Evaluation set determination method and device, storage medium and electronic equipment

Abstract

The embodiment of the invention provides an evaluation set determination method and device, a storage medium and electronic equipment, and the method comprises the steps: determining an index set for evaluating the power generation performance of a wind turbine generator, and obtaining an index value corresponding to each index in the index set; the degradation degree corresponding to each index is determined according to the index value corresponding to each index, an evaluation set used for evaluating the power generation performance of the wind turbine generator set is determined, and the evaluation set comprises a plurality of evaluation elements; a membership degree set corresponding to each index is determined according to the deterioration degree and a membership degree function, and the membership degree set is used for indicating the membership degree of each index to each evaluation element; an evaluation set of the power generation performance of the wind turbine generator is determined according to the membership degree set, and each element in the evaluation set is used for indicating the membership degree of the power generation performance of the wind turbine generator belonging to each evaluation element.

Description

Technical Field

[0001] The present application relates to the field of wind power, and in particular, to a method and device for determining an evaluation set, a storage medium, and an electronic device. Background Art

[0002] Wind turbines work under alternating loads all year round and in harsh working environments. Therefore, it is inevitable that mechanical components will age, fail or even be damaged, which will seriously reduce the power generation performance. Evaluating the power generation performance of wind turbines plays an important role in reducing operation and maintenance costs, providing optimal maintenance strategies and improving the power generation capacity of wind turbines. How to accurately evaluate the performance of wind turbines has become a research hotspot.

[0003] At present, there are two methods for evaluating the power generation performance of wind turbines: one is to use intelligent algorithms such as neural networks to train the index values ​​and evaluation results to obtain the evaluation model; the other is to calculate based on some indicators of IET, and then compare the evaluation parameters of several wind turbines horizontally to perform ranking and comparison evaluation. However, the above methods do not take into account that the power generation performance of wind turbines is a gradual process, not a jump process, so the evaluation results obtained have the problem of low accuracy.

[0004] In the prior art, the evaluation results of the power generation performance of wind turbines have a problem of low accuracy, and no effective solution has been proposed yet.

[0005] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the invention

[0006] The embodiments of the present application provide a method and device for determining an evaluation set, a storage medium, and an electronic device, so as to at least solve the problem of low accuracy of the evaluation results of the power generation performance of a wind turbine in the prior art.

[0007] According to an embodiment of the present application, a method for determining an evaluation set is provided, comprising: determining an indicator set for evaluating the power generation performance of a wind turbine, and obtaining an indicator value corresponding to each indicator in the indicator set; determining a degradation degree corresponding to each indicator according to the indicator value corresponding to each indicator, and determining an evaluation set for evaluating the power generation performance of the wind turbine, wherein the evaluation set comprises: a plurality of evaluation elements; determining a membership set corresponding to each indicator according to the degradation degree and a membership function, wherein the membership set is used to indicate the membership degree of each indicator to each evaluation element; determining an evaluation set for the power generation performance of the wind turbine according to the membership set, wherein each element in the evaluation set is used to indicate the membership degree of the power generation performance of the wind turbine to each evaluation element.

[0008] In an exemplary embodiment, an evaluation set of power generation performance of the wind turbine is determined based on the membership set, including: determining a constant weight of each indicator, and modifying the constant weight according to the degradation degree to obtain a target weight of each indicator; determining an evaluation matrix of each indicator according to the target weight of each indicator and the membership set corresponding to each indicator; and determining an evaluation set of power generation performance of the wind turbine according to the evaluation matrix of each indicator.

[0009] In an exemplary embodiment, determining the constant weight of each indicator includes: obtaining a sample indicator value corresponding to each indicator in the indicator set, wherein the sample indicator value is an indicator value corresponding to a sample wind turbine; determining a sample evaluation value corresponding to each indicator of each wind turbine according to the sample indicator value; determining the proportion of the indicator value of each sample wind turbine under each indicator according to the sample evaluation value corresponding to each indicator; determining an entropy value of each indicator according to the proportion of the indicator value; and determining the constant weight of each indicator according to the entropy value of each indicator.

[0010] In an exemplary embodiment, the constant weight is modified according to the degradation degree to obtain the target weight of each indicator, including: modifying the constant weight to obtain the target weight of each indicator by the following formula:

[0011] Among them, w j is the target weight of the jth indicator in the indicator set, is the constant weight of the jth indicator, l j is the degradation degree of the jth index, T is the variable weight coefficient, l k is the degradation degree of the kth indicator in the indicator set, is the constant weight of the kth indicator, and Z is used to indicate the number of indicators in the indicator set.

