Offshore wind plant fan safety risk evaluation method and system based on AHP-LEC method

Abstract

The invention discloses an offshore wind plant fan safety risk evaluation method and system based on an AHP-LEC method. The method comprises the following steps: constructing a three-level evaluation system comprising a target layer, a criterion layer and a scheme layer based on an analytic hierarchy process AHP; every two risk category indexes of the criterion layer are subjected to importance comparison, an n * n-order judgment matrix is established, and n is the number of risk categories; calculating the maximum eigenvalue and the corresponding eigenvector of the judgment matrix through an eigenvector method, obtaining the weight coefficient of each risk category after normalization processing, and performing consistency check; carrying out quantitative scoring on the identified risk factors by adopting an operation condition risk evaluation method LEC, carrying out weighted calculation on an LEC scoring result and a weight coefficient obtained by an AHP method, and outputting a final risk evaluation result; the method can reflect the dynamic change of risk factors, considers the weight difference between different factors, overcomes the subjectivity of manual assignment, and improves the risk management and control accuracy.

Description

Technical Field

[0001] The present invention belongs to the technical field of offshore wind power engineering, and in particular relates to a safety risk assessment method and system for wind turbines in offshore wind farms based on an AHP-LEC method. Background Art

[0002] As a key component of clean energy, offshore wind power has experienced rapid global growth in recent years. As the scale of offshore wind farms continues to expand and the number of wind turbines continues to increase, the complexity of their operation and maintenance has also increased significantly. Offshore wind farms are typically located in harsh marine environments, where wind turbines are constantly exposed to natural factors such as high salt spray, strong winds and waves, and humidity, resulting in high equipment failure rates and safety hazards. To ensure the stable operation of wind farms, the industry generally adopts measures such as regular inspections, remote monitoring, and preventive maintenance. However, the unique offshore operating environment and the difficulty of operation and maintenance place higher demands on safety risk management.

[0003] Currently, offshore wind farm wind turbine safety risk assessment primarily uses the LEC (Likelihood-Exposure-Consequence) evaluation method, which uses semi-quantitative analysis to assess hazard sources. The LEC method comprehensively calculates three dimensions: the likelihood of an accident (L), the frequency of personnel exposure to hazardous environments (E), and the potential consequences of an accident (C). This method derives a risk value to assess the hazard level. This method is simple to use and can quickly identify high-risk factors, leading to its widespread use in the wind power industry.

[0004] Although the LEC evaluation method has played a certain role in the safety risk assessment of offshore wind farms, it is a static evaluation method that cannot reflect the dynamic changes of risk factors and does not consider the weight differences between different factors, which may cause the evaluation results to deviate from the actual situation. In addition, the traditional method ignores the dynamic impact of the risk transmission path and is difficult to accurately portray the risk evolution process under the coupling of multiple factors, thus affecting the accuracy of risk management. Summary of the Invention

[0005] Purpose of the invention: The purpose of the present invention is to provide an offshore wind farm wind turbine safety risk assessment method based on the AHP-LEC method that can reflect the dynamic changes of risk factors, consider the weight differences between different factors, and improve the accuracy of risk management; on the other hand, to provide an offshore wind farm wind turbine safety risk assessment system based on the AHP-LEC method.

[0006] Technical solution: The wind turbine safety risk assessment method of the present invention comprises the following steps:

[0007] (1) Based on the analytic hierarchy process (AHP), a three-level evaluation system consisting of a target layer, a criterion layer, and a solution layer is constructed. The target layer is the safety risk assessment of wind turbines in offshore wind farms. The criterion layer includes at least two risk category indicators. The solution layer includes consequence indicators corresponding to various risks. This can systematically sort out the hierarchical relationship of safety risks of wind turbines in offshore wind farms, ensuring that the evaluation indicators are comprehensive and clearly structured. This step decomposes complex risk factors into a hierarchical structure that can be quantified and evaluated, providing a logical framework for subsequent weight calculation and risk quantification, and improving the scientific nature and operability of the evaluation system.

[0008] (2) Compare the importance of each risk category indicator at the criterion level and establish an n×n order judgment matrix, where n is the number of risk categories. This matrix can objectively reflect the relative weight relationship between different risk factors and avoid the arbitrariness of subjective assignment in traditional assessment methods. This step combines expert experience with standardized scales to ensure the rationality of weight allocation and lay the foundation for the subsequent accurate calculation of risk weights.

