An improved offshore wind turbine collision damage assessment method

Abstract

The application discloses an offshore wind turbine collision damage evaluation method of improved empowerment method, and relates to the technical field of ocean engineering, and comprises the following steps: S1, obtaining damage data and data processing: a finite element simulation model of a ship colliding with an offshore wind turbine is constructed, collision under different collision conditions is simulated, damage indexes are extracted, and a mathematical model between collision damage factors and damage evaluation indexes is constructed; S2, assigning weights of each collision damage factor and each damage evaluation index: using an analytic hierarchy process with objective information correction to correct the weights, using a fuzzy inverse equation method to calculate and select the weights, combining the subjective and objective to determine multiple possible weight vectors as an alternative set of the fuzzy inverse equation method, obtaining a Nash equilibrium solution of the multiple weight vectors, and solving the optimal weight; S3, implementation of the evaluation model: determining a wind turbine damage grade division basis and corresponding maintenance measures, defining a membership function, constructing a second-order comprehensive evaluation matrix, and outputting a damage degree through fuzzy comprehensive evaluation.

Description

Technical Field

[0001] The present invention relates to the field of marine engineering technology, and more particularly to an offshore wind turbine collision damage evaluation method with an improved weighting method. Background Art

[0002] As an important component of clean energy, offshore wind power generation occupies an increasingly important position in the global energy structure. Due to their unique operating environment and working characteristics, offshore wind turbines are susceptible to accidents such as ship collisions, resulting in structural damage and increased maintenance costs. Therefore, damage safety assessment and monitoring of offshore wind turbines are key to ensuring their stable operation and reducing operational risks. At present, offshore wind turbine damage assessment mainly relies on strain gauges, vibration sensors, etc. to obtain wind turbine operating data and extract characteristic values, and use artificial intelligence, machine learning algorithms, etc. to perform structural health monitoring to determine the damage status and even location. Alternatively, the model correction method iteratively updates the damage parameters of the finite element model to make it consistent with the test data after the actual damage, thereby achieving damage identification. However, these methods are complex systems, accompanied by high costs, and have high requirements for data accuracy and real-time performance.

[0003] After an offshore wind turbine suffers an accident such as a ship collision, its overall damage level is often difficult to accurately assess using traditional methods within the permitted range of a single indicator. This is because there is a complex relationship between damage factors such as collision speed, ship mass, collision angle, etc. and the response of the wind turbine structure, and the actual damage situation may involve a combination of multiple damage modes and degrees, and the judgment of the degree of damage is ambiguous. All of these pose challenges to accurately assessing the damage level. In addition, the evaluation results are significantly affected by the weight of the indicators, and the determination of the weights should follow certain principles. The weight determination method should have reasonable mathematical logic, contain clear practical significance, and meet the needs of accurately evaluating the degree of damage. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide an offshore wind turbine collision damage evaluation method with an improved weighting method, which has the advantages of reasonable mathematical logic and accuracy.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an offshore wind turbine collision damage assessment method using an improved weighting method, comprising the following steps:

[0006] S1. Damage data acquisition and data processing: Construct a finite element simulation model of a ship colliding with an offshore wind turbine, simulate collisions under different collision conditions, extract damage indicators, and construct a mathematical model between collision damage factors and damage evaluation indicators;

[0007] S2, assigning each collision damage factor and each damage evaluation index weight: using the analytic hierarchy process with objective information correction to correct the weight, using the fuzzy inverse equation method to calculate and select the weight, combining subjective and objective to determine multiple possible weight vectors as the alternative set of the fuzzy inverse equation method, obtaining the Nash equilibrium solution of multiple weight vectors, and solving the optimal weight;

[0008] wherein the corrected weight determination index is:

[0009]

[0010] wherein a i,j is the data of the i-th row and j-th column of the judgment matrix, E i is the information entropy of the damage data corresponding to index i, std i is the standard deviation of the damage data corresponding to index i, R i,j is the correlation coefficient of index i to index j, and W i is the weight vector corresponding to index i.

[0011] S3, implementation of the evaluation model: determining the basis for dividing the damage level of the wind turbine and the corresponding maintenance measures, defining the membership function, constructing the second-order comprehensive evaluation matrix, and outputting the damage degree through fuzzy comprehensive evaluation.

[0012] Further, in step S2, in the analytic hierarchy process, a judgment matrix is constructed for each possible case of each importance degree and consistency check is performed, and the passers are retained and the non-passers are discarded.