[0012] In an exemplary embodiment, determining an evaluation set of power generation performance of the wind turbine generator system according to the evaluation matrix of each indicator includes: performing a sum operation on the evaluation matrix of each indicator to obtain the evaluation set.

[0013] In an exemplary embodiment, the degradation degree corresponding to each indicator is determined according to the indicator value corresponding to each indicator, including: judging the membership of the degradation degree to each evaluation element according to the membership function to determine the membership of each indicator to each evaluation element; and determining the membership set corresponding to each indicator according to the membership of each indicator to each evaluation element.

[0014] According to another embodiment of the present application, a device for determining an evaluation set is provided, comprising: a first determination module, used to determine an indicator set for evaluating the power generation performance of a wind turbine, and to obtain an indicator value corresponding to each indicator in the indicator set; a second determination module, used to determine the degradation degree corresponding to each indicator according to the indicator value corresponding to each indicator, and to determine an evaluation set for evaluating the power generation performance of the wind turbine, wherein the evaluation set comprises: a plurality of evaluation elements; a third determination module, used to determine a membership set corresponding to each indicator according to the degradation degree and a membership function, wherein the membership set is used to indicate the membership degree of each indicator to each evaluation element; a fourth determination module, used to determine the evaluation set of the power generation performance of the wind turbine according to the membership set.

[0015] According to another embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when run.

[0016] According to another embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0017] According to another embodiment of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0018] Through the present application, an index set for evaluating the power generation performance of a wind turbine is determined, and an index value corresponding to each index in the index set is obtained; the degradation degree corresponding to each index is determined according to the index value corresponding to each index, and an evaluation set for evaluating the power generation performance of the wind turbine is determined, wherein the evaluation set includes: multiple evaluation elements; the membership set corresponding to each index is determined according to the degradation degree and the membership function, wherein the membership set is used to indicate the membership degree of each index belonging to each evaluation element; the evaluation set of the power generation performance of the wind turbine is determined according to the membership set. That is, in the embodiment of the present application, the fuzzy evaluation method based on fuzzy mathematics evaluates the power generation performance of the wind turbine, and the results in the evaluation set include not only affirmation and negation, but also a transition stage in an intermediate state, which can better reflect its true operating state, thereby realizing accurate and objective quantification of the power generation performance of the wind turbine. Therefore, the problem of low accuracy in the evaluation results of the power generation performance of the wind turbine in the prior art can be solved, and the effect of improving the accuracy can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0021] Figure 1 It is a hardware structure block diagram of a computer device for a method for determining an evaluation set in an embodiment of the present application;

[0022] Figure 2 is a flowchart of a method for determining an evaluation set according to an embodiment of the present application;

[0023] Figure 3 It is a structural block diagram of a device for determining an evaluation set according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] The embodiments of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0026] The method embodiments provided in the embodiments of the present application can be executed in a computer device or a similar computing device. Taking running on a computer device as an example, Figure 1 1 is a hardware structure block diagram of a computer device for determining a method for evaluating a set in an embodiment of the present application. Figure 1 As shown, the computer device may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the above-mentioned computer device may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0027] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for determining the evaluation set in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0028] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer device. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] In this embodiment, a method for determining an evaluation set is provided, which is applied to the above-mentioned computer device. Figure 2 is a flowchart of a method for determining an evaluation set according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps:

[0030] Step S202, determining an index set for evaluating the power generation performance of the wind turbine, and obtaining an index value corresponding to each index in the index set;

[0031] It should be noted that the indicators include but are not limited to annual power entropy of wind turbines, annual power excess entropy, wind speed power responsiveness, wind energy utilization coefficient, power accessibility, time availability and energy availability.

[0032] Step S204, determining the degradation degree corresponding to each indicator according to the indicator value corresponding to each indicator, and determining an evaluation set for evaluating the power generation performance of the wind turbine generator set, wherein the evaluation set includes: a plurality of evaluation elements;

[0033] According to the obtained index values, the degradation degree of each index is calculated, and the performance degradation degree of the wind turbine in each index is quantified through normalization. The method for determining the degradation degree may depend on the index type (such as the smaller the better, the intermediate type, or the larger the better) and the specific limit value.

[0034] A set of multiple evaluation elements is established, which represent different evaluation levels of wind turbine power generation performance, such as "excellent", "good", "average" and "poor". The determination of the evaluation set provides a framework for subsequent fuzzy evaluation.