[0009] (3) The maximum eigenvalue and corresponding eigenvector of the judgment matrix are calculated by the eigenvector method. After normalization, the weight coefficient of each risk category is obtained and consistency test is performed. This can effectively solve the logical consistency problem when allocating weights of multiple factors, ensure that the judgment matrix conforms to objective laws, improve the credibility of weight calculation through normalization and consistency verification, avoid distortion of evaluation results due to subjective bias, and enhance the reliability of risk assessment.

[0010] (4) The identified risk factors are quantitatively scored using the operating condition hazard evaluation method (LEC). The LEC scoring results are weighted with the weight coefficients obtained by the AHP method to output the final risk assessment results, realizing the integration of qualitative analysis and quantitative evaluation, and improving the accuracy and practicality of risk assessment. This step comprehensively considers the probability of risk occurrence, exposure frequency and severity of consequences, and outputs an intuitive risk assessment value, providing data support for wind farm operation and maintenance decision-making and optimizing risk control measures.

[0011] Preferably, the risk category indicators of the criterion layer in step 1 include human factor risk, material factor risk and environmental factor risk;

[0012] The sub-criteria layer corresponding to the human factor risk includes working without wearing a seat belt;

[0013] The sub-criteria layers corresponding to the physical risk factors include frequent vibration of wind turbines, fatigue fracture of high-strength bolts, working without wearing safety belts, and severe cable wear;

[0014] The sub-criteria layers corresponding to the environmental risk factors include typhoons, wind turbine blade icing, and fault elimination operations in high-wind weather;

[0015] The consequence indicators of the scenario layer include wind turbine tower collapse, object impact, fall from height, fire and other injuries.

[0016] By subdividing the risk categories at the criterion level and further clarifying the specific risk factors at the sub-criteria level, it is possible to accurately identify the key risk sources in the operation and maintenance of offshore wind farms, avoiding omissions or duplications caused by vague classification in traditional assessments. At the same time, the solution level associates various risks with specific consequence indicators, establishing a direct mapping relationship from "risk factors to consequences", making the evaluation results more targeted and operational, thereby helping operation and maintenance personnel quickly locate high-risk links and formulate prevention and control measures, thereby improving the systematic and effective safety management of wind farms.

[0017] Preferably, the establishment of the judgment matrix in step 2 includes:

[0018] (21) Designing a risk importance scale with a 1-9 scale, which corresponds to the scale meaning of the preset AHP method;

[0019] (22) distributing the scale to experts and operation and maintenance personnel in the field of offshore wind power, and having them compare and score different risk factors in the criterion layer;

[0020] (23) Collect the scoring results of experts and operation and maintenance personnel and calculate the average value to form a wind turbine safety scale for offshore wind farms;

[0021] (24) Construct a judgment matrix A based on the scale table:

[0022]

[0023] Among them, a ij >0,a ij =1 / a ij And a ii =1, where i,j=1,2,...,n, and n is the number of risk categories.

[0024] By adopting a 1-9 point scaling method to construct a judgment matrix and integrating the wisdom of a group of experts to compare and score risk factors pairwise, the problems of strong subjectivity and poor consistency in traditional risk assessment can be effectively solved. Specifically: a standardized scaling system provides a unified quantitative basis for expert judgment, avoiding deviations caused by different scoring scales; the processing method of taking the average after independent scoring by multiple experts retains field experience while reducing individual subjective influences; a judgment matrix that satisfies the mathematical properties of reciprocity (aij=1 / aji) and reflexivity (aii=1) is constructed to ensure the theoretical rigor of weight calculation; this technical solution significantly improves the scientific nature and reliability of the judgment of the relative importance of risk factors, lays a data foundation for the subsequent accurate calculation of weight coefficients, and enables risk assessment results to more objectively reflect the actual risk status of offshore wind farms.

