Photovoltaic power station disaster damage assessment method and system

Abstract

The invention discloses a photovoltaic power station disaster damage assessment method and system, and the method comprises the steps: generating a simulated wind speed working condition set matched with a current photovoltaic power station based on the wind speed of strong wind and the data of the photovoltaic power station; traversing the simulated wind speed working condition set, calculating the loss probability contribution when each simulated wind speed working condition acts on the current photovoltaic power station, and counting the loss probability contributions corresponding to all the simulated wind speed working conditions to obtain the loss ratio probability of the photovoltaic power station; performing Bayesian updating iteration on the photovoltaic vulnerability prior distribution by using the loss ratio probability of the photovoltaic power station to obtain photovoltaic vulnerability posterior distribution as post-disaster loss probability distribution; the overall damage condition of the photovoltaic power station after the photovoltaic power station suffers from the wind disaster is evaluated, and reference is provided for typhoon disaster evaluation and typhoon prevention measures of the photovoltaic power station.

Description

Technical Field

[0001] This invention relates to the field of infrastructure disaster damage assessment technology, specifically to a method and system for assessing disaster damage to photovoltaic power plants. Background Technology

[0002] To improve the power generation efficiency of photovoltaic (PV) systems, photovoltaic (PV) panels are often large in area, and PV power plants typically install these panels in arrays. This results in a large windward area for the PV power plant, leading to significant structural responses when subjected to strong winds (such as typhoons). Strong winds acting on the surface of the PV system generate enormous wind pressure and suction, causing wind-induced vibrations. Strong winds can cause flutter and aeroelastic vibrations in the PV panels, easily leading to fatigue failure of the base support structures. Under the combined influence of wind load and wind vibration effects, PV systems are prone to structural damage, resulting in property loss, threats to personal safety, and serious economic losses and safety issues. Therefore, developing wind disaster risk assessment methods and disaster damage prediction techniques for PV systems is of great practical significance for supporting PV systems in resisting strong typhoon damage and assessing post-disaster losses of PV power plants.

[0003] Currently, researchers have conducted studies on the performance of photovoltaic (PV) systems under extreme weather conditions, but research on disaster assessment aspects such as PV loss rates and vulnerability curves under extreme wind disasters is still limited. Among related technologies, patent application CN117371307A proposes a Bayesian theory-based vulnerability modeling method for PV panels under typhoon conditions. This method primarily addresses the safety and operational status of PV modules during typhoons. It analyzes PV module failures, uses a log-normal function to simulate the prior wind speed distribution, and combines this with a failure likelihood function to establish a Bayesian formula. Then, it uses the Monte Carlo Markov algorithm to generate a posterior distribution model. However, it focuses on the causes of failures and disaster data at the module level, without addressing issues such as the overall loss distribution of large-scale PV arrays. The thesis "Study on Wind-Induced Vulnerability of Solar Photovoltaic Panels on Building Roofs, Master's Thesis of Harbin Institute of Technology" proposes a vulnerability curve generation method based on structural reliability theory, combined with static load tests and finite element simulation, using Monte Carlo sampling to construct a load-resistance probability model. The research focuses on photovoltaic panels on building roofs, studying their wind-induced vulnerability and providing specific optimization directions for the wind-resistant design of rooftop photovoltaics. However, it does not consider the overall wind-induced response and loss distribution characteristics of large photovoltaic arrays due to their array layout and large windward area. Summary of the Invention

[0004] The technical problem to be solved by this invention is how to assess the overall damage of a photovoltaic power station after a wind disaster, so as to provide a reference for typhoon disaster assessment and typhoon prevention measures for photovoltaic power stations.

[0005] The present invention solves the above-mentioned technical problems through the following technical means:

[0006] A method for assessing the disaster damage of photovoltaic power plants is proposed, the method comprising:

[0007] Based on strong wind speed and photovoltaic power station data, a set of simulated wind speed conditions matching the current photovoltaic power station is generated.

[0008] Traverse the set of simulated wind speed conditions, calculate the loss probability contribution of each simulated wind speed condition to the current photovoltaic power station, and statistically analyze the loss probability contribution of all simulated wind speed conditions to obtain the loss ratio probability of the photovoltaic power station.

[0009] By using the loss ratio probability of photovoltaic power plants to perform Bayesian update iteration on the prior distribution of photovoltaic vulnerability, the posterior distribution of photovoltaic vulnerability is obtained as the post-disaster loss probability distribution.

[0010] Furthermore, the generation of a set of simulated wind speed conditions matching the current photovoltaic power station, based on typhoon data and photovoltaic power station data, includes:

[0011] Several first wind speed samples are randomly generated using a Gumbel distribution for the strong wind speed, where the strong wind speed is the average wind speed at a set height above the ground within a set time period.

[0012] Based on the current topographic features of the photovoltaic power station, several first wind speed samples are converted into second wind speed samples at the height of the photovoltaic substation above the ground. The topographic features include the current height of the photovoltaic power station above the ground and the wind profile index.

[0013] The horizontal plane is divided at predetermined angles to generate several wind direction angles;

[0014] By combining several second wind speed samples and several wind direction angles, a set of simulated wind speed conditions that matches the current photovoltaic power station is generated.

[0015] Furthermore, the process of traversing the set of simulated wind speed conditions, calculating the loss probability contribution of each simulated wind speed condition to the current photovoltaic power station, and statistically analyzing the loss probability contributions corresponding to all simulated wind speed conditions to obtain the loss ratio probability of the photovoltaic power station includes:

[0016] Traverse the set of simulated wind speed conditions, and for each simulated wind speed condition acting on the current photovoltaic power station, determine the wind load damage and wind vibration damage of the photovoltaic segments in the photovoltaic power station, and determine the loss probability contribution of each simulated wind speed condition acting on the current photovoltaic power station, among which wind load damage has a higher priority than wind vibration damage.

[0017] The loss ratio probability of a photovoltaic power plant is obtained by statistically analyzing the loss probability contribution corresponding to all simulated wind speed conditions.

