CN118008729A - Beidou-based offshore wind turbine structure state monitoring method and system - Google Patents
Beidou-based offshore wind turbine structure state monitoring method and system Download PDFInfo
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Abstract
The invention relates to the technical field of intelligent monitoring of structural states of offshore fans, and discloses a Beidou-based method and a Beidou-based system for monitoring structural states of offshore fans, wherein the method comprises the following steps: collecting monitoring data of the offshore wind turbine structure based on the Beidou technology, and extracting evaluation indexes based on the monitoring data; determining the weight of each evaluation index by using an analytic hierarchy process, and adjusting according to the Vague value to obtain an adjusted weight; and calculating the comprehensive health index of the offshore wind turbine structure according to the Vague value of each evaluation index and the adjusted weight, and evaluating the health grade of the offshore wind turbine structure based on the health index. The invention not only can realize the high-efficiency and real-time monitoring of the structural state of the offshore wind turbine, but also can introduce a Vague value theory to fully consider the uncertainty and the ambiguity of an expert in the process of setting the evaluation index weight, improve the scientificity and the accuracy of the evaluation result and can evaluate the structural health state of the offshore wind turbine more accurately.
Description
Technical Field
The invention relates to the technical field of intelligent monitoring of structural states of offshore wind turbines, in particular to a Beidou-based method and a Beidou-based system for monitoring structural states of offshore wind turbines.
Background
Offshore wind power, an emerging clean renewable energy source, has become an important direction for global energy structure adjustment. Offshore wind power has a greater development potential than land wind power because offshore wind power is more stable and wind speed is higher, and more power can be generated. However, the offshore wind turbine is in a complex and changeable environment, so that not only is the test of high wind and high waves faced, but also the erosion of salt fog, humidity and other climatic conditions are required, and the factors can cause the structure of the wind turbine to be gradually changed and even cause faults. Under the prior art condition, the state monitoring of the offshore wind turbine structure is particularly critical. Through the health status of real-time supervision fan structure, can in time discover potential safety hazard, ensure the steady operation of fan, improve the whole security and the reliability of offshore wind farm. At present, a plurality of monitoring technologies such as vibration monitoring, acoustic emission detection, infrared thermal imaging and the like are emerging, but certain limitations exist.
The Beidou satellite navigation system is taken as a global satellite navigation system independently researched and developed in China, has the unique advantages of high-precision positioning, high-precision time service and the like, and provides a new technical approach for monitoring the structural state of the offshore wind turbine. The Beidou system is utilized to acquire accurate position and time information of the fan in real time, so that remote monitoring of the fan structure is realized. In addition, the Beidou system has strong anti-interference capability and good reliability, and can normally operate in a severe marine environment. Along with the rapid development of the offshore wind power industry in China, the requirements on the fan structure state monitoring technology are also higher and higher. The offshore wind turbine structure state monitoring method based on the Beidou system is beneficial to improving the operation and maintenance efficiency of the offshore wind farm, reducing the operation and maintenance cost, ensuring the safe and stable operation of the wind turbine and providing solid technical support for sustainable development of the offshore wind power industry in China. Therefore, the invention provides a method for monitoring the structural state of the offshore wind turbine based on the Beidou system.
Disclosure of Invention
Aiming at the problems in the related art, the invention provides a method and a system for monitoring the structural state of an offshore wind turbine based on Beidou, which are used for overcoming the technical problems existing in the prior related art.
For this purpose, the invention adopts the following specific technical scheme:
According to one aspect of the invention, there is provided a Beidou-based offshore wind turbine structural state monitoring method, which comprises the following steps:
s1, acquiring monitoring data of an offshore wind turbine structure based on a Beidou technology, and extracting an evaluation index based on the monitoring data;
S2, determining the weight of each evaluation index by using an analytic hierarchy process, and adjusting according to the Vague value to obtain an adjusted weight;
And S3, calculating the comprehensive health index of the offshore wind turbine structure according to the Vague value of each evaluation index and the adjusted weight, and evaluating the health grade of the offshore wind turbine structure based on the health index.