[0035] Step S206, determining a membership set corresponding to each indicator according to the degradation degree and the membership function, wherein the membership set is used to indicate the membership of each indicator to each evaluation element;

[0036] In the embodiment of the present application, triangular and semi-trapezoidal functions are used as membership functions, which can convert the index value into the membership of each evaluation element in the evaluation set. The parameter setting of the membership function (such as the fuzzy decomposition interval) should be based on expert experience and criteria to ensure that the function can accurately reflect the association between the index value and the evaluation element.

[0037] By combining the degradation degree with the membership function, a membership set is generated for each indicator, and each element in the set corresponds to the degree to which the indicator belongs to a specific evaluation element in the evaluation set.

[0038] Step S208 , determining an evaluation set of the power generation performance of the wind turbine according to the membership set, wherein each element in the evaluation set is used to indicate the membership of the power generation performance of the wind turbine to each evaluation element.

[0039] According to the membership set, the weight of each indicator is calculated using the entropy weight method and variable weight theory, and then the overall evaluation set of wind turbine power generation performance is determined. Each evaluation element is selected according to its maximum membership principle to reflect the comprehensive evaluation results of wind turbine power generation performance.

[0040] Through the above steps, an index set for evaluating the power generation performance of a wind turbine is determined, and an index value corresponding to each index in the index set is obtained; the degradation degree corresponding to each index is determined according to the index value corresponding to each index, and an evaluation set for evaluating the power generation performance of a wind turbine is determined, wherein the evaluation set includes: multiple evaluation elements; the membership set corresponding to each index is determined according to the degradation degree and the membership function, wherein the membership set is used to indicate the membership degree of each index to each evaluation element; the evaluation set of the power generation performance of the wind turbine is determined according to the membership set. That is, in the embodiment of the present application, the fuzzy evaluation method based on fuzzy mathematics evaluates the power generation performance of the wind turbine, and the results in the evaluation set include not only affirmation and negation, but also a transition stage in an intermediate state, which can better reflect its true operating state, thereby realizing accurate and objective quantification of the power generation performance of the wind turbine. Therefore, the problem of low accuracy in the evaluation results of the power generation performance of the wind turbine in the prior art can be solved, and the effect of improving the accuracy can be achieved.

[0041] In an exemplary embodiment, an evaluation set of power generation performance of the wind turbine is determined based on the membership set, including: determining a constant weight of each indicator, and modifying the constant weight according to the degradation degree to obtain a target weight of each indicator; determining an evaluation matrix of each indicator according to the target weight of each indicator and the membership set corresponding to each indicator; and determining an evaluation set of power generation performance of the wind turbine according to the evaluation matrix of each indicator.

[0042] In the embodiment of the present application, the weight of the index is dynamically adjusted according to the degradation of the wind turbine. In some cases, if the index value exceeds the limit, its impact on the power generation performance may be underestimated. The weight correction of the variable weight theory can make up for this evaluation deviation and ensure that the evaluation result is closer to the actual operating condition of the wind turbine.

[0043] The evaluation matrix is ​​a two-dimensional matrix formed by combining the membership degree of the wind turbine under each indicator with the modified target weight.

[0044] In an exemplary embodiment, determining the constant weight of each indicator includes: obtaining a sample indicator value corresponding to each indicator in the indicator set, wherein the sample indicator value is an indicator value corresponding to a sample wind turbine; determining a sample evaluation value corresponding to each indicator of each wind turbine according to the sample indicator value; determining the proportion of the indicator value of each sample wind turbine under each indicator according to the sample evaluation value corresponding to each indicator; determining an entropy value of each indicator according to the proportion of the indicator value; and determining the constant weight of each indicator according to the entropy value of each indicator.

[0045] In the embodiment of the present application, the entropy weight method is used to determine the constant weight of each indicator. The entropy weight method is an objective weighting method, which determines the weight of the indicator according to the degree of variation of the indicator data. The greater the degree of variation of the data, the greater the weight of the indicator, and vice versa. Specifically:

[0046] 1) Obtain the sample index value corresponding to each index in the index set, that is, find the relevant index values ​​of all wind turbines used for evaluation from the collected data. These index values ​​are used as samples for subsequent weight calculation. The sample index values ​​reflect the specific performance of the wind turbine power generation performance under different indicators.

[0047] 2) Determine the sample evaluation value corresponding to each indicator of each wind turbine according to the sample indicator value, that is, it is necessary to evaluate each indicator of the wind turbine to obtain the evaluation value of each wind turbine under each indicator. The evaluation value can be determined based on different evaluation criteria, such as the degradation degree vector and membership function mentioned above, which converts the indicator value into a comparable evaluation result.