[0018] Furthermore, the determination of wind load damage and wind vibration damage to photovoltaic segments within the photovoltaic power station for each simulated wind speed condition applied to the current photovoltaic power station includes:

[0019] By using the shading coefficient field corresponding to the current photovoltaic power station, the wind speed in each simulated wind speed condition is transformed to obtain the actual equivalent wind speed matrix of the photovoltaic area;

[0020] Traverse the actual equivalent wind speed of the photovoltaic segment under each working condition in the actual equivalent wind speed matrix, and compare the actual equivalent wind speed of the photovoltaic segment under the current working condition with the critical wind speed of the photovoltaic segment to determine whether photovoltaic wind load damage has occurred under the current working condition.

[0021] If so, calculate the contribution of the current operating condition to the probability of loss of the photovoltaic segment;

[0022] If not, the actual wind vibration coefficient of the photovoltaic segment calculated based on the current operating conditions will be compared with the critical wind vibration coefficient of the photovoltaic segment to determine whether photovoltaic wind vibration damage has occurred under the current operating conditions.

[0023] If so, the loss probability contribution of the current operating condition to the current photovoltaic power station is calculated based on the duration of strong wind and the wind vibration fatigue duration coefficient; otherwise, the loss probability contribution of the current operating condition to the photovoltaic segment is 0.

[0024] Furthermore, before traversing the set of simulated wind speed conditions and determining the wind load damage and wind vibration damage of the photovoltaic segments within the photovoltaic power station for each simulated wind speed condition acting on the current photovoltaic power station, and before determining the loss probability contribution of each simulated wind speed condition acting on the current photovoltaic power station, the method further includes:

[0025] Obtain the wind tunnel segment test data of the current photovoltaic power station, and expand the wind tunnel segment test data to obtain the expanded wind tunnel segment test data;

[0026] The maximum shading coefficient is calculated based on the expanded wind tunnel segment test data, and the shading coefficient field of the current photovoltaic power station is generated based on the maximum shading coefficient.

[0027] The center value of the critical wind speed is calculated based on the expanded wind tunnel segment test data, and the critical wind speed field is generated based on the center value of the critical wind speed using a standard normal distribution.

[0028] The center value of the critical wind vibration coefficient is calculated based on the expanded wind tunnel segment test data, and the critical wind vibration coefficient field is generated by using the standard normal distribution based on the center value of the critical wind vibration coefficient.

[0029] Furthermore, the calculation of the loss probability contribution of the current operating condition acting on the current photovoltaic power station based on the duration of strong wind and the wind-induced fatigue duration coefficient includes:

[0030] The probability contribution of loss when calculating the current photovoltaic power station based on the duration of strong winds and the wind-induced fatigue duration coefficient is R / M. Where t is the duration of the typhoon, T k is the wind-induced fatigue duration coefficient, and M is the total number of simulated wind speed conditions.

[0031] Furthermore, the formula for the prior distribution of photovoltaic vulnerability is expressed as:

[0032]

[0033] In the formula, V is the given wind speed value, x is the loss ratio, P(x / V) represents the probability density of the loss ratio x at a given wind speed V, and α and β are shape distribution parameters.

[0034] Furthermore, the prior distribution of photovoltaic vulnerability is updated using Bayesian iteration based on the loss ratio probability of the photovoltaic power station to obtain the posterior distribution of photovoltaic vulnerability as the post-disaster loss probability distribution, which is expressed by the following formula:

[0035]

[0036] In the formula, P(x / V)' represents the posterior probability of photovoltaic vulnerability, α and β are shape distribution parameters, n is the number of scenario simulations, k is the number of times the loss ratio probability equals x in n scenario simulations, V is the wind speed, and x is the loss ratio (0 to 1).

[0037] Furthermore, after traversing the set of simulated wind speed conditions, calculating the loss probability contribution of each simulated wind speed condition acting on the current photovoltaic power station, and statistically analyzing the loss probability contributions corresponding to all simulated wind speed conditions to obtain the loss ratio probability of the photovoltaic power station, the method further includes:

[0038] Based on the loss ratio probability, a cloud map of the loss ratio distribution of photovoltaic power plants and a vulnerability curve of photovoltaic power plants to typhoon disasters are generated.

[0039] Furthermore, this invention also proposes a photovoltaic power plant disaster damage assessment system, the system comprising:

[0040] The wind speed condition generation module is used to generate a set of simulated wind speed conditions that match the current photovoltaic power station based on strong wind speed and photovoltaic power station data.

[0041] The loss statistics module is used to traverse the set of simulated wind speed conditions, calculate the loss probability contribution of each simulated wind speed condition to the current photovoltaic power station, and calculate the loss probability contribution of all simulated wind speed conditions to obtain the loss ratio probability of the photovoltaic power station.

[0042] The post-disaster loss probability distribution calculation module is used to perform Bayesian update iteration on the prior distribution of photovoltaic vulnerability using the loss ratio probability of photovoltaic power plants, and obtain the posterior distribution of photovoltaic vulnerability as the post-disaster loss probability distribution.

[0043] The advantages of this invention are as follows: Based on strong wind speed and photovoltaic power station data, this invention generates a set of simulated wind speed conditions that match the current photovoltaic power station. After generating these wind speed conditions, it iterates through these conditions, calculates and statistically analyzes the loss ratio probability of the photovoltaic power station, and updates the shape distribution parameters in the prior distribution of photovoltaic vulnerability using a Bayesian update algorithm based on the loss ratio probability. The resulting photovoltaic power station vulnerability curve, i.e., the posterior distribution of photovoltaic vulnerability, can simulate the loss situation of photovoltaic power stations after suffering typhoon disasters, providing technical reference for the assessment of typhoon damage to photovoltaic power stations or the direction of disaster prevention and mitigation measures.