Preferably, the collecting the monitoring data of the offshore wind turbine structure based on the Beidou technology, extracting the evaluation index based on the monitoring data, and preprocessing the evaluation index comprises the following steps:
s11, collecting monitoring data of the offshore wind turbine structure through detection equipment pre-installed on the offshore wind turbine structure, and cleaning, denoising and normalizing the monitoring data;
And S12, selecting characteristics related to the structural health state of the offshore wind turbine from the processed monitoring data as evaluation indexes, and transmitting the monitoring data and the evaluation index data to a remote monitoring terminal by using a Beidou communication technology.
Preferably, the determining the weight of each evaluation index by using the analytic hierarchy process, and adjusting according to the Vague value, and obtaining the adjusted weight includes the following steps:
S21, constructing a hierarchical structure model, wherein the hierarchical structure model takes the structural state monitoring of an offshore wind turbine as a target layer, takes the structural integrity and the running performance as an evaluation criterion layer, and takes the vibration frequency, the strain root mean square, the surface crack length, the blade pitch angle and the yaw angle as an evaluation index layer;
s22, comparing factors in the same layer in pairs to construct a judgment matrix; calculating the maximum eigenvalue and corresponding eigenvector of the judgment matrix by using an eigenvalue method to obtain the initial weight of each evaluation index;
s23, converting a language fuzzy variable of an evaluation index given by an expert into a Vague value based on a true membership function and a false membership function;
S24, calculating the score of each evaluation index by using a calculation function, determining the importance level of the evaluation index according to the score, and determining the weight of each evaluation index by normalizing the score;
And S25, adjusting the weight of each evaluation index by using the true membership of the Vague value to obtain the adjusted weight.
Preferably, the factors in the same layer are compared pairwise to construct a judgment matrix; calculating the maximum eigenvalue and the corresponding eigenvector of the judgment matrix by using an eigenvalue method to obtain the initial weight of each evaluation index, wherein the initial weight comprises the following steps:
S221, comparing factors in the same layer in pairs, and quantizing the comparison result by using a nine-level scale method to construct a judgment matrix;
s222, calculating the maximum eigenvalue and the corresponding eigenvector of the judgment matrix by utilizing the eigenvalue, and carrying out consistency test;
s223, carrying out normalization processing on the feature vector, wherein each element in the feature vector after normalization processing is the initial weight of the evaluation index.
Preferably, the Vague value is expressed as:
the calculation formula of the true membership function is as follows:
the calculation formula of the false membership function is as follows:
Wherein t i represents a true membership degree, i represents a numerical value of an evaluation index, a and b represent a minimum value and a maximum value in an evaluation index language fuzzy variable interval, f i represents a false membership degree, and V represents a Vague value.
Preferably, the calculation formula of the calculation function is:
in the formula, S i represents the score of the evaluation index.
Preferably, the adjusting the weight of each evaluation index by using the true membership of the Vague value, and obtaining the adjusted weight includes the following steps:
The true membership of the Vague value of each evaluation index is obtained, the true membership of the Vague value is combined with the weight of the corresponding evaluation index, and normalization processing is carried out to obtain an adjusted weight, wherein the adjusted weight=the true membership of the Vague value of the weight of the evaluation index.
Preferably, the calculating the comprehensive health index of the offshore wind turbine structure according to the Vague value of each evaluation index and the adjusted weight, and the evaluating the health grade of the offshore wind turbine structure based on the health index comprises the following steps:
s31, calculating the comprehensive health index of the offshore wind turbine structure according to the Vague value of each evaluation index and the adjusted weight by using a weighted summation method;
S32, evaluating the health grade of the offshore wind turbine structure according to the comprehensive health index of the offshore wind turbine structure and a preset health grade classification table.
Preferably, the health level of the offshore wind turbine structure comprises excellent, good, general, warning and dangerous, and each health level corresponds to one comprehensive health index threshold interval respectively.
