs. The electrical stress parameters described in step S1 include voltage, current, and energy; the electrical parameters that are easily exceeded based on the critical values ​​of voltage, current, and energy include voltage and current.

3. The method for evaluating the annual failure rate of lightning arresters in offshore wind farms considering the randomness of lightning according to claim 2 is characterized in that: In step S2, the lightning current division interval is set to 5kA, which is divided into interval, then conduct the Interval The lightning simulation under the return stroke sequence is carried out, and the Weibull distribution is used to calculate the Interval Failure probability of single lightning arrester under different return stroke sequences , （1）； in, （2）； （3）； In the formula, For the Interval Voltage amplitude of lightning arrester under different return stroke sequences Exceeding the residual voltage under the rated discharge current of the arrester The voltage failure probability of a single lightning strike arrester; For the Interval The current amplitude of the arrester under the return stroke sequence Exceeding the rated discharge current of the arrester The failure probability of a single lightning arrester current; An intermediate amount.

4. The method for evaluating annual failure rate of lightning arresters in offshore wind farms considering lightning randomness according to claim 3 is characterized in that: Step S3 specifically includes the following steps: S31. Use ArcGIS geographic software to filter the lightning current data of the sea area where the offshore wind farm is located in the lightning location system data, record each return stroke as a lightning strike, and count the return stroke density in the corresponding area. , the proportion of various return stroke sequences and the lightning current amplitude characteristics corresponding to each return stroke sequence; Among them, each return stroke sequence ratio includes the positive polarity single lightning stroke ratio , Negative polarity single lightning strike ratio , Positive polarity first strike ratio , Negative polarity first strike ratio , Positive polarity follow-up strike ratio , Negative polarity follow-up strike ratio ; S32. Using the lightning current amplitude cumulative probability function in IEEE, the lightning current amplitude corresponding to each return stroke sequence is fitted to obtain the cumulative probability of lightning current amplitude. ,in .

5. The method for evaluating annual failure rate of lightning arresters in offshore wind farms considering lightning randomness according to claim 4 is characterized in that: The lightning current amplitude cumulative probability function expression described in step S32 is as follows: （4）； In the formula, Indicates Lightning current amplitude under different return stroke sequences Exceeding critical lightning current The probability of is the lightning current amplitude parameter; is the shape parameter; Thus, the proportion of positive polarity single lightning strike is obtained , Negative polarity single lightning strike ratio , Positive polarity first strike ratio , Negative polarity first strike ratio , Positive polarity follow-up strike ratio , Negative polarity follow-up strike ratio Cumulative probability under lightning current amplitude , , , , , .

6. The method for evaluating annual failure rate of lightning arresters in offshore wind farms considering lightning randomness according to claim 5 is characterized in that: In step S4, based on step S2, the proportion of positive polarity single lightning strikes is obtained. , Negative polarity single lightning strike ratio , Positive polarity first strike ratio , Negative polarity first strike ratio , Positive polarity follow-up strike ratio , Negative polarity follow-up strike ratio Next , , , , , Combined with the cumulative probability of lightning current amplitude described in step S3 , calculate the Interval Considering the cumulative probability of lightning current amplitude under the return stroke sequence The probability of arrester failure interval : （5）； In the formula, and Respectively The upper and lower limits of lightning current in the interval.

7. The method for evaluating annual failure rate of lightning arresters in offshore wind farms considering lightning randomness according to claim 6 is characterized in that: Step S5 specifically includes the following steps: S51. Set the lightning attraction area of ​​a single wind turbine to the area of ​​a circle with the wind turbine as the center and three times the wind turbine height as the radius. Obtain the lightning attraction area of ​​a single wind turbine based on the circle area calculation formula, and then multiply it by the number of wind turbines in the offshore wind farm to obtain the total lightning attraction area. ; S52, based on the return density and total lightning area , calculate the annual number of lightning strikes on wind turbines in offshore wind farms : （6）； S53, combined with the arrester failure interval probability , calculate the annual lightning failure rate of lightning arresters in offshore wind farms : （7）。

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