[0044] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating a photovoltaic power plant disaster damage assessment method proposed in one embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram of the overall process of a photovoltaic power plant disaster damage assessment method in one embodiment of the present invention;

[0047] Figure 3 This is a vulnerability curve variation diagram in one embodiment of the present invention;

[0048] Figure 4 This is a vulnerability curve after 20 Bayesian updates in one embodiment of the present invention;

[0049] Figure 5 This is an output interface diagram of the loss ratio distribution cloud map of a photovoltaic power station in one embodiment of the present invention;

[0050] Figure 6 This is an output interface diagram of the fragility curve and the vulnerability curve in one embodiment of the present invention;

[0051] Figure 7 This is a vulnerability curve diagram under a wind angle of 90° in one embodiment of the present invention;

[0052] Figure 8 This is a vulnerability curve diagram under a wind direction angle of 90° in one embodiment of the present invention;

[0053] Figure 9 This is a cloud map showing the distribution of the loss ratio of a photovoltaic power station under a wind speed of 55 m / s in one embodiment of the present invention;

[0054] Figure 10 This is a schematic diagram of the structure of a photovoltaic power plant disaster damage assessment system proposed in an embodiment of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] like Figure 1 As shown in the figure, an embodiment of the present invention proposes a method for assessing the disaster damage of a photovoltaic power station, the method comprising the following steps:

[0057] S10. Based on strong wind speed and photovoltaic power station data, generate a set of simulated wind speed conditions that match the current photovoltaic power station.

[0058] It should be noted that strong winds, such as typhoons, acting on the surface of photovoltaic systems will generate enormous wind pressure and suction. A set of simulated wind speed conditions matching the current photovoltaic power station is generated based on strong wind speed and photovoltaic power station data, such as the terrain where the photovoltaic power station is located.

[0059] S20. Traverse the set of simulated wind speed conditions, calculate the loss probability contribution of each simulated wind speed condition to the current photovoltaic power station, and statistically analyze the loss probability contribution of all simulated wind speed conditions to obtain the loss ratio probability of the photovoltaic power station.

[0060] S30. Using the loss ratio probability of photovoltaic power plants, perform Bayesian update iteration on the prior distribution of photovoltaic vulnerability to obtain the posterior distribution of photovoltaic vulnerability as the post-disaster loss probability distribution.

[0061] It should be noted that the prior distribution of photovoltaic vulnerability is initially set as a Beta distribution. At this time, the distribution exhibits the greatest uncertainty and is only a preliminary estimate. The actual loss ratio data calculated by the scenario extrapolation model is updated using Bayesian methods, which can iteratively optimize the shape parameters of the Beta distribution, making the posterior distribution more consistent with the loss pattern of photovoltaic power plants under real strong winds, reducing the bias caused by the initial assumptions, and improving the accuracy of the model in calculating the loss probability distribution.

[0062] As a further preferred technical solution, step S10: Based on typhoon data and photovoltaic power station data, generating a set of simulated wind speed conditions that match the current photovoltaic power station, specifically includes the following steps:

[0063] S11. Several first wind speed samples are randomly generated using a Gumbel distribution for the strong wind speed, where the strong wind speed is the average wind speed over a set time period at a set height above the ground.

[0064] It should be noted that, in this specific embodiment, when a photovoltaic power station is subjected to a typhoon, the 10-minute average wind speed at a distance of 10m from the ground is used for research. The distribution of this average wind speed is randomly generated using a Gumbel distribution, resulting in multiple first wind speed samples:

[0065]

[0066] In the formula, v is the average wind speed of the strong wind, μ is the location parameter, and β is the scale parameter.

[0067] Specifically, the Gumbel distribution will be used to generate 50 initial wind speed samples at a height of 10m above the ground.

[0068] S12. Based on the current topographic features of the photovoltaic power station, convert several first wind speed samples into second wind speed samples at the height of the photovoltaic substation above the ground. The topographic features include the current height of the photovoltaic power station above the ground and the wind profile index.

[0069] Specifically, the second wind speed sample within the photovoltaic system area is calculated based on the atmospheric boundary layer wind speed profile distribution, as follows:

[0070]

[0071] In the formula, V g For the second wind speed sample, v is the average wind speed at a height of 10 meters above the ground during strong winds, Z is the height of the photovoltaic power station above the ground, and α is the wind speed profile index, which is related to the roughness of the ground and needs to be selected according to the topography. The value is taken from Table 1.

[0072] Table 1 Wind Profile Parameters

[0073]

[0074] In this classification, A represents nearshore waters or island areas, B represents open areas such as fields and villages, C represents small and medium-sized towns and suburbs of large cities, and D represents urban areas of large cities with dense clusters of high-rise buildings. In practice, photovoltaic power stations are generally located in open rural areas, and the terrain roughness is generally classified as B.

[0075] S13. Divide the horizontal plane at set angles to generate several wind direction angles;

[0076] S14. Combine several second wind speed samples and several wind direction angles to generate a set of simulated wind speed conditions that match the current photovoltaic power station.

[0077] Specifically, in this embodiment, a wind direction angle is set every 15° on the horizontal plane, and a circle around the photovoltaic power station is 360°, for a total of 24 wind direction angle conditions. Then, these 50 typhoon wind speed samples and 24 wind direction angle samples are combined to obtain 1200 wind conditions, which are then iterated over to the photovoltaic power station one by one to accumulate the photovoltaic loss probability.

[0078] As a further preferred technical solution, step S20: traversing the set of simulated wind speed conditions, calculating the loss probability contribution of each simulated wind speed condition acting on the current photovoltaic power station, and statistically analyzing the loss probability contributions corresponding to all simulated wind speed conditions to obtain the loss ratio probability of the photovoltaic power station, specifically includes the following steps:

[0079] S21. Traverse the set of simulated wind speed conditions, and for each simulated wind speed condition acting on the current photovoltaic power station, determine the wind load damage and wind vibration damage of the photovoltaic segments in the photovoltaic power station, and determine the loss probability contribution of each simulated wind speed condition acting on the current photovoltaic power station, where the priority of wind load damage is higher than that of wind vibration damage.

[0080] It should be noted that in this embodiment, wind load damage is given higher priority than wind vibration damage. If a segment of the photovoltaic system experiences wind load damage, wind vibration damage at that segment is not considered. If a segment of the photovoltaic system does not experience wind load damage, the judgment of wind vibration damage continues.

[0081] S22. Calculate the loss probability contribution of all simulated wind speed conditions to obtain the loss ratio probability of the photovoltaic power station.

[0082] As a further preferred technical solution, step S21: Traversing the set of simulated wind speed conditions, and judging the wind load damage and wind vibration damage of the photovoltaic segments in the photovoltaic power station when each simulated wind speed condition acts on the current photovoltaic power station, specifically including the following steps:

[0083] S221. Using the shading coefficient field corresponding to the current photovoltaic power station, the wind speed in each simulated wind speed condition is converted to obtain the actual equivalent wind speed matrix of the photovoltaic area.

