Offshore wind plant disaster risk evaluation method based on analytic hierarchy process

Abstract

The invention discloses an offshore wind power plant disaster risk evaluation method based on an analytic hierarchy process. The method comprises the following steps: establishing a three-level risk evaluation index system comprising a target layer, a criterion layer and an index layer; constructing a comprehensive judgment matrix among the indexes based on an analytic hierarchy process, and calculating index weights; historical typhoon data and wind power plant data are collected, and an index historical data set is constructed; based on the index weight and the index historical data set, performing normalized score assignment on the index, and calculating a risk score of the disaster of the offshore wind plant under the typhoon effect; dividing the risk scores to obtain a risk grade division table, comparing the risk scores with the risk grade division table to obtain the disaster risk grade of the offshore wind plant under the typhoon effect, and completing risk evaluation. The method solves the problems that in the prior art, research objects are wide, index selection is extensive, and the method is difficult to apply to a specific disaster-bearing body, namely an offshore wind power plant, in offshore natural disaster-causing risk evaluation research.

Description

Technical Field

[0001] The present invention relates to the technical field of wind farm disaster risk assessment, and in particular to an offshore wind farm disaster risk assessment method based on a hierarchy analysis method. Background Art

[0002] Offshore wind turbine structures are directly exposed to variable or even extreme climatic conditions, which can easily cause damage to the wind turbine structure. At present, most of the work focuses on the risk analysis or loss assessment of marine natural disasters to a certain region or industry. Their characterization of danger is relatively mature, and they usually break it down into several most representative disaster factors, such as defining typhoon danger as: wind speed, typhoon waves, duration, etc., and defining storm surge danger as: water increase, flooding depth, etc. However, the disaster-bearing body in these research systems is often not single, but a combination of personnel and various building structures. This also leads to the lack of detailed selection of indicators for exposure and vulnerability in the evaluation system. The two are combined and considered as "vulnerability", and the indicators are not disassembled and selected based on the dynamic response characteristics of the disaster-bearing body, but the vulnerability is generally defined as the vulnerability of a building or even a certain industry.

[0003] Based on the above reasons, the present invention proposes a disaster risk assessment method for offshore wind farms based on the analytic hierarchy process. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the existing technology in the research on the risk assessment of natural disasters at sea, such as broad research objects, extensive indicator selection, and difficulty in application to the specific disaster-prone body of offshore wind farms, and to propose a disaster risk assessment method for offshore wind farms based on the hierarchical analysis method.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for disaster risk assessment of offshore wind farms based on analytic hierarchy process includes the following steps:

[0007] Step S1, establishing a three-level risk assessment indicator system including a target layer, a criterion layer, and an indicator layer;

[0008] Step S2, constructing a comprehensive judgment matrix between various indicators based on the hierarchical analysis method, and calculating the indicator weights;

[0009] Step S3, collecting historical typhoon data and wind farm data to construct an indicator historical data set;

[0010] Step S4, normalizing and scoring the indicators based on the indicator weights and the indicator historical data set, and calculating the risk score of the offshore wind farm being damaged by the typhoon;

[0011] Step S5, classify the risk scores to obtain a risk level classification table, compare the risk scores and the risk level classification table to obtain the disaster risk level of the offshore wind farm under the typhoon, and complete the risk assessment.

[0012] For a further solution of the present invention, in step S1, the process of establishing a three-level risk assessment indicator system is as follows:

[0013] Step S11, defining a target layer composed of disaster risk;

[0014] Step S12, defining a criterion layer consisting of danger, exposure, and vulnerability;

[0015] Step S13, defining an index layer consisting of an index H1 of the highest wind speed near the typhoon center, an index H2 of the lowest air pressure near the typhoon center, an index H3 of the typhoon duration, an index H4 of the maximum wave height in the study area, an index H5 of the maximum water increase in the study area, an index E1 of the number of wind turbines, an index E2 of the wind farm area, an index E3 of the water depth of the wind farm, an index E4 of the typhoon distance from the wind farm, an index V1 of the wind rotor diameter, an index V2 of the hub height, an index V3 of the maximum tower diameter, an index V4 of the steel strength, and an index V5 of the concrete strength;

[0016] In the constructed indicator layer, indicators H1 to H5 are indicators of risk, indicators E1 to E4 are indicators of exposure, and indicators V1 to V5 are indicators of vulnerability.