[0013] Further, in step S2, the geometric mean of each damage evaluation index in the judgment matrix is used as the basis for subjective weighting, and the objective data of each index is calculated as a correction term for the geometric mean. The objective data characteristics include:

[0014] 1) information entropy, the calculation method of which is:

[0015] wherein,

[0016] wherein e j is the information entropy of the j-th evaluation index, k represents the normalization of the information entropy, p ij represents the proportion of the i-th damage evaluation index under the j-th damage evaluation index, n is the order of one damage evaluation index, x ij is the data of the i-th row and j-th column in the comprehensive evaluation matrix, and m is the order of another damage evaluation index.

[0017] 4) 2) standard deviation, the calculation method of which is:

[0018]

[0019] In the formula, μ is the data mean, x i is the data corresponding to the i-th damage assessment index;

[0020] 5) Pearson correlation coefficient, which is calculated as follows:

[0021]

[0022] Where R ij is the correlation coefficient between the two damage assessment indicators, Cov(X,Y) is the covariance of the two data, s X ,s Y is the standard deviation of the two data.

[0023] Furthermore, the method for solving the optimal weight is:

[0024]

[0025] Where ω is the optimal weight, is the weight vector set, β k is the weight coefficient;

[0026] when hour,

[0027] The corresponding linear equations are:

[0028]

[0029] In formula (5), β k is the weight coefficient, is the weight vector set, ω i is the optimal weight of index i; in formula (6), ω G is the optimal weight of indicator G, is the weight vector of the index G in the weight vector concentration, β G is the weight coefficient of index G; the optimal linear combination β1, β2, ..., β n , and then normalize it and output the optimal weight according to formula (4).

[0030] Furthermore, in step S1, the collision damage factors include collision speed, ship mass and collision angle; the damage evaluation indicators include area damage rate, wind turbine natural frequency, maximum displacement of tower top during collision, and maximum displacement of pile foundation mud surface.

[0031] Furthermore, in step S1, constructing a mathematical model between collision damage factors and damage evaluation indicators includes the following steps:

[0032] First, the collision damage data is interpolated and the damage index under different collision speeds and ship masses is fitted using the bivariate polynomial equation shown in formula (1):

[0033] G(x)=g1x 3 +g2y 3 +g3x 2 y+g4y 2 x+g5x 2 +g6y 2 +g7xy+g8x+g9y+g 10 (1)

[0034] Where x represents the mass of the ship, y represents the collision speed, g1, g2...g 10 represents the fitting coefficient;

[0035] Secondly, the tangent function shown in formula (2) is used to fit the damage index under different collision angles:

[0036] f(x)=a·arctan(bx 2 +cx+d)+e (2)

[0037] Where x represents the collision angle; a, b, c, d, and e are fitting coefficients;

[0038] Furthermore, in step S3, the membership function is a Z-type membership function:

[0039]

[0040] When the index value is positively correlated with the degree of injury, M1(x) is used; when the index value is negatively correlated with the degree of injury, M2(x) is used.

[0041] Furthermore, in step S3, the Monte Carlo simulation method is used to process the output result of the membership function of a single damage evaluation index to generate the damage membership vector of the index value. The damage membership vectors of all index values ​​are combined to obtain the first-order fuzzy comprehensive evaluation matrix R1. The weight of the first-order fuzzy evaluation matrix is ​​A1. The first-order fuzzy comprehensive evaluation matrix R1 and the weight A1 are fuzzy transformed to obtain the first-order fuzzy comprehensive evaluation result B1:

[0042]

[0043] The B1 calculated for each factor in the collision damage factor set is used as the row vector of the second-order fuzzy comprehensive evaluation matrix to obtain the second-order fuzzy comprehensive evaluation matrix R2. The weight vector of the collision damage factor set is A2. After fuzzy transformation of the second-order fuzzy comprehensive evaluation matrix R2 and the weight vector A2 of the collision damage factor set, the second-order comprehensive evaluation result B2 can be obtained:

[0044]

[0045] Furthermore, in step S3, A1 = [K1, K2, K3, K4], where K1, K2, K3, and K4 represent the membership of the four indicators of area damage rate, wind turbine natural frequency, maximum displacement of tower top, and maximum displacement of pile foundation mud surface in the comprehensive fuzzy evaluation; A2 = [P1, P2], where P1 and P2 represent the membership of the collision speed & ship mass coupling factor and the collision angle in the comprehensive evaluation process, respectively.