According to another aspect of the invention, there is provided a Beidou-based offshore wind turbine structure state monitoring system, which comprises an evaluation index extraction module, an index weight determination module and a health grade evaluation module, wherein the evaluation index extraction module, the index weight determination module and the health grade evaluation module are sequentially connected;
the evaluation index extraction module is used for acquiring monitoring data of the offshore wind turbine structure based on the Beidou technology and extracting evaluation indexes based on the monitoring data;
The index weight determining module is used for determining the weight of each evaluation index by using a analytic hierarchy process, and adjusting according to the Vague value to obtain the adjusted weight;
the health grade evaluation module is used for calculating the comprehensive health index of the offshore wind turbine structure according to the Vague value of each evaluation index and the adjusted weight, and evaluating the health grade of the offshore wind turbine structure based on the health index.
The beneficial effects of the invention are as follows:
The invention not only can overcome the limitation of the traditional monitoring method through integrating the Beidou communication technology, and realize the high-efficiency and real-time monitoring of the structural state of the offshore wind turbine, but also can determine the weight of the evaluation index through a hierarchical analysis method, and introduce a Vague value theory to fully consider the uncertainty and the ambiguity of an expert in the process of setting the weight of the evaluation index, thereby improving the scientificity and the accuracy of the evaluation result, simultaneously adjusting the weight of the evaluation index according to the true membership of the Vague value, dynamically reflecting the change of the structural state of the offshore wind turbine, improving the adaptability and the flexibility of an evaluation model, and more accurately evaluating the structural health state of the offshore wind turbine.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for monitoring the structural state of an offshore wind turbine based on Beidou according to an embodiment of the invention.
Detailed Description
For the purpose of further illustrating the various embodiments, the present invention provides the accompanying drawings, which are a part of the disclosure of the present invention, and which are mainly used to illustrate the embodiments and, together with the description, serve to explain the principles of the embodiments, and with reference to these descriptions, one skilled in the art will recognize other possible implementations and advantages of the present invention, wherein elements are not drawn to scale, and like reference numerals are generally used to designate like elements.
According to the embodiment of the invention, a method and a system for monitoring the structural state of an offshore wind turbine based on Beidou are provided.
The invention will be further described with reference to the accompanying drawings and the specific embodiments, as shown in fig. 1, according to one embodiment of the invention, there is provided a method for monitoring the structural state of an offshore wind turbine based on Beidou, the method for monitoring the structural state of the offshore wind turbine based on Beidou comprises the following steps:
S1, acquiring monitoring data of an offshore wind turbine structure based on a Beidou technology, and extracting an evaluation index based on the monitoring data; specifically, the monitoring data comprise accurate position information (including longitude, latitude, altitude and the like) of the offshore wind turbine, operation parameters (including parameters of rotating speed, generating capacity, temperature, humidity and the like) and structural health related data (including vibration data, strain data, image data and the like); the evaluation indexes include vibration frequency (characteristic frequency obtained by analyzing vibration data for evaluating the health condition of a structure), strain root mean square value (index obtained by processing strain sensor data for evaluating the stress condition of a structure), surface crack length (crack length obtained by processing and analyzing image data for evaluating the fatigue condition of a structure), blade pitch angle value and yaw angle value (blade angle change obtained by analyzing operation data of a fan for evaluating the operation state of the fan and the integrity of the blade).