[0084] Specifically, the second wind speed sample V at the photovoltaic height g Divide by the corresponding shading coefficient to obtain the actual equivalent wind speed of the photovoltaic segment. By using the shading coefficient field corresponding to the photovoltaic power station to transform the wind speed in each simulated wind speed condition, the actual equivalent wind speed matrix of the photovoltaic area can be obtained.

[0085]

[0086] In the formula, V g(i,j) SR represents the actual equivalent wind speed of the photovoltaic segment at row i and column j. (i,j)V is the shading coefficient of the photovoltaic segment at the i-th row and j-th column. g This represents the actual equivalent wind speed at the height of the photovoltaic power station.

[0087] S222. Traverse the actual equivalent wind speed of the photovoltaic segment under each working condition in the actual equivalent wind speed matrix, and compare the actual equivalent wind speed of the photovoltaic segment under the current working condition with the critical wind speed of the photovoltaic segment to determine whether photovoltaic wind load damage has occurred under the current working condition. If yes, proceed to step S223; otherwise, proceed to step S224.

[0088] It should be noted that the critical maximum wind speed is the core threshold for determining whether a photovoltaic segment has suffered wind load damage: when the actual equivalent wind speed applied to a photovoltaic segment exceeds this critical value, the segment is determined to have suffered wind load damage, and wind load damage has a higher priority than wind vibration damage; its distribution is based on the wind tunnel test results to determine the center value, and the standard normal distribution is used to reflect the probabilistic characteristics of the wind resistance capacity of different segments, providing a quantitative basis for calculating the probability of wind load damage, and directly affecting the statistics of the overall loss ratio of the photovoltaic power station.

[0089] S223. Calculate the contribution of the current operating condition to the probability of loss of the photovoltaic segment;

[0090] Specifically, the criterion for determining wind load damage to photovoltaic (PV) segments is that the actual equivalent wind speed acting on the PV segment is greater than the critical wind speed of the PV segment. If the PV segment is damaged by wind load, the probability contribution of the loss is 1 / M, where M = 1200 = 50 × 24, 50 represents 50 wind speed points, and 24 represents 24 wind direction angles.

[0091] S224. Compare the actual wind vibration coefficient of the photovoltaic segment calculated based on the current working conditions with the critical wind vibration coefficient of the photovoltaic segment to determine whether photovoltaic wind vibration damage has occurred under the current working conditions. If yes, proceed to step S225; otherwise, proceed to step S226.

[0092] It should be noted that in this embodiment, wind load damage is prioritized over wind vibration damage. If wind load damage occurs in a photovoltaic segment, wind vibration damage at that segment is not considered. If no wind load damage occurs, the determination of wind vibration damage is as follows:

[0093] The actual wind vibration coefficient of the photovoltaic segment calculated based on the current operating conditions is compared with the critical wind vibration coefficient of the photovoltaic segment. If the calculated wind vibration coefficient of the photovoltaic segment is greater than the critical wind vibration coefficient at that location, it is determined that wind vibration damage has occurred; otherwise, it is determined that no wind vibration damage has occurred.

[0094] Specifically, the actual wind vibration coefficient of a photovoltaic (PV) segment at a given wind speed is equal to the maximum vibration amplitude of the PV system at that wind speed divided by the average vibration amplitude. The critical wind vibration coefficient of a PV segment is obtained by wind tunnel testing of the PV model. During wind tunnel testing, the wind vibration coefficient of the first row of segments facing the wind is primarily tested, as this segment has the highest coefficient. After measuring the wind vibration coefficient, the wind vibration coefficients of other PV segments are calculated using the shading coefficient matrix, in the same way as the calculation of the actual wind speed Vg(i,j) at the PV segment. Because wind tunnel testing involves multiple wind speed and wind direction angle conditions, a linear interpolation method can be used to obtain the critical wind vibration coefficient field of the PV power plant under different wind speed and wind direction angle conditions.

[0095] S225. Calculate the loss probability contribution when the current working condition acts on the current photovoltaic power station based on the duration of strong wind and the wind vibration fatigue duration coefficient.

[0096] Specifically, the loss probability contribution of the current photovoltaic power station is calculated as R / M based on the duration of strong winds and the wind vibration fatigue duration coefficient. Where t is the duration of the typhoon, T k is the wind-induced fatigue duration coefficient, and M is the total number of simulated wind speed conditions.

[0097] Specifically, if the value of R is greater than or equal to 1, that is, when the wind vibration time exceeds the fatigue time coefficient, the photovoltaic system will fail. If the value of R is less than 1, then R / 1200 is the partial failure probability, where 1200 = 50 × 24, 50 represents 50 wind speed points, 24 represents 24 wind direction angles, and there are a total of 1200 wind speed conditions.

[0098] S226. Determine that the contribution of the current operating condition to the probability of loss of the photovoltaic segment is 0.

[0099] As a further preferred technical solution, before step S21: traversing the set of simulated wind speed conditions, judging the wind load damage and wind vibration damage of the photovoltaic segments in the photovoltaic power station when each simulated wind speed condition acts on the current photovoltaic power station, and determining the loss probability contribution of each simulated wind speed condition acting on the current photovoltaic power station, the method further includes the following steps:

[0100] S21' Obtain the wind tunnel segment test data of the current photovoltaic power station, and expand the wind tunnel segment test data to obtain the expanded wind tunnel segment test data;

[0101] Specifically, the data from wind tunnel photovoltaic (PV) segment tests cannot meet the required data volume, as it only contains data on the wind vibration coefficient and shading coefficient of PV segments under limited combinations of parameters such as wind speed, wind direction angle, and tilt angle. This embodiment employs a linear interpolation method to expand the wind tunnel segment test data, enabling the complete construction of a database of PV segment wind vibration coefficients and shading coefficients, covering all combinations of wind vibration coefficient and shading coefficient values ​​under wind speeds (1–80 m / s), wind direction angles (-180°–180°), and tilt angles (5°, 10°, 15°, 20°). The expanded data is used to support the traversal calculation function. As long as the working condition combination entered in the parameter input interface is within the range of wind speed (1~80m / s), wind direction angle (-180°~180°), and tilt angle (5°, 10°, 15°, 20°), the shading coefficient, wind vibration coefficient, wind speed, etc. of the photovoltaic segment can be calculated, which is convenient for judging whether the photovoltaic segment is damaged.