[0046] In summary, the present invention has the following beneficial effects: the method of the present invention is based on fuzzy theory, uses a fuzzy comprehensive evaluation method to establish a damage model for a ship colliding with an offshore wind turbine, uses multiple damage indicators and damage factors as model input information, and comprehensively considers the combined effect of each indicator information in the damage evaluation process, and successfully achieves the purpose of outputting a more accurate damage evaluation result using low-complexity calculations; the method of the present invention improves the weight determination method in the comprehensive evaluation process, combines subjective experience judgment with multiple groups of objective data information, and effectively handles the ambiguity in the subjective judgment process, has clear mathematical and physical meanings, and is conducive to improving the accuracy of the evaluation model. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the weight distribution method provided by the present invention. DETAILED DESCRIPTION

[0048] The present invention is further described in detail below with reference to the examples.

[0049] Currently, a relatively efficient approach to assessing offshore wind turbine collision damage involves constructing a damage assessment model. This model uses methods such as fuzzy comprehensive evaluation and TOPSIS comprehensive evaluation to establish a set of collision damage factors and damage assessment indicators. This model then constructs a damage assessment system based on several easily observable damage indicators. To ensure more reasonable evaluation results, weights must be assigned to each damage assessment indicator to determine its contribution to the damage assessment results.

[0050] To overcome the shortcomings of the prior art, the present invention provides an offshore wind turbine collision damage assessment method with an improved weighting method, comprising the following steps:

[0051] S1. Damage data acquisition and data processing: Construct a finite element simulation model of a ship colliding with an offshore wind turbine, simulate collisions under different collision conditions, extract damage indicators, and construct a mathematical model between collision damage factors and damage evaluation indicators;

[0052] In the embodiment, the collision damage factors are the collision speed, the ship mass and the angle of deviation; the damage evaluation indexes are the area damage rate, the inherent frequency of the fan, the maximum displacement of the tower top during the collision process and the maximum displacement of the pile foundation mud surface;

[0053] The calculation method of the area damage rate is: judging whether a grid element is damaged by whether the equivalent plastic strain (PEEQ) of the grid element during the collision process is 0, adding the areas of all damaged elements and dividing the total area to obtain the area damage rate; the calculation method of the inherent frequency of the fan is: carrying out modal analysis on the grid element after the collision damage deformation, and obtaining the first-order modal frequency as the inherent frequency of the fan; the maximum displacement of the tower top during the collision process and the maximum displacement of the pile foundation mud surface are directly extracted from the simulation results.

[0054] The mathematical model between the collision damage factors and the damage evaluation indexes includes the following steps:

[0055] A. First, the collision damage data is interpolated, and a binary polynomial equation as shown in formula (1) is used to fit the damage indexes under different collision speeds and ship masses:

[0056] G(x)=g1x 3 +g2y 3 +g3x 2 y+g4y 2 x+g5x 2 +g6y 2 +g7xy+g8x+g9y+g 10 (1)

[0057] In the formula, x represents the ship mass, y represents the collision speed, g1, g2…g 10 represent the fitting coefficients

[0058] B. Secondly, a tangent function as shown in formula (2) is used to fit the damage indexes under different angles of deviation:

[0059] f(x)=a·arctan(bx 2 +cx+d)+e (2)

[0060] In the formula, x represents the angle of deviation; a, b, c, d and e are all fitting coefficients.

[0061] S2. Assign the weights of each collision damage factor and each damage evaluation index: use the analytic hierarchy process with objective information correction to correct the weights, use the fuzzy inverse equation method to calculate and select the weights, combine the subjective and objective to determine multiple possible weight vectors as the alternative set of the fuzzy inverse equation method, obtain the Nash equilibrium solution of the multiple weight vectors, and solve the optimal weight;

[0062] In the hierarchical analysis method of this embodiment, a judgment matrix is ​​constructed for each possible situation of each importance and a consistency test is performed. Those that pass the consistency test are retained, and those that fail are discarded. Since the importance levels in conventional judgment matrices are often ambiguous, this method retains the controversial points and lists multiple possibilities to increase the accuracy of the evaluation results.