The method comprises the following steps of collecting monitoring data of an offshore wind turbine structure based on a Beidou technology, extracting an evaluation index based on the monitoring data, and preprocessing the evaluation index:
s11, collecting monitoring data of the offshore wind turbine structure through detection equipment pre-installed on the offshore wind turbine structure, and cleaning, denoising and normalizing the monitoring data;
Specifically, firstly, the high-precision positioning and time synchronization functions of the Beidou system are utilized to collect the position data of the offshore wind turbine in real time, and through a sensor arranged on the offshore wind turbine, the operation data such as vibration characteristic frequency, strain root mean square value, surface crack length, blade pitch angle value, yaw angle value and the like are synchronously collected. And then synchronizing the Beidou position data with the operation data acquired by the sensor, ensuring the time consistency of the data, and integrating the data from different sources to form a complete fan operation state data set. Then identifying and processing abnormal values, missing values and repeated records in the data; correcting the abnormal value by adopting interpolation, smoothing and other technologies; selecting proper interpolation methods for the missing values according to the characteristics of the data, such as linear interpolation, nearest neighbor interpolation and the like; finally, filtering technology such as low-pass filtering, wavelet denoising and the like is adopted to remove noise in the data, retain effective information in the data and improve the data quality; and the data is normalized, indexes with different dimensions and magnitudes are mapped to the [0,1] interval, so that comparison and analysis are facilitated, and methods such as maximum and minimum standardization, Z-score standardization and the like can be adopted.
And S12, selecting characteristics related to the structural health state of the offshore wind turbine from the processed monitoring data as evaluation indexes, and transmitting the monitoring data and the evaluation index data to a remote monitoring terminal by using a Beidou communication technology.
Specifically, selecting characteristics related to the health state of the offshore wind turbine structure from the processed monitoring data as an evaluation index is a key step in the offshore wind turbine structure health monitoring system, and the following is a detailed description of the process and application of the process combined with Beidou communication technology:
and (3) selecting an evaluation index:
1) Feature extraction: and selecting key characteristics capable of reflecting the health state of the offshore wind turbine structure from the preprocessed data as an evaluation index. These features include:
Vibration frequency: reflecting the dynamic response of the blower structure.
Root mean square of strain: the overall level of structural strain is measured and correlated to structural fatigue.
Surface crack length: the development of cracks directly affects the structural integrity.
Blade pitch angle and yaw angle: the dynamic position change of the blades influences the wind capturing efficiency and the structural load of the fan.
Application of Beidou communication technology:
1) And (3) data transmission: and transmitting the monitoring data and the evaluation index data from the offshore wind turbine to the remote monitoring terminal by using the Beidou communication technology. The Beidou system in the embodiment has the following advantages:
high reliability: the Beidou system has a powerful satellite network, and can realize all-weather and global coverage data transmission.
High-precision positioning: positioning accuracy of the meter level and even the centimeter level is provided, and accurate monitoring of the position and the state of the fan is facilitated.
Short message communication: the Beidou system supports the short message communication function, and can realize effective data transmission even in areas with insufficient signal coverage, such as remote areas or offshore areas.
And (3) real-time monitoring: after the remote monitoring terminal receives the data, the structural health state of the offshore wind turbine can be monitored in real time, and a response can be made in time.
3) Technical advantage
Real-time performance: the Beidou communication technology ensures real-time transmission of monitoring data and is beneficial to quick response to potential structural problems.
Accuracy: the selected evaluation index is closely related to the structural health state, and the actual condition of the fan can be accurately reflected.
Remote monitoring: the remote monitoring terminal can be located in a control center on land, so that management and maintenance personnel can conveniently monitor and diagnose the state of the fan remotely.
Safety: the Beidou system provides a stable communication link and data encryption measures, so that the safety of data transmission is ensured.
By the method, structural health monitoring of the offshore wind turbine becomes more efficient and reliable, and operation and maintenance efficiency and safety of the offshore wind farm are improved.
S2, determining the weight of each evaluation index by using an analytic hierarchy process, and adjusting according to the Vague value to obtain an adjusted weight;
because the expert is often easily affected by subjective judgment and fuzzy information when setting the weight of the evaluation index, the expert is difficult to achieve complete objectivity and accuracy. Vague value theory can provide a more refined tool for experts to quantify their ambiguous and uncertain judgment of the assessment index. In addition, the use of Vague value theory to adjust weights can provide a more detailed explanation for the determination of weights, making the decision process more transparent and understandable. For example, if the true membership of the Vague value of a certain evaluation index is high, it is stated that the expert generally considers this index to be important. Meanwhile, through calculation and adjustment of the Vague value, weight distribution can be optimized, errors of human judgment are reduced, and an evaluation result is more scientific and accurate. In conclusion, the Vague value theory is introduced to adjust the weight of an expert in setting an evaluation index so as to improve the accuracy and the reliability of evaluation, integrate and express uncertainty and fuzzy judgment of the expert better and optimize the weight distribution result, thereby providing more enhanced and powerful decision support for the structural state monitoring of the offshore wind turbine.