[0025] Preferably, step 3 includes:

[0026] (31) Calculate the maximum eigenvalue λ of the judgment matrix A max and its corresponding eigenvector W, and normalize the eigenvector W to obtain the weight vector w i =(w1, w2, ..., w6), where w i Indicates the weight value of each risk category;

[0027] (32) To perform consistency test, first calculate the consistency index CI:

[0028]

[0029] CI = 0 means that the judgment matrix is completely consistent. The larger the CI, the more serious the inconsistency of the judgment matrix;

[0030] According to the judgment matrix order n and the scale meaning of the AHP method, the random consistency index RI is obtained:

[0031]

[0032] Calculate the consistency ratio CR:

[0033]

[0034] If CR<0.1, the judgment matrix passes the consistency test, otherwise the judgment matrix A is modified;

[0035] (33) Perform hierarchical total ranking and total ranking consistency test

[0036] In the total ranking, CI satisfies the following formula:

[0037] CI k =CI (k-1) w (k-1)

[0038] Among them, CI k is the hierarchical single ranking consistency index of the pairwise comparison of factors in the kth layer, w (k-1) is the total ranking vector of the total target at the (k-1)th layer;

[0039] Remember RI k =RI (k-1) ω (k-1) ,but If CR<0.1, the judgment matrix passes the consistency test, otherwise the judgment matrix A is modified.

[0040] Through a systematic weight calculation and consistency verification mechanism, the scientificity and reliability of the risk assessment process are achieved; this technical solution adopts the eigenvector method for weight calculation, and through multi-level logical consistency verification, it effectively balances the subjectivity of expert experience and the objectivity of the mathematical model; among them, the weight calculation method based on matrix theory ensures the rationality of the weight distribution of each risk factor, while the strict consistency verification mechanism can automatically identify and correct logical deviations in the evaluation process, so that expert judgment is more in line with objective laws; this dual guarantee mechanism not only retains the experience value of domain experts, but also eliminates the arbitrariness of human judgment through mathematical methods. The final output risk weight reflects both actual engineering needs and theoretical rigor, providing a scientific basis for subsequent risk quantification analysis, and significantly improving the reliability and validity of the entire evaluation system.

[0041] Preferably, the maximum characteristic root λ max The relationship between and the eigenvector W is as follows:

[0042]

[0043] By establishing the maximum characteristic root λ max The mathematical relationship between the risk factor and the eigenvector W provides a rigorous theoretical basis for the scientific calculation of risk weights. This mathematical relationship ensures that the weight calculation process strictly follows the matrix operation rules, so that the importance ranking of each risk factor has mathematical internal consistency. Through this theoretical model, the expert experience judgment can be effectively converted into a quantifiable weight coefficient, which not only retains the expert's professional understanding of the relative importance of risk factors, but also avoids the subjective arbitrariness of human weighting. This weight calculation method based on matrix theory makes the final risk weight both consistent with mathematical logic and reflecting engineering reality, significantly improving the objectivity and credibility of the risk assessment results, and providing a reliable quantitative basis for subsequent risk evaluation.

[0044] Preferably, the quantitative scoring of the identified risk factors using LEC in step 4 includes:

[0045] (41) Distribute LEC evaluation forms to experts and operation and maintenance personnel in the offshore wind power field to obtain independent scores of risk factors from each evaluator, where the L value represents the probability score of a safety accident, the E value represents the frequency score of exposure to hazardous environments, and the C value represents the severity score of the accident consequences;

[0046] (42) Calculate the mean score of each risk factor:

[0047]

[0048] Where n is the number of evaluators;

[0049] (43) The degree of danger of different risk categories is obtained based on the preset LEC score table.

[0050] By integrating the multi-dimensional quantitative indicators of the LEC evaluation method with the expert group decision-making mechanism, an accurate quantitative assessment of the risk factors of offshore wind farms is achieved; the technical solution innovatively combines the three dimensions of risk occurrence possibility (L), exposure frequency (E) and accident consequences (C). Through the processing method of independent expert scoring and mean calculation, it not only retains the accuracy of professional judgment but also effectively reduces individual subjective bias; based on the preset LEC score table, the risk level is graded, and the originally complex safety risks are transformed into intuitive and comparable quantitative indicators, which not only improves the objectivity and operability of risk assessment, but also provides a unified standard for the horizontal comparison of different risk factors, significantly enhancing the scientific basis for risk management and control decisions.