[0063] In this example, the geometric mean of each damage evaluation index in the judgment matrix is ​​used as the basis for subjective weighting, and the objective data of each index is calculated as a correction term for the geometric mean. The objective data characteristics include:

[0064] 1) Information entropy, which is calculated as follows:

[0065] in,

[0066] Where, e j is the information entropy of the jth evaluation index, k represents the normalized information entropy, and p ij represents the proportion of the i-th damage evaluation index under the j-th damage evaluation index, n is the order of one of the damage evaluation indicators, x ij is the data in the i-th row and j-th column of the comprehensive evaluation matrix, and m is the order of another damage evaluation index;

[0067] 2) Standard deviation, which is calculated as follows:

[0068]

[0069] In the formula, μ is the data mean, x i is the data corresponding to the i-th damage assessment index;

[0070] 3) Pearson correlation coefficient, which is calculated as follows:

[0071]

[0072] Where R ij is the correlation coefficient between the two damage assessment indicators, Cov(X, Y) is the covariance of the two data, s X , s Y is the standard deviation of the two data.

[0073] As mentioned above, the greater the information entropy, the higher the uncertainty of the indicator, the smaller the amount of information provided, and its weight should be relatively small. Conversely, the smaller the information entropy, the greater the weight of the indicator. In a multi-indicator evaluation scheme, when the standard deviation of an indicator is large, it means that the performance of different collision conditions under this indicator varies greatly, so this indicator is more important to the evaluation process. The Pearson correlation coefficient can be used to identify and analyze the correlation between two or more indicators to identify whether there is overlap in the information provided by each indicator about the system or problem, which can reduce redundancy in the model and avoid repeated consideration of the same information during the evaluation process.

[0074] The revised weight determination index is:

[0075]

[0076] Where, α i，j is the data in row i and column j of the judgment matrix, E i is the information entropy of the damage data corresponding to index i, std i is the standard deviation of the damage data corresponding to index i, R i，j is the correlation coefficient of indicator i to indicator j, W i is the weight vector corresponding to index i;

[0077] The method to solve the optimal weight is:

[0078]

[0079] Where ω is the optimal weight, is the weight vector set, β k is the weight coefficient;

[0080] when hour,

[0081] The corresponding linear equations are:

[0082]

[0083] In formula (5), β k is the weight coefficient, is the weight vector set, ω i is the optimal weight of index i; in formula (6), ω G is the optimal weight of indicator G, is the weight vector of the index G in the weight vector concentration, β G is the weight coefficient of indicator G;

[0084] From formula (6), we can get the optimal linear combination β1, β2, ..., β n , normalize it and output the optimal weight according to formula (4);

[0085] When equation (5) is satisfied, ω can be made The deviations of the remaining weights are minimized; through the above settings, the deviation of the weighted sum can be minimized, so that consensus can be reached among different weights. By obtaining the Nash equilibrium solution of multiple groups of weight vectors and determining it as the optimal weight, large differences between groups of weights caused by subjective ambiguity can be avoided.

[0086] S3. Implementation of the evaluation model: Determine the basis for classifying wind turbine damage levels and their corresponding maintenance measures, define a membership function, construct a second-order comprehensive evaluation matrix, and output the damage level through fuzzy comprehensive evaluation. In this embodiment, the basis for classifying wind turbine damage levels and their corresponding maintenance measures are shown in Table 1.

[0087] Table 1 Classification basis of fan damage level and corresponding maintenance measures

[0088]

[0089] In this embodiment, the membership function is a Z-type membership function:

[0090]

[0091] When the index value is positively correlated with the degree of injury, M1(x) is used; when the index value is negatively correlated with the degree of injury, M2(x) is used.

[0092] In this embodiment, the Monte Carlo simulation method is used to process the output result of the single-index membership function. The output membership M is used as the reference value to randomly generate the gain coefficient K. The distribution of K satisfies the normal distribution with M as the mean and standard deviation σ=0.5M, as shown in formula (19):

[0093]

[0094] By statistically analyzing the frequency of the value of the product K*M of the gain coefficient and the single index membership value in each membership interval, the damage degree membership corresponding to each damage level under the current index value is obtained; after determining the single index membership function, the corresponding damage degree membership M can be given according to the index value, and then the Monte Carlo simulation method is used to perform 1000 random simulations. Finally, the number of times the simulation point falls in the membership interval corresponding to each damage level is counted to generate a 1×5 current index value damage membership vector; the four vectors are combined to obtain the first-order fuzzy comprehensive evaluation matrix R1; the weight of the first-order fuzzy evaluation matrix is ​​A1, a1 = [K1, K2, K3, K4], where K1, K2, K3, K4 represent the importance of the four indicators of area damage rate, wind turbine natural frequency, maximum displacement of tower top, and maximum displacement of pile foundation mud surface in the comprehensive fuzzy evaluation;