The method comprises the steps of determining the weight of each evaluation index by using a analytic hierarchy process, adjusting according to a Vague value, and obtaining the adjusted weight, wherein the step of obtaining the adjusted weight comprises the following steps of:
S21, constructing a hierarchical structure model, wherein the hierarchical structure model takes the structural state monitoring of an offshore wind turbine as a target layer, takes the structural integrity and the running performance as an evaluation criterion layer, and takes the vibration frequency, the strain root mean square, the surface crack length, the blade pitch angle and the yaw angle as an evaluation index layer;
s22, comparing factors in the same layer in pairs to construct a judgment matrix; calculating the maximum eigenvalue and corresponding eigenvector of the judgment matrix by using an eigenvalue method to obtain the initial weight of each evaluation index;
Specifically, the factors in the same layer are compared pairwise to construct a judgment matrix; calculating the maximum eigenvalue and the corresponding eigenvector of the judgment matrix by using an eigenvalue method to obtain the initial weight of each evaluation index, wherein the initial weight comprises the following steps:
s221, comparing factors in the same layer in pairs, quantifying comparison results by using a nine-level scale method, for example, 1 represents that two factors are equally important, 3 represents that one factor is slightly more important than the other factor, and 9 represents that one factor is more important than the other factor, and constructing a judgment matrix;
S222, calculating the maximum eigenvalue and the corresponding eigenvector of the judgment matrix by utilizing the eigenvalue, and carrying out consistency test to ensure that the consistency ratio of the judgment matrix is less than 0.1 so as to ensure the rationality of the weight;
Calculating the maximum eigenvalue and eigenvector: the maximum eigenvalue λ max and corresponding eigenvector of the decision matrix a are calculated using eigenvalue methods, which typically involves calculating the eigenvalue of the matrix det (a- λi) =0, solving for the eigenvalue, and then finding the maximum eigenvalue λ max. The feature vector corresponding to the maximum feature value contains the initial weight of each evaluation index.
Consistency test: calculating a consistency index CI= (lambda max -n)/(n-1), wherein n is the order of the judgment matrix, and I represents the identity matrix; searching a random consistency index RI, and determining according to a matrix order n; the consistency ratio cr=ci/RI is calculated, and if CR is less than 0.1, the consistency of the judgment matrix is considered acceptable, otherwise the judgment matrix needs to be adjusted until the consistency ratio meets the requirement.
S223, carrying out normalization processing on the feature vectors so that the sum of all weights is 1, and dividing each element of the feature vectors by the sum of the feature vectors.
S23, converting a language fuzzy variable of an evaluation index given by an expert into a Vague value based on a true membership function and a false membership function;
In the Vague set theory, a Vague value is used to describe the ambiguity of an object belonging to a certain set, and the Vague value is generally defined by a true membership function and a false membership function, which respectively represent the degree to which an element belongs to a set and the degree to which an element does not belong to a set. When converting the language fuzzy variable of the evaluation index given by the expert into the Vague value, the method comprises the following steps:
1) Determining a true membership function: the true membership function reflects the degree to which an expert believes an evaluation index belongs to a particular class. It can be determined by:
expert scoring: a panel of experts is allowed to score each evaluation index, typically within a specific range, such as between 0 and 1.
Language variable conversion: the expert's language scores (e.g. "very low", "medium", "high", "very high") are converted to numerical values, which are mapped to numerical values between 0 and 1, for example using a fuzzy quantization method.