[0102] S22' Calculate the maximum shading coefficient based on the expanded wind tunnel segment test data, and generate the shading coefficient field of the current photovoltaic power station based on the maximum shading coefficient;

[0103] Specifically, in this embodiment, the shading coefficient field of the photovoltaic power station is calculated based on the maximum shading coefficient and the number of rows and columns of photovoltaic segments in the photovoltaic power station, and the formula is expressed as follows:

[0104]

[0105] In the formula, SR (i,j) denoted as shading coefficient of the photovoltaic segment at row i and column j; I is the total number of rows in the photovoltaic power station; J is the total number of columns in the photovoltaic power station, and the number of rows and columns of the photovoltaic segments in the photovoltaic power station is determined according to the actual situation of the photovoltaic power station; s is the maximum shading coefficient, which is determined by wind tunnel segment test data.

[0106] It should be noted that the maximum shading coefficient is calculated by using wind tunnel testing of the photovoltaic model to obtain the maximum shading coefficient s of the photovoltaic system. Unlike the wind vibration coefficient, the shading coefficient of the last row of photovoltaic segments is usually taken as the maximum shading coefficient of the photovoltaic power station.

[0107] S23' Calculate the center value of the critical wind speed based on the expanded wind tunnel segment test data, and generate the critical wind speed field based on the center value of the critical wind speed using a standard normal distribution;

[0108] Specifically, the formula for generating the critical wind speed field based on the center value of the critical wind speed using the standard normal distribution is expressed as follows:

[0109]

[0110] In the formula, v c It is the critical wind speed, s vLet m be the standard deviation of the critical wind speed. v P(v) is the center value of the critical wind speed. c ) represents the probability density of the critical wind speed.

[0111] It should be noted that in this embodiment, the maximum critical wind speed is taken as 40-65 m / s, the flexible photovoltaic structure is taken as 40 m / s, the distribution pattern adopts the standard state distribution, and the center value of the distribution is obtained from the test results of the wind tunnel segment photovoltaic model.

[0112] S24' Calculate the center value of the critical wind vibration coefficient based on the expanded wind tunnel segment test data, and generate the critical wind vibration coefficient field based on the center value of the critical wind vibration coefficient using a standard normal distribution.

[0113] Specifically, the formula for generating the critical wind vibration coefficient field based on the standard normal distribution of the center value of the critical wind vibration coefficient is expressed as follows:

[0114]

[0115] In the formula, k c It is the critical wind vibration coefficient, s k m is the standard deviation of the critical wind vibration coefficient. k P(k) is the center value of the critical wind vibration coefficient. c ) represents the probability density of the critical wind vibration coefficient.

[0116] It should be noted that large photovoltaic arrays are characterized by a large number of segments, complex row and column distribution, and significant shading effects (attenuation of wind speed and wind vibration coefficient between segments). This embodiment calculates the shading coefficient of each segment and generates shading coefficient fields, critical wind vibration coefficient fields, and critical wind speed fields by combining parameters such as the number of rows and columns of the photovoltaic power station and the segment positions. This can accurately reflect the differences in wind load and wind vibration effects at different segments in a large array. Because the large photovoltaic power station is specifically defined on the photovoltaic segments, the calculation of the overall loss rate of the photovoltaic power station is more accurate.

[0117] Furthermore, in this embodiment, the tilt angles of the photovoltaic panels in the photovoltaic power station are selected as 5°, 10°, 15°, and 20° during wind tunnel testing, with the azimuth angle assuming the solar irradiation receiving surface faces due south. The tilt angle of the photovoltaic panel is the angle at which the photovoltaic panel is tilted on the photovoltaic support. Different tilt angles will cause changes in key wind disaster structural response parameters such as the shading coefficient, wind vibration coefficient, and wind load value of the photovoltaic segment, directly affecting the judgment of wind vibration damage and wind load damage. These tilt angles are key parameters for wind tunnel testing, which will obtain data such as the wind vibration coefficient and shading coefficient at different tilt angles, providing a basis for model calculations. In order to receive sufficient sunlight and ensure maximum power generation efficiency, the photovoltaic panels of the photovoltaic power station are all oriented due south. Setting the solar irradiation receiving surface of the photovoltaic power station to due south in the scenario simulation model is to match reality and ensure the accuracy of the calculation results of the scenario simulation model.

[0118] As a further preferred technical solution, the formula for the prior distribution of photovoltaic vulnerability is expressed as:

[0119]

[0120] In the formula, V is the given wind speed value, P(x / V) represents the probability density of loss ratio x under the given wind speed V, α and β are shape distribution parameters that change with the wind parameters; x represents the loss ratio; Γ(α) is the gamma function of shape parameter α, Γ(β) is the gamma function of shape parameter β, and Γ(α+β) represents the gamma function of the sum of α and β.

[0121] It should be noted that the initial values ​​of α and β are set to α = 0.5 and β = 0.5, indicating that the loss distribution of the photovoltaic power station exhibits the greatest uncertainty.

[0122] As a further preferred technical solution, in step S30, the prior distribution of photovoltaic vulnerability is updated and iterated using Bayesian methods based on the loss ratio probability of the photovoltaic power station to obtain the posterior distribution of photovoltaic vulnerability as the post-disaster loss probability distribution, which is expressed by the following formula:

[0123]

[0124] In the formula, P(x / V)' represents the posterior probability of photovoltaic vulnerability, α and β are shape distribution parameters, n is the number of scenario simulations, k is the number of times the loss ratio equals x in n scenario simulations, V is the wind speed, and x is the loss ratio, taking values ​​from 0 to 1. For example, when focusing on specific proportions such as a loss ratio of 10% or 50%, x is taken as 0.1, 0.5, etc., respectively. During the n scenario simulations, the number of times k the loss ratio equals x is counted and substituted into the formula to update the shape parameters α and β of the Beta distribution, thereby obtaining a more accurate posterior distribution model.