[0051] Preferably, the calculation process of the final risk assessment result in step 4 includes:

[0052] (44) Based on the consequence indicators P1, P2, P3, and P4 refined at the criterion level, repeat the operation of step 2 to calculate the weight vector a1=(k 11 ,k 21 ,k 31 ,k 41 ,k 51 ,k 61 );

[0053] a2=(k 12 ,k 23 ,k 32 ,k 42 ,k 52 ,k 62 );...;a6=(k 16 ,k 26 ,k 36 ,k 46 ,k 56 ,k 66 );

[0054] (45) The criterion layer weight vector W = (w1, w2, ..., w6) obtained in step 3 is weighted with the C value in the LEC method in step 4 to calculate the comprehensive risk assessment values D1, D2, D3, and D4, respectively:

[0055]

[0056] By weightedly integrating the weight coefficients determined by the AHP method with the LEC scoring results, a scientific transformation of risk assessment from qualitative analysis to quantitative calculation is achieved. The technical solution innovatively constructs a multi-level, multi-dimensional comprehensive evaluation model, which not only takes into account the relative importance differences of different consequence indicators, but also incorporates key parameters such as the probability of risk occurrence, degree of exposure and severity of consequences, so that the final output comprehensive risk assessment value can comprehensively and objectively reflect the actual risk status of offshore wind farms; this quantitative evaluation method not only improves the accuracy and comparability of risk assessment results, but also provides reliable data support for the formulation of differentiated risk control measures, effectively improving the pertinence and effectiveness of wind farm safety management.

[0057] Preferably, the wind turbine safety risk assessment method further includes constructing a wind turbine fault chain dynamic conduction model, specifically including:

[0058] Establish fault transmission paths based on historical fault data and expert experience, and use them as dynamic transmission risk weights to quantify the transmission probability of each path;

[0059] The dynamic transmission risk weight is added as a new criterion layer indicator into the AHP evaluation system, and the judgment matrix and weight coefficient are updated.

[0060] By constructing a dynamic conduction model of wind turbine fault chains, the fault propagation characteristics are innovatively incorporated into the risk assessment system, significantly improving the dynamic adaptability of the evaluation results. The technical solution integrates historical fault data and expert experience to establish a quantifiable fault conduction path probability model, and incorporates it into the AHP evaluation system as a dynamic weight indicator, achieving an organic combination of static risk factor analysis and dynamic fault propagation simulation. This method can not only reflect the risk characteristics of a single fault point, but also capture the compound risk effects brought about by the chain reaction of faults, extending risk assessment from isolated node analysis to system-level network analysis, providing a more comprehensive decision-making basis for preventing systemic failures, and effectively enhancing the foresight and reliability of wind farm risk prevention and control.

[0061] Preferably, the wind turbine safety risk assessment method also includes setting vibration, stress and displacement sensors on key components of the wind turbine to collect vibration, stress and displacement data in real time; setting primary risk thresholds and secondary risk thresholds, and when the monitoring data exceeds the primary threshold, executing primary blocking of speed regulation or blade angle adjustment, and when the monitoring data continues to exceed the secondary threshold, triggering automatic shutdown and pushing early warning to the operation and maintenance platform.

[0062] By building a multi-parameter real-time monitoring and graded early warning mechanism, dynamic all-weather prevention and control of offshore wind turbine safety risks is achieved; the technical solution innovatively combines multi-dimensional sensor data such as vibration, stress and displacement with graded threshold management. By setting a dual line of defense of primary and secondary risk thresholds, it can not only take preventive adjustment measures at the embryonic stage of risks, but also initiate protective shutdowns in time when risks escalate; this intelligent risk response mechanism not only significantly improves the timeliness and accuracy of fault warnings, but also achieves precise and minimized risk management through a graded disposal strategy, effectively avoiding the problems of over-protection or delayed response that may result from traditional single threshold control, and providing intelligent technical guarantees for the safe operation of offshore wind turbines.

[0063] The wind turbine safety risk assessment system of the present invention comprises:

[0064] The evaluation system construction module is used to construct a three-level evaluation system based on the analytic hierarchy process (AHP), which includes a target layer, a criterion layer, and a solution layer. The target layer is the safety risk assessment of wind turbines in offshore wind farms. The criterion layer includes at least two risk category indicators. The solution layer includes consequence indicators corresponding to each risk category.

[0065] The judgment matrix establishment module is used to compare the importance of each risk category indicator at the criterion layer and establish an n×n order judgment matrix, where n is the number of risk categories;

[0066] The weight calculation module is used to calculate the maximum eigenvalue and corresponding eigenvector of the judgment matrix through the eigenvector method, obtain the weight coefficient of each risk category after normalization, and perform consistency test;

[0067] The risk quantification module is used to quantitatively score the identified risk factors using the operating condition hazard evaluation method LEC, perform weighted calculation on the LEC score results and the weight coefficient obtained by the AHP method, and output the final risk assessment results.