[0017] For a further solution of the present invention, in step S2, a comprehensive judgment matrix between the indicators is constructed based on the hierarchical analysis method, and the process of calculating the indicator weights is as follows:

[0018] Step S21: designing an indicator importance survey questionnaire, distributing and counting the questionnaire results, processing the collected questionnaire results, scoring them according to their importance, and counting the average scores of all indicators in the questionnaire results, sorting the indicators according to the average scores, comparing the indicators in different criterion layers, and calculating the difference between the scores of the high-ranked indicators and all the scores of the low-ranked indicators, dividing the difference into multiple intervals, each interval corresponding to a scale value;

[0019] Step S22: Based on the importance of the indicators, a comprehensive judgment matrix is ​​formed to perform consistency check;

[0020] use Indicators With indicators Compared with the importance of It indicates the indicator With indicators Compared with the importance of and They are reciprocals of each other, and the expression formula is as follows:

[0021] ;

[0022] ;

[0023] In the above formula, Indicator With indicators Scaled value after importance comparison;

[0024] The indicators in each criterion layer are compared pairwise to construct a comprehensive judgment matrix, as shown in the following formula:

[0025] ;

[0026] In the above formula, Indicates the importance of indicator 1 compared to indicator 1, Indicates index 1 and index Compared with the importance of Indicators The importance compared with indicator 1, Indicators With indicators Compared with the importance of express The corresponding importance scale, express The corresponding importance scale, express The corresponding importance scale, express The corresponding importance scale; , , and All represent block matrices;

[0027] In order to test the coordination of the importance of each indicator, the following formula is used to perform consistency test on the comprehensive judgment matrix:

[0028] ;

[0029] ;

[0030] In the above formula, is the consistency verification coefficient; is the consistency indicator; is the maximum eigenvalue of the comprehensive judgment matrix, is the order of comprehensive judgment matrix; represents the average consistency index;

[0031] Step S23: Calculate the index weight according to the following formula :

[0032] ;

[0033] In the above formula, For indicators The geometric mean relative to other indicator scales.

[0034] For a further solution of the present invention, in step S3, the process of collecting historical typhoon data and wind farm data and constructing an indicator historical data set is as follows:

[0035] Step S31, statistically analyzing the historical typhoon data information of the study area, extracting the historical data of the index H1 of the highest wind speed near the typhoon center, the index H2 of the lowest air pressure near the typhoon center, the index H3 of the typhoon duration, the index H4 of the maximum wave height in the study area, the index H5 of the maximum water increase in the study area, and the index E4 of the distance between the typhoon and the wind farm;

[0036] Step S32, collecting statistics on offshore wind farm data information, extracting historical data of the index E1 of the number of wind turbines, the index E2 of the wind farm area, the index E3 of the water depth of the wind farm, the index V1 of the wheel diameter, the index V2 of the hub height, the index V3 of the maximum tower diameter, the index V4 of the steel strength, and the index V5 of the concrete strength;

[0037] Step S33, constructing an indicator historical data set based on the historical data extracted from the historical typhoon data information counted in step S31 and the offshore wind farm data information counted in step S32.

[0038] For a further solution of the present invention, step S4, the process of normalizing and scoring the indicators based on the indicator weights and the indicator historical data set, and calculating the risk score of the offshore wind farm being damaged by the typhoon is as follows:

[0039] Step S41, determine the index attribute:

[0040] The positive indicators include: the index of the highest wind speed near the typhoon center H1, the index of the typhoon duration H3, the index of the maximum wave height in the study area H4, the index of the maximum water increase in the study area H5, the index of the number of wind turbines E1, the index of the wind farm area E2, the index of the water depth of the wind farm E3, the index of the wheel diameter V1 and the index of the hub height V2;

[0041] Negative indicators include: the lowest air pressure index near the typhoon center H2, the typhoon distance from the wind farm E4, the maximum tower diameter index V3, the steel strength index V4 and the concrete strength index V5;

[0042] Step S42, based on the historical indicator data set, normalize and score the indicator:

[0043] The positive and negative indicators are formed by normalizing and assigning scores to the maximum and minimum values ​​in the historical data set of the indicator and the original data of each indicator, and the following formula is used for calculation:

[0044] ;

[0045] In the above formula, It indicates the normalized score of the index in this typhoon. It indicates the original value of the indicator in this typhoon. Indicates the minimum value in the indicator's historical data set. Indicates the maximum value in the indicator's historical data set;

[0046] Step S43, using the following formula to calculate the risk score of the offshore wind farm being damaged by the typhoon:

[0047] ;

[0048] ;

[0049] ;

[0050] ;

[0051] in, , , They represent the hazard score, exposure score and vulnerability score respectively. Indicators of risk The weight of Indicators of risk The points assigned, Exposure index The weight of Exposure index The points assigned, Indicators of vulnerability The weight of Indicators of vulnerability The points assigned, Represents the risk score of offshore wind farms being damaged by typhoons.