[0125] It should be noted that the initial vulnerability prior distribution is set as a Beta distribution (α = 0.5, β = 0.5), at which point the distribution exhibits the greatest uncertainty and is only a preliminary estimate. Bayesian updates are performed using the actual loss ratio data calculated by the scenario simulation model, iteratively optimizing the shape parameters (α and β) of the Beta distribution. This makes the posterior distribution more consistent with the loss patterns of photovoltaic power plants under actual typhoon conditions, reducing biases introduced by the initial assumptions and improving the accuracy of the model's calculation of the loss probability distribution. The Bayesian update iteration dynamically adjusts the distribution parameters by continuously integrating the loss ratio information obtained from scenario simulations (e.g., the number of times the loss ratio is x0 in n scenario simulations, k), and the resulting posterior distribution and corresponding vulnerability curve more accurately reflect the loss probability distribution of photovoltaic power plants under specific wind speeds and directions (e.g., the probability of a loss rate below 20% at 20 m / s and the probability of a loss rate above 70% at 45 m / s), providing a more reliable basis for loss rate estimation. Figure 3 The nearly horizontal blue line in the middle is obtained from the vulnerability prior distribution model, while the other curves represent the process of extrapolating 20 times using the scenario extrapolation model.

[0126] This embodiment specifically updates the shape distribution parameters α and β in the photovoltaic typhoon vulnerability model by performing 20 Bayesian update iterations based on the calculated loss ratio probability and the posterior distribution of photovoltaic vulnerability, thereby obtaining the posterior distribution model of photovoltaic typhoon vulnerability. The output vulnerability curve is shown in the figure below. Figure 4 As shown.

[0127] It should be noted that one Bayesian update requires 1200 scenario simulations. The number of Bayesian updates depends on whether the shape parameters tend to stabilize, i.e., the loss ratio output by the posterior distribution model no longer changes significantly, at which point the update can be stopped.

[0128] As a further preferred technical solution, such as Figure 2 , Figure 5 and Figure 6 As shown, in step S20: after traversing the set of simulated wind speed conditions, calculating the loss probability contribution of each simulated wind speed condition acting on the current photovoltaic power station, and statistically analyzing the loss probability contributions corresponding to all simulated wind speed conditions to obtain the loss ratio probability of the photovoltaic power station, the method further includes the following steps:

[0129] Based on the loss ratio probability, a cloud map of the loss ratio distribution of photovoltaic power plants and a vulnerability curve of photovoltaic power plants to typhoon disasters are generated.

[0130] It should be noted that this embodiment can also output a loss ratio distribution cloud map of the photovoltaic power station and a typhoon disaster vulnerability curve of the photovoltaic power station based on the loss ratio, making the loss and vulnerability situation more intuitive. Specifically, based on the loss ratio of each photovoltaic segment in the photovoltaic power station, a loss ratio distribution cloud map of the entire photovoltaic power station can be output, as shown in the following figure. Figure 9 As shown in the figure, different colors are used to mark the proportion of damage to different photovoltaic sections in the photovoltaic power station after being hit by a typhoon. This provides a clear visual representation of the loss distribution of the photovoltaic power station. The scenario simulation model can also output photovoltaic vulnerability and fragility curves. The vulnerability curve reflects the probability distribution of the loss proportion of the photovoltaic power station under a certain typhoon wind speed and direction angle, such as... Figure 8 As shown, when the wind angle is 90° and the typhoon speed is 20 m / s, the loss rate of the photovoltaic power station is likely to be less than 20%. When the wind speed rises to 45 m / s, the loss rate of the photovoltaic power station is very likely to reach over 70%. The vulnerability curve can reflect the probability of loss of a photovoltaic power station under different typhoon wind speeds at a certain wind angle, such as... Figure 7 As shown, at a wind angle of 90°, when the typhoon wind speed is close to 20m / s and 40m / s, the loss rate of the photovoltaic power station will rise sharply. At 80m / s, the loss rate of the photovoltaic power station can reach 100%.

[0131] It should be noted that this invention can obtain a photovoltaic power station loss ratio distribution cloud map, a photovoltaic system vulnerability curve, and a photovoltaic system fragility curve. The photovoltaic power station loss ratio distribution cloud map can visually show the loss distribution of the photovoltaic power station after a typhoon, which is helpful in identifying wind-damaged areas of the photovoltaic power station. The photovoltaic system vulnerability curve reflects the probability distribution of loss after a photovoltaic power station suffers a typhoon disaster, and the approximate loss rate of the photovoltaic power station can be estimated based on the vulnerability curve. The photovoltaic system fragility curve reflects the change in the loss ratio of the photovoltaic power station under different typhoon wind speeds, showing the disaster damage trend of the photovoltaic power station during the disaster period. This invention can provide certain technical support for the fields of typhoon-resistant structural reinforcement of photovoltaic power stations and typhoon disaster insurance loss assessment of photovoltaic power stations, and to a certain extent fills the gap in typhoon disaster loss assessment technology for photovoltaic power stations.

[0132] In addition, such as Figure 10 As shown, another embodiment of the present invention also proposes a photovoltaic power plant disaster damage assessment system, the system comprising:

[0133] The wind speed condition generation module 10 is used to generate a set of simulated wind speed conditions that match the current photovoltaic power station based on strong wind speed and photovoltaic power station data.

[0134] The loss statistics module 20 is used to traverse the set of simulated wind speed conditions, calculate the loss probability contribution of each simulated wind speed condition when it acts on the current photovoltaic power station, and calculate the loss probability contribution of all simulated wind speed conditions to obtain the loss ratio probability of the photovoltaic power station.

[0135] The post-disaster loss probability distribution calculation module 30 is used to perform Bayesian update iteration on the prior distribution of photovoltaic vulnerability using the loss ratio probability of photovoltaic power station, and obtain the posterior distribution of photovoltaic vulnerability as the post-disaster loss probability distribution.

[0136] As a further preferred technical solution, the wind speed condition generation module 10 specifically includes:

[0137] The first wind speed sample generation unit is used to randomly generate several first wind speed samples from the strong wind speed using a Gumbel distribution. The strong wind speed is the average wind speed at a set height above the ground within a set time period.