[0068] Beneficial effects: Compared with the existing technology, the present invention has the following significant advantages: 1. It can reflect the dynamic changes of risk factors, consider the weight differences between different factors, overcome the subjectivity of human assignment, and improve the accuracy of risk management; 2. Through the AHP-LEC fusion evaluation method, it realizes the combination of quantitative and qualitative analysis, and improves the scientificity and comprehensiveness of risk assessment; 3. Based on the dynamic conduction model of the fault chain and real-time monitoring data, it can realize risk warning and active prevention and control, and reduce the risk of sudden failure of wind turbines; 4. The use of expert group decision-making and data mean processing can reduce individual evaluation deviation and enhance the objectivity and stability of evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 is a flow chart of the method of the present invention;

[0070] Figure 2 It is the hierarchical structure model based on the AHP method of the present invention;

[0071] Figure 3 This is a schematic diagram of the wind turbine "fault chain" model of the present invention;

[0072] Figure 4 Schematic diagram of the risk threshold transmission mechanism of the present invention. DETAILED DESCRIPTION

[0073] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0074] The present invention provides an offshore wind farm wind turbine safety risk assessment method based on the AHP-LEC method, which is applied to an offshore wind farm wind turbine safety risk assessment system based on the AHP-LEC method. The method can comprehensively consider qualitative and quantitative factors, and conduct weighted analysis on offshore wind farm wind turbine hazard sources by using the AHP method. The weighted hazard source weights and their corresponding L, E, and C values are introduced into the LEC method to comprehensively assess the offshore wind farm wind turbine hazard risk score, thereby reducing the potential operational risks of wind turbine operation and maintenance personnel.

[0075] like Figure 1 As shown, the wind turbine safety risk assessment method of the present invention includes the following steps:

[0076] Step 1: Based on the AHP method, a hierarchical model is established, namely, the target layer T (safety risk assessment of offshore wind turbines), the criterion layer A (different risk categories), and the solution layer P (different risk consequences) of the safety risk assessment are determined;

[0077] Using the Analytic Hierarchy Process (AHP), the simple intensive AHP decomposes the problem into the target layer and the criterion layer, such as Figure 2 As shown in the figure, the criterion layer can be generally divided into human factor risk, material factor risk and environmental factor risk. Then, according to the three major risk categories, specific risk types are subdivided to list six sub-criteria layers (typhoon, frequent vibration of wind turbines, fatigue fracture of high-strength bolts, ice coating of wind turbine blades, failure to wear safety belts, severe cable wear, and fault elimination work in windy weather, respectively denoted as A1, A2, ..., A6) and the corresponding four scenario layers (wind turbine tower collapse, object impact, other injuries, falling from height, and fire, respectively denoted as P1, P2, P3, and P4).

[0078] Step 2: Compare the different risks at the criterion level in pairs and establish a judgment matrix based on the comparison;

[0079] A risk importance scale was distributed to experts and operation and maintenance personnel in the offshore wind power field. Using a 1-9 scale, the experts and operation and maintenance personnel were asked to compare the different risk factors in the criterion layer. The meaning of the AHP scale is shown in Table 1:

[0080] Table 1 Meaning of each scale of AHP method

[0081]

[0082]

[0083] The average value of each person's results was taken to obtain the offshore wind farm wind turbine safety scale table as shown in Table 2:

[0084] Table 2 Safety scale of wind turbines in offshore wind farms

[0085]

[0086] On this basis, the judgment matrix A is established, that is, the quasi-measurement layer is constructed into a matrix form of (aij)m×n, as shown below:

[0087]

[0088] The elements in A satisfy aij>0, aij=1 / aij and aii=1.

[0089] Step 3: Perform hierarchical ranking and one-time inspection to determine the risk weights of various operational aspects of the offshore wind farm;

[0090] (31) Corresponding to the eigenvector W of the judgment matrix A with the largest eigenroot being λmax, the weight vector wi = (w1, w2, ..., w6) is obtained by normalizing the vector W, where wi is the component of W, which refers to the weight and corresponds to the single order of its corresponding element. The relationship between the largest eigenroot and the eigenvector satisfies the following:

[0091]

[0092] (32) Since manual judgment may be inconsistent, it is necessary to use the three indicators CI, RI, and CR to determine whether the judgment matrix is acceptable. The following consistency index CI test is used to obtain the consistency index:

[0093]