2) Determining a false membership function: the pseudo-membership function reflects the degree to which an expert believes an evaluation index does not belong to a particular class. It can be determined by:
The principle of complementarity: if the value of the true membership function is t i, then the value of the false membership function may be set to 1-t i, indicating the degree of non-membership.
3) Calculating Vague values: once the true and false membership functions are determined, a Vague value may be calculated.
Specifically, the expression of the Vague value is:
the calculation formula of the true membership function is as follows:
the calculation formula of the false membership function is as follows:
Wherein t i represents a true membership degree, i represents a numerical value of an evaluation index, a and b represent a minimum value and a maximum value in an evaluation index language fuzzy variable interval, f i represents a false membership degree, and V represents a Vague value.
For example: the language fuzzy variable of one evaluation index is 'high', and the language fuzzy variable is obtained through expert scoring and fuzzy quantization: true membership t i = 0.8 (indicating that the expert deems this index "high" to be 80%); the false membership degree f i=1-ti =0.2 (indicating that the expert considers this index "not high" to be 20%). Such a conversion then allows to handle the ambiguous and uncertain evaluation of the expert in a quantitative way, thus providing a more flexible and accurate means for subsequent decision analysis and data processing.
S24, calculating the score of each evaluation index by using a calculation function, determining the importance level of the evaluation index according to the score, and determining the weight of each evaluation index by normalizing the score;
specifically, the process of calculating the score of each evaluation index and determining the importance level and weight thereof using a calculation function generally includes the steps of:
1) Calculating a score: first, a calculation function needs to be defined to convert the Vague value into a score. This calculation function may calculate a composite score based on the true and false membership of the Vague value. The calculation formula of the calculation function is as follows:
wherein S i represents the score of the evaluation index;
2) Determining an importance level: from the score of each evaluation index, the importance level thereof can be determined. The higher the score, the more important the index is generally meant. For example: the evaluation index with the highest score may be regarded as a "very important" level; the evaluation index of the intermediate score may be "medium importance"; the lowest scoring evaluation index may be "less important".
3) Normalizing the score: to determine the weight of each evaluation index, the score needs to be normalized to within the [0, 1] interval. This can be achieved by the following formula: normalized score= (score-minimum score)/(maximum score-minimum score). This step may be omitted if the score is already in the 0,1 interval.
4) Determining weights: the normalized score may be a weight for each evaluation index. These weights reflect the relative importance of each index in the overall evaluation. For example: assume three evaluation indexes, the normalized scores of which are 0.5, 0.8 and 0.3, respectively. These scores can be directly weighted: weight of index 1 = 0.5; weight of index 2 = 0.8; weight of index 3 = 0.3.
And S25, adjusting the weight of each evaluation index by using the true membership of the Vague value to obtain the adjusted weight.
Specifically, the method for adjusting the weight of each evaluation index by using the true membership of the Vague value, and obtaining the adjusted weight includes the following steps:
The true membership of the Vague value of each evaluation index is obtained, the true membership of the Vague value is combined with the weight of the corresponding evaluation index, and normalization processing is carried out to obtain an adjusted weight, wherein the adjusted weight=the true membership of the Vague value of the weight of the evaluation index.
And S3, calculating the comprehensive health index of the offshore wind turbine structure according to the Vague value of each evaluation index and the adjusted weight, and evaluating the health grade of the offshore wind turbine structure based on the health index.
The comprehensive health index of the offshore wind turbine structure is calculated according to the Vague value of each evaluation index and the adjusted weight, and the health grade of the offshore wind turbine structure is evaluated based on the health index, and the method comprises the following steps of:
s31, calculating the comprehensive health index of the offshore wind turbine structure according to the Vague value of each evaluation index and the adjusted weight by using a weighted summation method;
S32, evaluating the health grade of the offshore wind turbine structure according to the comprehensive health index of the offshore wind turbine structure and a preset health grade classification table.