[0052] For a further solution of the present invention, in step S5, the risk scores of offshore wind farms affected by typhoons are classified to obtain a risk level classification table, and the risk scores and the risk level classification table are compared to obtain the risk level of offshore wind farms affected by typhoons. The process is as follows:

[0053] Step S51, the hazard, exposure, and vulnerability of the criterion layer are divided into four levels of "low", "lower", "higher", and "high" according to the disadvantage from low to high;

[0054] Step S52, correspondingly classifying the risk scores from low to high into four levels: low risk, lower risk, higher risk, and high risk. The upper and lower limits of the risk scores of each level are obtained by multiplying the upper and lower limits of the scores of the corresponding hazard, exposure, and vulnerability levels to obtain a risk level classification table;

[0055] Step S53, compare the risk score obtained in step S4 with the risk level classification table obtained in step S52, obtain the risk level at this time, and complete the disaster risk assessment of the offshore wind farm under the typhoon.

[0056] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention solves the problems in the existing risk assessment research on natural disasters at sea, such as the wide range of research objects, extensive selection of indicators, and difficulty in applying to offshore wind farms, a specific disaster-prone body. The indicators are selected based on the dynamic response characteristics of the disaster-prone body, and a three-level risk assessment system is established to refine the indicator selection, making up for the deficiency that the existing research is difficult to apply to a single, specific disaster-prone body; for specific disaster-prone bodies, sufficient historical data are collected to ensure the rationality of the historical data set, and considering the nonlinearity of the calculation formula, an uneven risk level classification table is constructed, which can intuitively judge the degree of disaster risk of offshore wind farms under the action of typhoons. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic diagram of a process for evaluating the disaster risk of an offshore wind farm based on the analytic hierarchy process proposed by the present invention;

[0058] Figure 2 Schematic diagram of a three-level risk assessment system in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0060] Risk assessment of marine natural disasters has long been taken seriously. In recent years, relevant studies have also studied the risks of natural disasters from multiple perspectives and formulated a series of disaster reduction plans. In existing studies, the concept that "risk consists of hazard, exposure, and vulnerability" has been recognized, and based on this, risk assessment and loss assessment of marine natural disasters including typhoons and storm surges have been carried out. At present, there are many tools for constructing risk assessment methods. The selection of indicators is constrained by various principles; the determination of indicator weights includes "hierarchical analysis method", "grey correlation method", "entropy method", etc.; the scoring of indicators also includes "Min-Max standardization" and "Z-Score standardization". Due to the lack of relevant disaster data, most studies use more subjective methods.

[0061] Combination Figure 1 and Figure 2 The method for evaluating the risk of offshore wind farms under typhoon based on the analytic hierarchy process of the present invention is further described in detail. The method mainly includes the following steps:

[0062] Step S1, establishing a three-level risk assessment indicator system including a target layer, a criterion layer and an indicator layer.

[0063] Step S2, based on the hierarchical analysis method, according to the relative importance of the indicators, construct a comprehensive judgment matrix between the indicators and calculate the indicator weights.

[0064] Step S3, collect historical typhoon data and wind farm data, and construct an indicator historical data set.

[0065] Step S4, normalizing and scoring the indicators based on the indicator weights and the indicator historical data set, and calculating the risk score of the offshore wind farm being damaged by the typhoon.

[0066] Step S5, classify the risk scores to obtain a risk level classification table, compare the risk scores and the risk level classification table to obtain the disaster risk level of the offshore wind farm under the typhoon, and complete the risk assessment.

[0067] For the above main steps, each of the above steps is specifically described in detail in this embodiment.

[0068] Based on the risk concept model proposed by the United Nations in 2010, risk is equal to the product of hazard, exposure and vulnerability. Hazard fundamentally determines whether disaster risk exists and also determines the size of the risk; exposure refers to the intersection of the impact range of the disaster and the distribution of the disaster-prone body in space. Only when there is an intersection can a disaster factor constitute a risk; vulnerability is the inherent attribute of the disaster-prone body, which refers to the disaster-prone body's own response and resistance ability when facing natural disasters. Based on the above concepts, the target layer is determined as risk, and the criterion layer is determined as hazard, exposure and vulnerability.