[0138] The second wind speed sample generation unit is used to convert several first wind speed samples into second wind speed samples at the height of the photovoltaic substation above the ground, based on the current topographic features of the photovoltaic power station. The topographic features include the current height of the photovoltaic power station above the ground and the wind profile index.

[0139] The wind direction angle sample generation unit is used to divide the horizontal plane at set angles to generate several wind direction angles.

[0140] The wind speed condition generation unit is used to combine several second wind speed samples and several wind direction angles to generate a set of simulated wind speed conditions that match the current photovoltaic power station.

[0141] As a further preferred technical solution, the loss statistics module 20 specifically includes:

[0142] The damage judgment unit is used to traverse the set of simulated wind speed conditions, and to judge the wind load damage and wind vibration damage of the photovoltaic segments in the photovoltaic power station when each simulated wind speed condition acts on the current photovoltaic power station. It determines the loss probability contribution of each simulated wind speed condition when it acts on the current photovoltaic power station, with wind load damage having a higher priority than wind vibration damage.

[0143] The statistical unit is used to calculate the loss probability contribution of all simulated wind speed conditions to obtain the loss ratio probability of the photovoltaic power station.

[0144] As a further preferred technical solution, the damage determination unit specifically includes:

[0145] The conversion subunit is used to convert the wind speed in each simulated wind speed condition using the shading coefficient field corresponding to the current photovoltaic power station, so as to obtain the actual equivalent wind speed matrix of the photovoltaic area.

[0146] The first judgment subunit is used to traverse the actual equivalent wind speed of the photovoltaic segment under each working condition in the actual equivalent wind speed matrix, and compare the actual equivalent wind speed of the photovoltaic segment under the current working condition with the critical wind speed of the photovoltaic segment to determine whether photovoltaic wind load damage has occurred under the current working condition.

[0147] The loss probability contribution calculation subunit is used to calculate the loss probability contribution of the current operating condition to the photovoltaic segment when the output result of the first judgment subunit is yes.

[0148] The second judgment subunit is used to compare the actual wind vibration coefficient of the photovoltaic segment calculated based on the current working conditions with the critical wind vibration coefficient of the photovoltaic segment when the output result of the first judgment subunit is negative, and to determine whether photovoltaic wind vibration damage has occurred under the current working conditions.

[0149] The loss probability contribution calculation subunit is also used to calculate the loss probability contribution of the current working condition to the current photovoltaic power station based on the duration of strong wind and the wind vibration fatigue duration coefficient when the output result of the second judgment subunit is yes; otherwise, it is determined that the loss probability contribution of the current working condition to the photovoltaic segment is 0.

[0150] As a further preferred technical solution, the system also includes a parameter field generation module, specifically comprising:

[0151] The data augmentation unit is used to acquire the wind tunnel segment test data of the current photovoltaic power station and augment the wind tunnel segment test data to obtain the augmented wind tunnel segment test data.

[0152] The shading coefficient field calculation unit is used to calculate the maximum shading coefficient based on the expanded wind tunnel segment test data, and generate the shading coefficient field of the current photovoltaic power station based on the maximum shading coefficient.

[0153] The critical wind speed field calculation unit is used to calculate the center value of the critical wind speed based on the expanded wind tunnel segment test data, and to generate the critical wind speed field based on the center value of the critical wind speed using a standard normal distribution.

[0154] The critical wind vibration coefficient field calculation unit is used to calculate the center value of the critical wind vibration coefficient based on the expanded wind tunnel segment test data, and to generate the critical wind vibration coefficient field based on the center value of the critical wind vibration coefficient using a standard normal distribution.

[0155] As a further preferred technical solution, the formula for the prior distribution of photovoltaic vulnerability is expressed as:

[0156]

[0157] In the formula, V is the given wind speed value, x is the loss ratio, which is taken as (0, 1), P(x / V) represents the probability density of the loss ratio x at a given wind speed V, and α and β are shape distribution parameters.

[0158] As a further preferred technical solution, the post-disaster loss probability distribution calculation module 30 is used to perform Bayesian update iteration to obtain the posterior distribution of photovoltaic vulnerability as the post-disaster loss probability distribution, which is expressed by the following formula:

[0159]

[0160] In the formula, P(x / V)' represents the posterior probability of photovoltaic vulnerability, α and β are shape distribution parameters, n is the number of scenario simulations, k is the number of times the loss ratio probability equals the set threshold in n scenario simulations, V is the wind speed, and x is the loss ratio (0 to 1).

[0161] It should be noted that other embodiments or specific implementation methods of the photovoltaic power plant disaster loss assessment system described in this invention can refer to the above-mentioned method embodiments, and will not be repeated here.

[0162] It should be noted that the computer-readable medium disclosed in this embodiment may be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0163] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform a zero-sample image anomaly detection method according to the above embodiments.

[0164] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server.

[0165] In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0166] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0167] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0168] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" or "several" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0169] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for assessing the disaster damage of a photovoltaic power station, characterized in that, include: Based on strong wind speed and photovoltaic power station data, a set of simulated wind speed conditions matching the current photovoltaic power station is generated. Traverse the set of simulated wind speed conditions, calculate the loss probability contribution of each simulated wind speed condition to the current photovoltaic power station, and statistically analyze the loss probability contribution of all simulated wind speed conditions to obtain the loss ratio probability of the photovoltaic power station. By using the loss ratio probability of photovoltaic power plants to perform Bayesian update iteration on the prior distribution of photovoltaic vulnerability, the posterior distribution of photovoltaic vulnerability is obtained as the post-disaster loss probability distribution.

2. The photovoltaic power plant disaster damage assessment method as described in claim 1, characterized in that, The process of generating a set of simulated wind speed conditions that match the current photovoltaic power station, based on typhoon data and photovoltaic power station data, includes: Several first wind speed samples are randomly generated using a Gumbel distribution for the strong wind speed, where the strong wind speed is the average wind speed at a set height above the ground within a set time period. Based on the current topographic features of the photovoltaic power station, several first wind speed samples are converted into second wind speed samples at the height of the photovoltaic substation above the ground. The topographic features include the current height of the photovoltaic power station above the ground and the wind profile index. The horizontal plane is divided at predetermined angles to generate several wind direction angles; By combining several second wind speed samples and several wind direction angles, a set of simulated wind speed conditions that matches the current photovoltaic power station is generated.