Specifically, the health level of the offshore wind turbine structure comprises excellent, good, general, warning and dangerous, and each health level corresponds to one comprehensive health index threshold interval respectively. The health index dividing criteria are: the excellent comprehensive health index threshold interval is 0.8-1.0, the good comprehensive health index threshold interval is 0.6-0.8, the general comprehensive health index threshold interval is 0.4-0.6, the warning comprehensive health index threshold interval is 0.2-0.4, and the dangerous comprehensive health index threshold interval is 0-0.2.
According to another aspect of the invention, there is provided a Beidou-based offshore wind turbine structure state monitoring system, which comprises an evaluation index extraction module, an index weight determination module and a health grade evaluation module, wherein the evaluation index extraction module, the index weight determination module and the health grade evaluation module are sequentially connected;
the evaluation index extraction module is used for acquiring monitoring data of the offshore wind turbine structure based on the Beidou technology and extracting evaluation indexes based on the monitoring data;
The index weight determining module is used for determining the weight of each evaluation index by using a analytic hierarchy process, and adjusting according to the Vague value to obtain the adjusted weight;
the health grade evaluation module is used for calculating the comprehensive health index of the offshore wind turbine structure according to the Vague value of each evaluation index and the adjusted weight, and evaluating the health grade of the offshore wind turbine structure based on the health index.
In summary, by means of the technical scheme, the method and the device not only overcome the limitation of the traditional monitoring method through the integrated Beidou communication technology and realize high-efficiency and real-time monitoring of the structural state of the offshore wind turbine, but also can determine the weight of the evaluation index through a hierarchical analysis method, fully consider the uncertainty and the ambiguity of an expert in the process of setting the weight of the evaluation index by introducing the Vague value theory, improve the scientificity and the accuracy of the evaluation result, adjust the weight of the evaluation index according to the true membership of the Vague value, dynamically reflect the change of the structural state of the offshore wind turbine, improve the adaptability and the flexibility of an evaluation model and evaluate the structural health state of the offshore wind turbine more accurately.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. The method for monitoring the structural state of the offshore wind turbine based on the Beidou is characterized by comprising the following steps of:
s1, acquiring monitoring data of an offshore wind turbine structure based on a Beidou technology, and extracting an evaluation index based on the monitoring data;
S2, determining the weight of each evaluation index by using an analytic hierarchy process, and adjusting according to the Vague value to obtain an adjusted weight;
And S3, calculating the comprehensive health index of the offshore wind turbine structure according to the Vague value of each evaluation index and the adjusted weight, and evaluating the health grade of the offshore wind turbine structure based on the health index.
2. The method for monitoring the state of the offshore wind turbine structure based on the Beidou according to claim 1, wherein the Beidou technology-based collection of the monitoring data of the offshore wind turbine structure, the extraction of the evaluation index based on the monitoring data, and the preprocessing of the evaluation index comprise the following steps:
s11, collecting monitoring data of the offshore wind turbine structure through detection equipment pre-installed on the offshore wind turbine structure, and cleaning, denoising and normalizing the monitoring data;
And S12, selecting characteristics related to the structural health state of the offshore wind turbine from the processed monitoring data as evaluation indexes, and transmitting the monitoring data and the evaluation index data to a remote monitoring terminal by using a Beidou communication technology.
3. The method for monitoring the structural state of the offshore wind turbine based on Beidou according to claim 1, wherein the step of determining the weight of each evaluation index by using a hierarchical analysis method and adjusting according to a Vague value to obtain the adjusted weight comprises the following steps:
S21, constructing a hierarchical structure model, wherein the hierarchical structure model takes the structural state monitoring of an offshore wind turbine as a target layer, takes the structural integrity and the running performance as an evaluation criterion layer, and takes the vibration frequency, the strain root mean square, the surface crack length, the blade pitch angle and the yaw angle as an evaluation index layer;
S22, comparing the factors in the same layer to construct a judgment matrix; calculating the maximum eigenvalue and corresponding eigenvector of the judgment matrix by using an eigenvalue method to obtain the initial weight of each evaluation index;
s23, converting a language fuzzy variable of an evaluation index given by an expert into a Vague value based on a true membership function and a false membership function;
S24, calculating the score of each evaluation index by using a calculation function, determining the importance level of the evaluation index according to the score, and determining the weight of each evaluation index by normalizing the score;
And S25, adjusting the weight of each evaluation index by using the true membership of the Vague value to obtain the adjusted weight.