[0069] In step S1, the process of establishing a three-level risk assessment indicator system including a target layer, a criterion layer, and an indicator layer is as follows:

[0070] Step S11, defining a target layer composed of disaster risk;

[0071] Step S12, defining a criterion layer consisting of danger, exposure, and vulnerability;

[0072] Step S13, defining an index layer consisting of an index H1 of the highest wind speed near the typhoon center, an index H2 of the lowest air pressure near the typhoon center, an index H3 of the typhoon duration, an index H4 of the maximum wave height in the study area, an index H5 of the maximum water increase in the study area, an index E1 of the number of wind turbines, an index E2 of the wind farm area, an index E3 of the water depth of the wind farm, an index E4 of the typhoon distance from the wind farm, an index V1 of the wind rotor diameter, an index V2 of the hub height, an index V3 of the maximum tower diameter, an index V4 of the steel strength, and an index V5 of the concrete strength;

[0073] In the constructed indicator layer, indicators H1 to H5 are indicators of risk, indicators E1 to E4 are indicators of exposure, and indicators V1 to V5 are indicators of vulnerability.

[0074] As a meteorological disaster, the maximum wind speed near the center of a typhoon is the most intuitive indicator of its strength and is also the most important factor in related meteorological and hydrological forecasts. The pressure difference is the main factor in wind formation. The lower the pressure at the center of a typhoon, the greater the pressure gradient within the typhoon range and the corresponding wind force. Therefore, the lowest pressure near the center of a typhoon can well reflect the intensity of a typhoon. The longer a typhoon lasts, the longer the research object will be affected by it and the greater the possibility of a disaster. The typhoon waves caused by a typhoon directly act on the pile foundation structure of the wind turbine. The higher the value, the greater the harm to the research object. The storm surge caused by a typhoon will cause the water level at the research site to rise, making it possible to exceed the design water level.

[0075] For the number of wind turbines and the area of ​​wind farms, the larger their values ​​are, the greater the possibility of exposure to typhoons will be. Therefore, they are included in the index system to describe the impact of factors of the wind farm itself; the water depth can indirectly reflect the distance from the shore of the wind farm to a certain extent.

[0076] The ease with which offshore wind turbine structures are threatened by typhoon disasters and suffer losses is expressed by vulnerability. The higher the vulnerability, the greater the degree of loss caused by the disaster. The main parameters of the wind turbine structure are selected: rotor diameter, hub height, maximum tower diameter, steel strength and concrete strength to qualitatively describe its vulnerability.

[0077] The selection of some indicators should be modified according to the characteristics of specific offshore wind farms.

[0078] In step S2, a comprehensive judgment matrix between indicators is constructed based on the hierarchical analysis method, and the detailed process of calculating the indicator weights is as follows:

[0079] Step S21: Design an indicator importance survey questionnaire, distribute and count the questionnaire results (the questionnaire can be distributed to experts in related fields), process the collected questionnaire results, score them according to their importance, and count the average scores of all indicators in the questionnaire results, sort the indicators according to the average scores, compare the indicators in different criterion layers, calculate the difference between the scores of high-ranked indicators and all the scores of low-ranked indicators, and divide the differences into multiple intervals, each of which corresponds to a scale value. The questionnaire can be set up in the form of Table 1.

[0080] Table 1 Risk assessment questionnaire

[0081]

[0082] For example, the collected questionnaire results are processed, with particularly important being scored as 5, important being scored as 4, generally important being scored as 3, slightly important being scored as 2, and unimportant being scored as 1. The average scores of all indicators in all questionnaires are calculated, and the indicators are sorted according to the scores, with indicators in different criterion layers being compared separately. Based on the sorting results, the difference between the scores of the high-ranking indicators and all the scores of the low-ranking indicators is calculated separately. The difference is divided into 9 intervals, each of which corresponds to a scale. The scale comparison table is shown in Table 2. The difference between 0 and 0.25 is recorded as scale 1, that is, the two are equally important; the difference between 0.75 and 1.25 is recorded as scale 3, that is, the former is slightly more important than the latter; the difference between 1.75 and 2.25 is recorded as scale 5, that is, the former is obviously more important than the latter; the difference between 2.75 and 3.25 is recorded as scale 7, that is, the former is strongly important than the latter; the difference between 3.75 and 4 is recorded as scale 9, that is, the former is obviously more important than the latter; the middle section corresponds to scales 2 to 8. The corresponding meaning of each scale is shown in Table 3.