[0094] CI = 0 means that the judgment matrix is completely consistent. The larger the CI, the more serious the inconsistency of the judgment matrix. At this time, in order to measure the size of CI, the random one-time index RI is introduced. The matrix order and its corresponding RI value can be determined according to the value table 3:

[0095]

[0096] Table 3 Matrix order and corresponding RI value

[0097] Matrix order n 1 2 3 4 5 6 7 8 9 10 11 12 13 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51 1.54 1.56

[0098] Considering that the deviation from consistency may be caused by random reasons, when checking whether the judgment matrix has satisfactory consistency, it is also necessary to compare CI and random consistency index RI to obtain the consistency ratio CR, which is as follows:

[0099]

[0100] Generally, if CR < 0.1, the judgment matrix is considered to have passed the consistency test; otherwise, it does not have satisfactory consistency and the judgment matrix A and weight values need to be appropriately adjusted.

[0101] (33) In order to determine the importance weight of each factor for the target layer, a total hierarchical ranking and consistency test are required. The CI in the total ranking satisfies the following formula:

[0102] CI k =CI (k-1) w (k-1)

[0103] Where: CI k is the hierarchical single ranking consistency index of the pairwise comparison of factors in the kth layer, w (k-1) is the total ranking vector of the total target at the (k-1)th layer.

[0104] Taking the calculation of the risk of wind turbine tower collapse (P1) relative to typhoon (A1) as an example, a 2×2 matrix is constructed according to Table 4. Following the method in step 2, experts and operation and maintenance personnel in the offshore wind power field are asked to compare wind turbine tower collapse (P1) and object impact and other damage (P2) in pairs. According to step (31), hierarchical single sorting is performed to obtain the weights of wind turbine tower collapse (P1) and object impact and other damage (P2). Then, this weight can be used as the risk score of wind turbine tower collapse (P1) and object impact and other damage (P2) for typhoon (A1).

[0105] Table 4 Judgment Matrix

[0106]

[0107]

[0108] Taking the wind turbine tower collapse (P1) as an example at the scenario level, the total risk score weight k1 of the wind turbine tower collapse (P1) = the risk score of the wind turbine tower collapse (P1) for typhoon (A1) × the weight w1 of typhoon (A1) + the risk score of the wind turbine tower collapse (P1) for frequent vibration of the wind turbine and fatigue fracture of high-strength bolts (A2) × the weight w2 of frequent vibration of the wind turbine and fatigue fracture of high-strength bolts (A2).

[0109] RI k =RI (k-1) ω (k-1)

[0110]

[0111] Similarly, if CR < 0.1, the judgment matrix is considered to have passed the consistency test.

[0112] Step 4: Based on the risk types of offshore wind farm wind turbines and combined with the AHP risk factor weight analysis, the identified risks are evaluated at the LEC level to complete the offshore wind farm wind turbine safety risk assessment based on the AHP-LEC method.

[0113] (41) The LEC evaluation form was distributed to experts and operation and maintenance personnel in the field of offshore wind power, and the L (likelihood of safety accidents), E (frequency of exposure to hazardous environments), and C (consequences of accidents) judged by each person were obtained. Assuming that the L values of risk judged by different people are L1, L2, ..., Ln, then (L1 + L2 + ... + Ln) / n is used to obtain the average value of L. Similarly, we can get the mean of E and C Then derive the risk score The specific LEC method values are shown in Table 5, and the degree of danger of different risks is judged based on Table 6.

[0114] Table 5 Parameter values and corresponding situations of LEC method

[0115]

[0116]

[0117] Table 6 The values of the risk index D and the corresponding risk levels of the LEC method

[0118] Danger Index D Danger level of hidden dangers ≥320 Extremely dangerous [160,320) Highly dangerous [70,160) Significant danger [20,70) General hazards <20 Slightly dangerous

[0119] Based on the consequence indicators P1, P2, P3, and P4 refined at the criterion level, repeat step 2 to calculate the weight vector a1=(k 11 ,k 21 ,k 31 ,k41 ,k 51 ,k 61 );

[0120] a2=(k 12 ,k 23 ,k 32 ,k 42 ,k 52 ,k 62 );...;a6=(k 16 ,k 26 ,k 36 ,k 46 ,k 56 ,k 66 ); perform weighted calculation on the criterion layer weight vector W = (w1, w2, ..., w6) obtained in step 3 and the C value in the LEC method in step 4 to calculate the comprehensive risk assessment values D1, D2, D3, and D4 respectively:

[0121]

[0122] The final evaluation results of offshore wind farm wind turbine safety using the LEC method are shown in Table 7:

[0123] Table 7 Evaluation results of wind turbine safety in offshore wind farms using the LEC method

[0124]

[0125]

[0126] Step 5: Based on historical fault data and expert experience, construct a wind turbine "fault chain" model (such as Figure 3 The probability of the conduction path (bolt breakage → blade imbalance → tower collapse) is quantified and used as a new dimension of the AHP criterion layer (dynamic conduction risk weight). The risk weight matrix is updated to achieve dynamic optimization of the evaluation results.

[0127] Step 6: Deploy IoT sensors on key components of wind turbines (such as bolts and blades) to collect vibration, stress, displacement and other data in real time; Figure 4As shown in the figure, a risk threshold is set. When the monitoring data exceeds the threshold, a two-level blocking mechanism is triggered: primary blocking: local stress is relieved by adjusting the blade angle or reducing the rotational speed; advanced blocking: if the primary blocking is ineffective and the stress exceeds the secondary threshold, an emergency shutdown is automatically triggered and an early warning is sent to the operation and maintenance platform. For example, at an offshore wind farm in Jiangsu, vibration sensors were deployed at the bolted connections of wind turbines, with a stress threshold set at 500 MPa. When the sensors detected a bolt stress of 550 MPa, the system automatically triggered blade angle adjustment (primary blocking) and initiated a dynamic update of the AHP-LEC model. If the stress continued to rise to 600 MPa, the system executed an emergency shutdown (advanced blocking) and simultaneously generated a fault report and pushed it to the operation and maintenance terminal.

Claims

1. A wind turbine safety risk assessment method for offshore wind farms based on the AHP-LEC method, characterized in that: The following steps are involved: (1) Based on the analytic hierarchy process (AHP), a three-level evaluation system consisting of a target layer, a criterion layer, and a solution layer was constructed. The target layer is the safety risk assessment of wind turbines in offshore wind farms. The criterion layer includes at least two risk category indicators. The solution layer includes consequence indicators corresponding to each risk category. (2) Compare the importance of each risk category indicator in the criterion layer and establish an n×n order judgment matrix, where n is the number of risk categories; (3) Calculate the maximum eigenvalue and corresponding eigenvector of the judgment matrix by the eigenvector method, obtain the weight coefficient of each risk category after normalization, and perform consistency test; (4) The identified risk factors are quantitatively scored using the operating condition hazard evaluation method (LEC). The LEC score results are weighted with the weight coefficients obtained by the AHP method to output the final risk assessment results.

2. The wind turbine safety risk assessment method according to claim 1, characterized in that: The risk category indicators of the criterion layer described in step 1 include human factor risk, material factor risk and environmental factor risk; The sub-criteria layer corresponding to the human factor risk includes working without wearing a seat belt A1; The sub-criteria layers corresponding to the physical risk factors include frequent vibration of wind turbines, fatigue fracture of high-strength bolts A2, working without wearing safety belts A1, and severe cable wear A3; The sub-criteria layers corresponding to the environmental risk factors include typhoon A4, wind turbine blade icing A5, and windy weather fault elimination operations A6; The consequence indicators of the scenario layer include wind turbine tower collapse P1, object impact and other injuries P2, falling from height P3, and fire P4.

3. The wind turbine safety risk assessment method according to claim 1, characterized in that: The establishment of the judgment matrix in step 2 includes: (21) Designing a risk importance scale with a 1-9 scale, which corresponds to the scale meaning of the preset AHP method; (22) distributing the scale to experts and operation and maintenance personnel in the field of offshore wind power, and having them compare and score different risk factors in the criterion layer; (23) Collect the scoring results of experts and operation and maintenance personnel and calculate the average value to form a wind turbine safety scale for offshore wind farms; (24) Construct a judgment matrix A based on the scale table: Among them, a ij >0,a ij =1 / a ij And a ii =1, where i,j=1,2,...,n, and n is the number of risk categories.