[0095] Performing fuzzy transformation on the first-order fuzzy comprehensive evaluation matrix R1 and weight A1, we can get the first-order fuzzy comprehensive evaluation result B1:

[0096]

[0097] The B1 calculated for each factor in the collision damage factor set is used as the row vector of the second-order fuzzy comprehensive evaluation matrix to obtain the second-order fuzzy comprehensive evaluation matrix R2. The second-order fuzzy comprehensive evaluation matrix R2 is a 2×5 matrix with a weight vector A2, A2 = [P1, P2], where P1 and P2 represent the membership of the collision speed & ship mass coupling factor and the collision angle in the comprehensive evaluation process, respectively. Fuzzy transformation is performed on the second-order fuzzy comprehensive evaluation matrix R2 and the factor set weight vector A2 to obtain the second-order comprehensive evaluation result B2:

[0098]

[0099] B2 is a 1×5 matrix, where each element represents the degree of membership of the damage level under the current collision conditions to the comments "completely destroyed," "severely damaged," "moderately damaged," "minorly damaged," and "no damage." Finally, according to the maximum membership principle, the comment with the highest membership is considered the actual damage level of the wind turbine, and corresponding maintenance measures are then taken.

[0100] The present invention constructs a two-level fuzzy comprehensive evaluation model for offshore wind turbine collision damage based on fuzzy theory. By establishing a clear basis for determining the damage level and determining the corresponding damage degree membership range, the constructed comprehensive evaluation model can well cope with the ambiguity of the damage level, output an accurate damage level, and thus reasonably arrange corresponding maintenance measures. At the same time, the existing subjective and objective fusion weighting method is improved, and subjective and objective information are combined. The game theory combined weighting method is used to solve the Nash equilibrium solution of multiple groups of weight vectors generated by the ambiguity of subjective judgment, and the optimal weight that can simultaneously reflect expert experience and objective data information is obtained.

[0101] This specific embodiment is merely an explanation of the present invention and is not intended to limit the present invention. After reading this specification, those skilled in the art may make non-creative modifications to this embodiment as needed. However, as long as such modifications are within the scope of the claims of the present invention, they are protected by patent law.

Claims

1. An offshore wind turbine collision damage assessment method based on an improved weighting method, characterized in that: The following steps are involved: S1. Damage data acquisition and data processing: Construct a finite element simulation model of a ship colliding with an offshore wind turbine, simulate collisions under different collision conditions, extract damage indicators, and construct a mathematical model between collision damage factors and damage evaluation indicators; S2. Assign weights to each collision damage factor and each damage evaluation indicator: Use the objective information-corrected analytic hierarchy process to modify the weights, and use the fuzzy inverse equation method to calculate and select the weights. Combine subjective and objective factors to determine multiple sets of possible weight vectors as alternative sets for the fuzzy inverse equation method. Obtain Nash equilibrium solutions for these multiple sets of weight vectors to determine the optimal weights. Among them, the modified weight judgment index is: Where, α i，j is the data in row i and column j of the judgment matrix, E i is the information entropy of the damage data corresponding to index i, std i is the standard deviation of the damage data corresponding to index i, R i，j is the correlation coefficient of indicator i to indicator j, w i is the weight vector corresponding to index i; S3. Implementation of the evaluation model: Determine the basis for classifying wind turbine damage levels and their corresponding maintenance measures, define the membership function, construct a second-order comprehensive evaluation matrix, and output the damage degree through fuzzy comprehensive evaluation.

2. According to the offshore wind turbine collision damage assessment method with the improved weighting method described in claim 1, a judgment matrix is ​​constructed and a consistency test is performed, those that pass the consistency test are retained, and those that fail are discarded.