[0083] Table 2 Scale comparison table

[0084]

[0085] Table 3 Scale corresponding meaning table

[0086]

[0087] Step S22: Based on the importance of the indicators, a comprehensive judgment matrix is ​​formed and consistency test is performed. In order to perform weight analysis, the relative importance of each indicator needs to be quantitatively expressed. Indicators With indicators Compared with the importance of It indicates the indicator With indicators Compared with the importance of and They are reciprocals of each other, and the specific expression formula is shown as follows:

[0088] ;

[0089] ;

[0090] In the above formula, Indicators With indicators Scaled value after importance comparison;

[0091] The indicators in each criterion layer are compared pairwise to construct a comprehensive judgment matrix, as shown in the following formula:

[0092] ;

[0093] In the above formula, Indicates the importance of indicator 1 compared to indicator 1, Indicates index 1 and index Compared with the importance of Indicators The importance compared with indicator 1, Indicators With indicators Compared with the importance of express The corresponding importance scale, express The corresponding importance scale, express The corresponding importance scale, express The corresponding importance scale; , , and All represent block matrices;

[0094] In order to test the coordination of the importance of each indicator, the following formula is used to perform consistency test on the comprehensive judgment matrix:

[0095] ;

[0096] ;

[0097] In the above formula, is the consistency verification coefficient; is the consistency index, and its value is shown in Table 4; is the maximum eigenvalue of the comprehensive judgment matrix, In order to comprehensively judge the matrix order, in the subsequent project implementation process, based on the amount of historical data that can be obtained and the actual historical data structure, the rationality and operability of the above methods will be considered, and the appropriate method will be selected to assign indicator weights; Represents the average (or random) consistency index.

[0098] Table 4 Value table

[0099]

[0100] When the verification coefficient When <0.10, it is considered that the consistency of the comprehensive judgment matrix meets the requirements, otherwise the importance scale between indicators should be revised or the original indicator system should be corrected.

[0101] Step S23: Calculate the index weights in sequence according to the following formula

[0102] ;

[0103] In the above formula, For indicators The geometric mean relative to other indicator scales.

[0104] In step S3, the process of collecting historical typhoon data and wind farm data and constructing an indicator historical data set is as follows:

[0105] Step S31, statistics the historical typhoon data information that has passed through the 250km range around the study area since 1979, and extract the historical data of the index H1 of the maximum wind speed near the typhoon center, the index H2 of the minimum air pressure near the typhoon center, the index H3 of the typhoon duration, the index H4 of the maximum wave height in the study area, the index H5 of the maximum water increase in the study area, and the index E4 of the typhoon distance from the wind farm.

[0106] Step S32, statistics of offshore wind farm data information, extracting historical data of index E1 of wind turbine quantity, index E2 of wind farm area, index E3 of wind farm water depth, index V1 of wheel diameter, index V2 of hub height, index V3 of maximum tower diameter, index V4 of steel strength, and index V5 of concrete strength.

[0107] Step S33, constructing an indicator historical data set based on the historical data extracted from the historical typhoon data information counted in step S31 and the offshore wind farm data information counted in step S32.

[0108] Step S4, normalizing and scoring the indicators based on the indicator weights and the indicator historical data set, and calculating the risk score of the offshore wind farm under the typhoon are as follows:

[0109] Step S41, determine the index attribute:

[0110] The positive indicators include: the index of the highest wind speed near the typhoon center H1, the index of the typhoon duration H3, the index of the maximum wave height in the study area H4, the index of the maximum water increase in the study area H5, the index of the number of wind turbines E1, the index of the wind farm area E2, the index of the water depth of the wind farm E3, the index of the wheel diameter V1 and the index of the hub height V2;

[0111] Negative indicators include: the lowest air pressure index near the typhoon center H2, the typhoon distance from the wind farm E4, the maximum tower diameter index V3, the steel strength index V4 and the concrete strength index V5;

[0112] Step S42, based on the historical indicator data set, normalize and score the indicator:

[0113] The positive and negative indicators are formed by normalizing and assigning scores to the maximum and minimum values ​​in the historical data set of the indicator and the original data of each indicator, and the following formula is used for calculation:

[0114] ;

[0115] In the above formula, It represents the normalized score of the index under this typhoon. The above calculation formula represents the calculation of positive index. The larger the value, the more it can reflect the index of the nature of the criterion layer, such as wind speed for danger. The larger the value, the more it can reflect the degree of typhoon danger. The following calculation formula represents the calculation of negative index. Negative index refers to the index that the smaller the value, the more it can reflect the nature of the criterion layer, such as the lowest air pressure for danger. It indicates the original value of the indicator in this typhoon. Indicates the minimum value in the indicator's historical data set. Indicates the maximum value in the indicator's historical data set.