3. The photovoltaic power plant disaster damage assessment method as described in claim 1, characterized in that, The set of simulated wind speed conditions is traversed, the loss probability contribution of each simulated wind speed condition to the current photovoltaic power station is calculated, and the loss ratio probability of the photovoltaic power station is obtained by statistically analyzing the loss probability contributions corresponding to all simulated wind speed conditions, including: Traverse the set of simulated wind speed conditions, and for each simulated wind speed condition acting on the current photovoltaic power station, determine the wind load damage and wind vibration damage of the photovoltaic segments in the photovoltaic power station, and determine the loss probability contribution of each simulated wind speed condition acting on the current photovoltaic power station, among which wind load damage has a higher priority than wind vibration damage. The loss ratio probability of a photovoltaic power plant is obtained by statistically analyzing the loss probability contribution corresponding to all simulated wind speed conditions.

4. The photovoltaic power plant disaster damage assessment method as described in claim 3, characterized in that, The determination of wind load damage and wind vibration damage to photovoltaic segments within a photovoltaic power station under each simulated wind speed condition includes: By using the shading coefficient field corresponding to the current photovoltaic power station, the wind speed in each simulated wind speed condition is transformed to obtain the actual equivalent wind speed matrix of the photovoltaic area; Traverse the actual equivalent wind speed of the photovoltaic segment under each working condition in the actual equivalent wind speed matrix, and compare the actual equivalent wind speed of the photovoltaic segment under the current working condition with the critical wind speed of the photovoltaic segment to determine whether photovoltaic wind load damage has occurred under the current working condition. If so, calculate the contribution of the current operating condition to the probability of loss of the photovoltaic segment; If not, the actual wind vibration coefficient of the photovoltaic segment calculated based on the current operating conditions will be compared with the critical wind vibration coefficient of the photovoltaic segment to determine whether photovoltaic wind vibration damage has occurred under the current operating conditions. If so, the loss probability contribution of the current operating condition to the current photovoltaic power station is calculated based on the duration of strong wind and the wind vibration fatigue duration coefficient; otherwise, the loss probability contribution of the current operating condition to the photovoltaic segment is 0.

5. The photovoltaic power plant disaster damage assessment method as described in claim 3, characterized in that, Before traversing the set of simulated wind speed conditions and determining the wind load damage and wind vibration damage assessment of the photovoltaic segments within the photovoltaic power station for each simulated wind speed condition acting on the current photovoltaic power station, and before determining the loss probability contribution of each simulated wind speed condition acting on the current photovoltaic power station, the method further includes: Obtain the wind tunnel segment test data of the current photovoltaic power station, and expand the wind tunnel segment test data to obtain the expanded wind tunnel segment test data; The maximum shading coefficient is calculated based on the expanded wind tunnel segment test data, and the shading coefficient field of the current photovoltaic power station is generated based on the maximum shading coefficient. The center value of the critical wind speed is calculated based on the expanded wind tunnel segment test data, and the critical wind speed field is generated based on the center value of the critical wind speed using a standard normal distribution. The center value of the critical wind vibration coefficient is calculated based on the expanded wind tunnel segment test data, and the critical wind vibration coefficient field is generated by using the standard normal distribution based on the center value of the critical wind vibration coefficient.

6. The photovoltaic power plant disaster damage assessment method as described in claim 4, characterized in that, The calculation of the loss probability contribution of the current operating conditions acting on the current photovoltaic power station based on the duration of strong winds and the wind-induced fatigue duration coefficient includes: The probability contribution of loss when calculating the current photovoltaic power station based on the duration of strong winds and the wind-induced fatigue duration coefficient is R / M. Where t is the duration of the typhoon, T k is the wind-induced fatigue duration coefficient, and M is the total number of simulated wind speed conditions.

7. The photovoltaic power plant disaster damage assessment method as described in claim 1, characterized in that, The formula for the prior distribution of photovoltaic vulnerability is expressed as follows: In the formula, V is the given wind speed value, x is the loss ratio, P(x / V) represents the probability density of the loss ratio x at a given wind speed V, and α and β are shape distribution parameters.

8. The photovoltaic power plant disaster damage assessment method as described in claim 1, characterized in that, The method involves using the loss ratio probability of a photovoltaic power station to perform a Bayesian update iteration on the prior distribution of photovoltaic vulnerability, resulting in the posterior distribution of photovoltaic vulnerability as the post-disaster loss probability distribution. The formula is as follows: In the formula, P(x / V)' represents the posterior probability of photovoltaic vulnerability, α and β are shape distribution parameters, n is the number of scenario simulations, k is the number of times the loss ratio probability equals x in n scenario simulations, V is the wind speed, and x is the loss ratio (0 to 1).

9. The photovoltaic power plant disaster damage assessment method as described in any one of claims 1 to 8, characterized in that, After traversing the set of simulated wind speed conditions, calculating the loss probability contribution of each simulated wind speed condition acting on the current photovoltaic power station, and statistically analyzing the loss probability contributions corresponding to all simulated wind speed conditions to obtain the loss ratio probability of the photovoltaic power station, the method further includes: Based on the loss ratio probability, a cloud map of the loss ratio distribution of photovoltaic power plants and a vulnerability curve of photovoltaic power plants to typhoon disasters are generated.

10. A photovoltaic power plant disaster damage assessment system, characterized in that, include: The wind speed condition generation module is used to generate a set of simulated wind speed conditions that match the current photovoltaic power station based on strong wind speed and photovoltaic power station data. The loss statistics module is used to traverse the set of simulated wind speed conditions, calculate the loss probability contribution of each simulated wind speed condition to the current photovoltaic power station, and calculate the loss probability contribution of all simulated wind speed conditions to obtain the loss ratio probability of the photovoltaic power station. The post-disaster loss probability distribution calculation module is used to perform Bayesian update iteration on the prior distribution of photovoltaic vulnerability using the loss ratio probability of photovoltaic power plants, and obtain the posterior distribution of photovoltaic vulnerability as the post-disaster loss probability distribution.

Images