4. The wind turbine safety risk assessment method according to claim 1, characterized in that: Step 3 includes: (31) Calculate the maximum eigenvalue λ of the judgment matrix A max and its corresponding eigenvector W, and normalize the eigenvector W to obtain the weight vector w i =(w1, w2, ..., w6), where w i Indicates the weight value of each risk category; (32) To perform consistency test, first calculate the consistency index CI: CI = 0 means that the judgment matrix is completely consistent. The larger the CI, the more serious the inconsistency of the judgment matrix; According to the judgment matrix order n and the scale meaning of the AHP method, the random consistency index RI is obtained: Calculate the consistency ratio CR: If CR<0.1, the judgment matrix passes the consistency test, otherwise the judgment matrix A is modified; (33) Perform hierarchical total ranking and total ranking consistency test In the total ranking, CI satisfies the following formula: CI k =CI (k-1) In (k-1) Among them, CI k is the hierarchical single ranking consistency index of the pairwise comparison of factors in the kth layer, w (k-1) is the total ranking vector of the total target at the (k-1)th layer; Remember RI k =RI (k-1) ω (k-1) ,but If CR<0.1, the judgment matrix passes the consistency test, otherwise the judgment matrix A is modified.

5. The wind turbine safety risk assessment method according to claim 4, characterized in that: The largest characteristic root λ max The relationship between and the eigenvector W is as follows:

6. The wind turbine safety risk assessment method according to claim 1, characterized in that: The quantitative scoring of the identified risk factors using LEC described in step 4 includes: (41) Distribute LEC evaluation forms to experts and operation and maintenance personnel in the offshore wind power field to obtain independent scores of risk factors from each evaluator, where the L value represents the probability score of a safety accident, the E value represents the frequency score of exposure to hazardous environments, and the C value represents the severity score of the accident consequences; (42) Calculate the mean score of each risk factor: Where n is the number of evaluators; (43) The degree of danger of different risk categories is obtained based on the preset LEC score table.

7. The wind turbine safety risk assessment method according to claim 1, characterized in that: The calculation process of the final risk assessment result described in step 4 includes: (44) Based on the refined consequence indicators P1, P2, P3, and P4 at the criterion level, repeat the operation in step 2 and calculate the weight vector a1=(k 11 ,k 21 ,k 31 ,k 41 ,k 51 ,k 61 ); a2=(k 12 ,k 23 ,k 32 ,k 42 ,k 52 ,k 62 );...;a6=(k 16 ,k 26 ,k 36 ,k 46 ,k 56 ,k 66 ); (45) The criterion layer weight vector W = (w1, w2, ..., w6) obtained in step 3 is weighted with the C value in the LEC method in step 4 to calculate the comprehensive risk assessment values D1, D2, D3, and D4, respectively:

8. The wind turbine safety risk assessment method according to claim 1, characterized in that: It also includes building a dynamic conduction model of the wind turbine fault chain, including: Establish fault transmission paths based on historical fault data and expert experience, and use them as dynamic transmission risk weights to quantify the transmission probability of each path; The dynamic transmission risk weight is added as a new criterion layer indicator into the AHP evaluation system, and the judgment matrix and weight coefficient are updated.

9. The wind turbine safety risk assessment method according to claim 1, characterized in that: It also includes installing vibration, stress and displacement sensors on key components of the wind turbine to collect vibration, stress and displacement data in real time; setting primary risk thresholds and secondary risk thresholds. When the monitoring data exceeds the primary threshold, primary blocking of speed regulation or blade angle adjustment is executed. When the monitoring data continues to exceed the secondary threshold, automatic shutdown is triggered and an early warning is pushed to the operation and maintenance platform.

10. A wind turbine safety risk assessment system for offshore wind farms based on the AHP-LEC method, characterized in that: include: The evaluation system construction module is used to construct a three-level evaluation system based on the analytic hierarchy process (AHP), which includes a target layer, a criterion layer, and a solution layer. The target layer is the safety risk assessment of wind turbines in offshore wind farms. The criterion layer includes at least two risk category indicators. The solution layer includes consequence indicators corresponding to each risk category. The judgment matrix establishment module is used to compare the importance of each risk category indicator at the criterion layer and establish an n×n order judgment matrix, where n is the number of risk categories; The weight calculation module is used to calculate the maximum eigenvalue and corresponding eigenvector of the judgment matrix through the eigenvector method, obtain the weight coefficient of each risk category after normalization, and perform consistency test; The risk quantification module is used to quantitatively score the identified risk factors using the operating condition hazard evaluation method LEC, perform weighted calculation on the LEC score results and the weight coefficient obtained by the AHP method, and output the final risk assessment results.

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