[0116] Step S43, according to the risk concept model released by the United Nations in 2010, the hazard (H) score, the exposure (E) score, and the vulnerability (V) score are multiplied together to calculate the risk score of the offshore wind farm under the typhoon using the following formula:

[0117] ;

[0118] ;

[0119] ;

[0120] ;

[0121] in, , , They represent the hazard score, exposure score and vulnerability score respectively. Indicators of risk The weight of Indicators of risk The points assigned, Exposure index The weight of Exposure index The points assigned, Indicators of vulnerability The weight of Indicators of vulnerability The points assigned, Represents the risk score of offshore wind farms being damaged by typhoons.

[0122] In step S5, the risk scores of offshore wind farms affected by typhoons are classified to obtain a risk level classification table. The risk scores and the risk level classification table are compared to obtain the risk level of offshore wind farms affected by typhoons as follows:

[0123] Step S51, the hazard, exposure and vulnerability of the criterion layer are equally spaced and divided into four levels of "low", "lower", "higher" and "high" from low to high according to the disadvantage at an interval of 0.25; to describe the degree of disadvantage, see the criterion layer classification table in Table 5.

[0124] Table 5 Criteria level classification table

[0125]

[0126] In step S52, the risk scores are divided into four levels from low to high: low risk, relatively low risk, relatively high risk and high risk. The upper and lower limits of the risk score of each level are obtained by multiplying the upper and lower limits of the scores of the corresponding hazard, exposure and vulnerability levels to obtain a risk level classification table, 0-0.016 is "low risk", 0.016-0.125 is "relatively low risk", 0.125-0.422 is "relatively high risk", and 0.422-1 is "high risk", see the risk level classification table in Table 6.

[0127] Table 6 Risk level classification table

[0128]

[0129] Note: 0.016=0.25 3 , 0.125=0.5 3 , 0.422=0.75 3 .

[0130] Step S53, compare the risk score obtained in step S4 with the risk level classification table obtained in step S52, obtain the risk level at this time, and complete the disaster risk assessment of the offshore wind farm under the typhoon.

[0131] In another specific embodiment: The present invention is described in detail below by taking the disaster risk assessment of Putuo No. 6 offshore wind farm during Typhoon No. 2413 "Bebejia" as an example.

[0132] A1: Combination Figure 2 Collect meteorological and hydrological data during Typhoon No. 2413 "Bebejia", and organize information on Putuo No. 6 offshore wind farm.

[0133] During the typhoon "Bebejia", the maximum wind speed was 45m / s, the minimum air pressure was 95500Pa, the duration was 196h, the maximum wave height in the study area was 2.39m, the maximum water increase in the study area was 44.79cm, and the closest distance between the typhoon and the wind farm was 190.89km. The number of wind turbines in Putuo No. 6 offshore wind farm is 63, and the wind farm area is 35km 2 , water depth 16m, wind rotor diameter 135m, hub height 90m, maximum tower diameter 5.06m, steel strength (design strength) 335MPa, concrete strength (design strength) 21.1MPa.

[0134] A2: Questionnaires were distributed to relevant experts to construct a comprehensive judgment matrix, and consistency was tested based on the formula. The risk, exposure, and vulnerability were calculated. They are 0.0249, 0.0177, and 0.0173 respectively, all less than 0.1, which meets the requirements. The indicator weights are calculated through steps S22 and S23, as shown in the indicator weight table in Table 7.

[0135] Table 7 Index weight table

[0136]

[0137] A3: The historical typhoon data passing through Putuo No. 6 offshore wind farm since 1979 and the information of major wind farms are collected to obtain the range table of index values. See Table 8 for the range table of each index value.