3. The offshore wind turbine collision damage assessment method with improved weighting method according to claim 1 is characterized in that: In step S2, the geometric mean of each damage assessment index in the judgment matrix is ​​used as the basis for subjective weighting, and the objective data of each index is calculated as a correction term for the geometric mean. The objective data features include: 1) Information entropy, which is calculated as follows: in, Where, e j is the information entropy of the jth evaluation index, k represents the normalized information entropy, and p ij represents the proportion of the i-th damage evaluation index under the j-th damage evaluation index, n is the order of one of the damage evaluation indicators, x ij is the data in the i-th row and j-th column of the comprehensive evaluation matrix, and m is the order of another damage evaluation index; 2) Standard deviation, which is calculated as follows: In the formula, μ is the data mean, x i is the data corresponding to the i-th damage assessment index; 3) Pearson correlation coefficient, which is calculated as follows: Where R ij is the correlation coefficient between the two damage assessment indicators, Cov(X, Y) is the covariance of the two data, s X , s Y is the standard deviation of the two data.

4. The offshore wind turbine collision damage assessment method based on the improved weighting method according to claim 3 is characterized in that: The method to solve the optimal weight is: Where ω is the optimal weight, is the weight vector set, β k is the weight coefficient; when hour, The corresponding linear equations are: In formula (5), β k is the weight coefficient, is the weight vector set, ω i is the optimal weight of index i; in formula (6), ω G is the optimal weight of indicator G, is the weight vector of the index G in the weight vector concentration, β G is the weight coefficient of index G; the optimal linear combination β1, β2, ..., β n , and then normalize it and output the optimal weight according to formula (4).

5. The offshore wind turbine collision damage assessment method with improved weighting method according to claim 1 is characterized in that: In step S1, collision damage factors include collision speed, ship mass, and collision angle; damage evaluation indicators include area damage rate, wind turbine natural frequency, maximum displacement of tower top during collision, and maximum displacement of pile foundation mud surface.

6. The offshore wind turbine collision damage assessment method with improved weighting method according to claim 5 is characterized in that: In step S1, constructing a mathematical model between collision damage factors and damage evaluation indicators includes the following steps: First, the collision damage data is interpolated and the damage index under different collision speeds and ship masses is fitted using the bivariate polynomial equation shown in formula (1): G(x)=g1x 3 +g2y 3 +g3x 2 y+g4y 2 x+g5x 2 +g6y 2 +g7xy+g8x+g9y+g 10 (1) Where x represents the mass of the ship, y represents the collision speed, g1, g2...g 10 represents the fitting coefficient; secondly, the tangent function shown in formula (2) is used to fit the damage index under different collision angles: f(x)=a·arctan(bx 2 +cx+d)+e (2) Where x represents the collision angle; a, b, c, d, and e are fitting coefficients.

7. The offshore wind turbine collision damage assessment method using the improved weighting method according to any one of claims 1 to 6, characterized in that: In step S3, the membership function is a Z-type membership function: When the index value is positively correlated with the degree of injury, M1(x) is used; when the index value is negatively correlated with the degree of injury, M2(x) is used.

8. The offshore wind turbine collision damage assessment method with improved weighting method according to claim 7 is characterized in that: In step S3, the Monte Carlo simulation method is used to process the output results of the membership function of a single damage evaluation index to generate the damage membership vector of the index value. The damage membership vectors of all index values ​​are combined to obtain the first-order fuzzy comprehensive evaluation matrix R1. The weight of the first-order fuzzy comprehensive evaluation matrix is ​​A1. The first-order fuzzy comprehensive evaluation matrix R1 and the weight A1 are fuzzy transformed to obtain the first-order fuzzy comprehensive evaluation result B1: The B1 calculated for each factor in the collision damage factor set is used as the row vector of the second-order fuzzy comprehensive evaluation matrix to obtain the second-order fuzzy comprehensive evaluation matrix R2. The weight vector of the collision damage factor set is A2. After fuzzy transformation of the second-order fuzzy comprehensive evaluation matrix R2 and the weight vector A2 of the collision damage factor set, the second-order comprehensive evaluation result B2 can be obtained:

9. The offshore wind turbine collision damage assessment method with improved weighting method according to claim 8 is characterized in that: In step S3, A1 = [K1, K2, K3, K4], where K1, K2, K3, and K4 represent the membership of the four indicators of area damage rate, wind turbine natural frequency, maximum displacement of tower top, and maximum displacement of pile foundation mud surface in the comprehensive fuzzy evaluation; A2 = [P1, P2], where P1 and P2 represent the membership of the collision speed & ship mass coupling factor and the collision angle in the comprehensive evaluation process, respectively.

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