4. The method for monitoring the structural state of the offshore wind turbine based on Beidou according to claim 3, wherein factors in the same level are compared with each other to construct a judgment matrix; calculating the maximum eigenvalue and the corresponding eigenvector of the judgment matrix by using an eigenvalue method to obtain the initial weight of each evaluation index, wherein the initial weight comprises the following steps:
S221, comparing the factors in the same layer, and quantizing the comparison result by using a nine-level scale method to construct a judgment matrix;
s222, calculating the maximum eigenvalue and the corresponding eigenvector of the judgment matrix by utilizing the eigenvalue, and carrying out consistency test;
s223, carrying out normalization processing on the feature vector, wherein each element in the feature vector after normalization processing is the initial weight of the evaluation index.
5. The method for monitoring the structural state of the offshore wind turbine based on Beidou according to claim 3, wherein the expression of the Vague value is:
the calculation formula of the true membership function is as follows:
the calculation formula of the false membership function is as follows:
Wherein t i represents a true membership degree, i represents a numerical value of an evaluation index, a and b represent a minimum value and a maximum value in an evaluation index language fuzzy variable interval, f i represents a false membership degree, and V represents a Vague value.
6. The method for monitoring the structural state of the offshore wind turbine based on Beidou according to claim 5, wherein a calculation formula of the calculation function is as follows:
in the formula, S i represents the score of the evaluation index.
7. The method for monitoring the structural state of the offshore wind turbine based on Beidou according to claim 3, wherein the step of adjusting the weight of each evaluation index by using the true membership of the Vague value to obtain the adjusted weight comprises the following steps:
The true membership of the Vague value of each evaluation index is obtained, the true membership of the Vague value is combined with the weight of the corresponding evaluation index, and normalization processing is carried out to obtain an adjusted weight, wherein the adjusted weight=the true membership of the Vague value of the weight of the evaluation index.
8. The method for monitoring the state of the offshore wind turbine structure based on Beidou according to claim 1, wherein the step of calculating the comprehensive health index of the offshore wind turbine structure according to the Vague value of each evaluation index and the adjusted weight and evaluating the health grade of the offshore wind turbine structure based on the health index comprises the following steps:
s31, calculating the comprehensive health index of the offshore wind turbine structure according to the Vague value of each evaluation index and the adjusted weight by using a weighted summation method;
S32, evaluating the health grade of the offshore wind turbine structure according to the comprehensive health index of the offshore wind turbine structure and a preset health grade classification table.
9. The method for monitoring the state of an offshore wind turbine structure based on Beidou according to claim 8, wherein the health level of the offshore wind turbine structure comprises excellent, good, general, warning and dangerous, and each health level corresponds to a comprehensive health index threshold interval.
10. The Beidou-based offshore wind turbine structure state monitoring system is used for realizing the steps of the Beidou-based offshore wind turbine structure state monitoring method according to any one of claims 1-9, and is characterized by comprising an evaluation index extraction module, an index weight determination module and a health grade evaluation module, wherein the evaluation index extraction module, the index weight determination module and the health grade evaluation module are sequentially connected;
the evaluation index extraction module is used for acquiring monitoring data of the offshore wind turbine structure based on the Beidou technology and extracting evaluation indexes based on the monitoring data;
The index weight determining module is used for determining the weight of each evaluation index by using a analytic hierarchy process, and adjusting according to the Vague value to obtain the adjusted weight;
the health grade evaluation module is used for calculating the comprehensive health index of the offshore wind turbine structure according to the Vague value of each evaluation index and the adjusted weight, and evaluating the health grade of the offshore wind turbine structure based on the health index.
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