[0138] Table 8 Range of values ​​for each indicator

[0139]

[0140] A4: According to step S4, the danger, exposure and vulnerability of Putuo No. 6 offshore wind farm during Typhoon "Bebejia" are calculated, which are 0.3441, 0.3254 and 06713 respectively. The three items are multiplied to obtain the disaster risk score of Putuo No. 6 offshore wind farm during Typhoon "Bebejia": 0.0752. The results are shown in the Bebejia risk analysis table in Table 9.

[0141] Table 9 Bebejia risk analysis table

[0142]

[0143] A5: Compared with Table 6, the disaster risk level of Putuo No. 6 offshore wind farm during Typhoon "Bebejia" is "low risk". Combined with the actual situation, Typhoon "Bebejia" has little actual impact on Putuo No. 6 offshore wind farm, and the results are quite consistent.

[0144] The above description of the method for evaluating the disaster risk of offshore wind farms under typhoons based on the analytic hierarchy process in the present invention refines the indicator selection by referring to the three-level risk evaluation system established by the risk concept model released by the United Nations in 2010, which makes up for the deficiency that the existing research is difficult to apply to a single, specific disaster-prone body; for offshore wind farms, sufficient historical data are collected to ensure the rationality of the historical data set; considering the nonlinearity of the calculation formula, an uneven risk level classification table is constructed, which can intuitively judge the degree of disaster risk of offshore wind farms under typhoons.

[0145] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for disaster risk assessment of offshore wind farms based on analytic hierarchy process, characterized in that: The following steps are involved: Step S1, establishing a three-level risk assessment indicator system including a target layer, a criterion layer, and an indicator layer; Step S2, constructing a comprehensive judgment matrix between various indicators based on the hierarchical analysis method, and calculating the indicator weights; Step S3, collecting historical typhoon data and wind farm data to construct an indicator historical data set; Step S4, normalizing and scoring the indicators based on the indicator weights and the indicator historical data set, and calculating the risk score of the offshore wind farm being damaged by the typhoon; Step S5, classify the risk scores to obtain a risk level classification table, compare the risk scores and the risk level classification table to obtain the disaster risk level of the offshore wind farm under the typhoon, and complete the risk assessment.

2. The method for disaster risk assessment of offshore wind farms based on analytic hierarchy process according to claim 1 is characterized in that: In step S1, the process of establishing the three-level risk assessment indicator system is as follows: Step S11, defining a target layer composed of disaster risk; Step S12, defining a criterion layer consisting of danger, exposure, and vulnerability; Step S13, defining an index layer consisting of an index H1 of the highest wind speed near the typhoon center, an index H2 of the lowest air pressure near the typhoon center, an index H3 of the typhoon duration, an index H4 of the maximum wave height in the study area, an index H5 of the maximum water increase in the study area, an index E1 of the number of wind turbines, an index E2 of the wind farm area, an index E3 of the water depth of the wind farm, an index E4 of the typhoon distance from the wind farm, an index V1 of the wind rotor diameter, an index V2 of the hub height, an index V3 of the maximum tower diameter, an index V4 of the steel strength, and an index V5 of the concrete strength; In the constructed indicator layer, indicators H1 to H5 are indicators of risk, indicators E1 to E4 are indicators of exposure, and indicators V1 to V5 are indicators of vulnerability.

3. The method for disaster risk assessment of offshore wind farms based on analytic hierarchy process according to claim 1 is characterized in that: In step S2, a comprehensive judgment matrix between indicators is constructed based on the hierarchical analysis method, and the process of calculating the indicator weights is as follows: Step S21, designing an indicator importance survey questionnaire, distributing and counting the questionnaire results, processing the collected questionnaire results, scoring them according to their importance, and counting the average scores of all indicators in the questionnaire results, sorting the indicators according to the average scores, comparing the indicators in different criterion layers, calculating the difference between the scores of the high-ranked indicators and all the scores of the low-ranked indicators, and dividing the difference into multiple intervals, each of which corresponds to a scale value; Step S22, based on the importance of the indicators, a comprehensive judgment matrix is ​​formed to perform consistency check; Indicator With indicators Compared with the importance of It indicates the indicator With indicators Compared with the importance of and They are reciprocals of each other, and the expression formula is as follows: ； ； In the above formula, Indicator With indicators Scaled value after importance comparison; The indicators in each criterion layer are compared pairwise to construct a comprehensive judgment matrix, as shown in the following formula: ； In the above formula, Indicates the importance of indicator 1 compared to indicator 1, Indicates index 1 and index Compared with the importance of Indicator The importance compared with indicator 1, Indicator With indicators Compared with the importance of express The corresponding importance scale, express The corresponding importance scale, express The corresponding importance scale, express The corresponding importance scale; , , and All represent block matrices; In order to test the coordination of the importance of each indicator, the following formula is used to perform consistency test on the comprehensive judgment matrix: ； ； In the above formula, is the consistency verification coefficient; is the consistency indicator, is the maximum eigenvalue of the comprehensive judgment matrix, is the order of comprehensive judgment matrix; represents the average consistency index; Step S23: Calculate the index weight according to the following formula : ； In the above formula, For indicators The geometric mean relative to other indicator scales.

4. The method for disaster risk assessment of offshore wind farms based on analytic hierarchy process according to claim 1 is characterized in that: In step S3, the process of collecting historical typhoon data and wind farm data and constructing an indicator historical data set is as follows: Step S31, statistically analyzing the historical typhoon data information of the study area, extracting the historical data of the index H1 of the highest wind speed near the typhoon center, the index H2 of the lowest air pressure near the typhoon center, the index H3 of the typhoon duration, the index H4 of the maximum wave height in the study area, the index H5 of the maximum water increase in the study area, and the index E4 of the distance between the typhoon and the wind farm; Step S32, collecting statistics on offshore wind farm data information, extracting historical data of the index E1 of the number of wind turbines, the index E2 of the wind farm area, the index E3 of the water depth of the wind farm, the index V1 of the wheel diameter, the index V2 of the hub height, the index V3 of the maximum tower diameter, the index V4 of the steel strength, and the index V5 of the concrete strength; Step S33, constructing an indicator historical data set based on the historical data extracted from the historical typhoon data information counted in step S31 and the offshore wind farm data information counted in step S32.

5. The method for disaster risk assessment of offshore wind farms based on analytic hierarchy process according to claim 1 is characterized in that: Step S4, normalizing and scoring the indicators based on the indicator weights and the indicator historical data set, and calculating the risk score of the offshore wind farm under the typhoon are as follows: Step S41, determine the index attribute: The positive indicators include: the index of the highest wind speed near the typhoon center H1, the index of the typhoon duration H3, the index of the maximum wave height in the study area H4, the index of the maximum water increase in the study area H5, the index of the number of wind turbines E1, the index of the wind farm area E2, the index of the water depth of the wind farm E3, the index of the wheel diameter V1 and the index of the hub height V2; Negative indicators include: the lowest air pressure index near the typhoon center H2, the typhoon distance from the wind farm E4, the maximum tower diameter index V3, the steel strength index V4 and the concrete strength index V5; Step S42, based on the historical indicator data set, normalize and score the indicator: The positive and negative indicators are formed by normalizing and assigning scores to the maximum and minimum values ​​in the historical data set of the indicator and the original data of each indicator, and the following formula is used for calculation: ； In the above formula, It indicates the normalized score of the index in this typhoon. It indicates the original value of the indicator in this typhoon. Indicates the minimum value in the indicator's historical data set. Indicates the maximum value in the indicator's historical data set; Step S43, using the following formula to calculate the risk score of the offshore wind farm being damaged by the typhoon: ； ； ； ； in, , , They represent the hazard score, exposure score and vulnerability score respectively. Indicators of risk The weight of Indicators of risk The points assigned, Exposure index The weight of Exposure index The points assigned, Indicators of vulnerability The weight of Indicators of vulnerability The points assigned, Represents the risk score of offshore wind farms being damaged by typhoons.

6. The method for disaster risk assessment of offshore wind farms based on analytic hierarchy process according to claim 1 is characterized in that: In step S5, the risk scores of offshore wind farms affected by typhoons are classified to obtain a risk level classification table. The risk scores and the risk level classification table are compared to obtain the risk level of offshore wind farms affected by typhoons as follows: Step S51, the hazard, exposure, and vulnerability of the criterion layer are separated by equal intervals, and divided into four levels of "low", "lower", "higher", and "high" according to the disadvantage from low to high; Step S52, correspondingly classifying the risk scores from low to high into four levels: "low risk", "relatively low risk", "relatively high risk" and "high risk", and the upper and lower limits of the risk scores of each level are obtained by multiplying the upper and lower limits of the scores of the corresponding hazard, exposure and vulnerability levels to obtain a risk level classification table; Step S53, compare the risk score obtained in step S4 with the risk level classification table obtained in step S52, obtain the risk level at this time, and complete the disaster risk assessment of the offshore wind farm under the typhoon.

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