[0048] 3) Determine the weight of the index value of each sample wind turbine under each index according to the sample evaluation value corresponding to each index, that is, calculate the relative importance or weight of the index value of each wind turbine under each index. The calculation of the weight can be based on the standardization of the sample evaluation value, that is, normalize the evaluation value under each index so that each evaluation value is between 0 and 1, reflecting the relative performance of the wind turbine under the index.

[0049] 4) Determine the entropy value of each indicator according to the weight of the indicator value, wherein the entropy value is a statistical concept used to measure the uncertainty or information content of the data. In the embodiment of the present application, the entropy value reflects the uncertainty of the weight of the indicator value, that is, whether the distribution of the indicator value is uniform. If the weight distribution is uniform, the entropy value will be close to the maximum value, indicating that the performance difference of the wind turbine under this indicator is not large; if the weight distribution is uneven, the entropy value will be small, indicating that the performance difference of the wind turbine under this indicator is obvious, and this indicator contributes more to the evaluation result.

[0050] 5) Determine the constant weight of each indicator according to the entropy value of each indicator, that is, determine the weight according to the entropy value of each indicator. Since the entropy value reflects the relative importance of the indicator, the entropy value will be considered in the weight determination formula, and 1-entropy value is usually used as the basis for weight calculation, thereby ensuring that those indicators with large performance differences (i.e. indicators with a large impact on the evaluation results) have larger weights. In this way, the evaluation model will pay more attention to those key indicators and improve the objectivity and accuracy of the evaluation results.

[0051] Through the above steps, the indicator weights can be automatically determined according to the degree of data variation, which not only avoids the bias of subjective weighting, but also ensures the scientificity and credibility of the evaluation results.

[0052] In an exemplary embodiment, the constant weight is modified according to the degradation degree to obtain the target weight of each indicator, including: modifying the constant weight to obtain the target weight of each indicator by the following formula:

[0053] Among them, w j is the target weight of the jth indicator in the indicator set, is the constant weight of the jth indicator, l j is the degradation degree of the jth index, T is the variable weight coefficient, l k is the degradation degree of the kth indicator in the indicator set, is the constant weight of the kth indicator, and Z is used to indicate the number of indicators in the indicator set.

[0054] In an exemplary embodiment, determining an evaluation set of power generation performance of the wind turbine generator system according to the evaluation matrix of each indicator includes: performing a sum operation on the evaluation matrix of each indicator to obtain the evaluation set.

[0055] It should be noted that an evaluation result can also be obtained based on the evaluation set, that is, according to the maximum membership principle, the power generation performance evaluation result of the wind turbine can be obtained.

[0056] In an exemplary embodiment, the degradation degree corresponding to each indicator is determined according to the indicator value corresponding to each indicator, including: judging the membership of the degradation degree to each evaluation element according to the membership function to determine the membership of each indicator to each evaluation element; and determining the membership set corresponding to each indicator according to the membership of each indicator to each evaluation element.

[0057] First, for each indicator, the degradation degree is calculated by formula according to its actual measured value and standard operating limit or optimal operating range. The degradation degree reflects the degree to which the indicator deviates from its ideal state and is a quantitative indicator of wind turbine performance degradation.

[0058] Next, the membership function is used to determine the membership of the degradation degree to each evaluation element. The membership function is a mathematical function used in fuzzy set theory to describe the degree to which an element belongs to a set, where the set is the evaluation element. According to the nature of the indicator (such as the smaller the better, the intermediate or the larger the better), different membership functions (such as triangular or semi-trapezoidal functions) are selected, and the parameters of the function, such as the upper and lower limits of the fuzzy decomposition interval, are determined based on expert experience.

[0059] For each indicator, according to its degradation degree and the corresponding membership function, the membership of the indicator to different evaluation elements can be obtained. These memberships together constitute a membership set, which fully describes the fuzzy distribution of the indicator among the evaluation elements.

[0060] Finally, based on the membership set of each indicator, combined with the weight determination method (such as entropy weight method and variable weight theory), the final degradation degree that comprehensively considers all evaluation elements can be calculated. This process takes into account the different importance of different evaluation elements on the performance of wind turbines. Therefore, the final degradation degree of each indicator is calculated based on its membership and weight on different evaluation elements.

[0061] In the exemplary embodiment of the present application, the degradation of the index is quantified to the fuzzy evaluation element through the membership function, and the importance of each evaluation element is determined through the weight calculation method, and finally an evaluation model that can comprehensively and objectively reflect the power generation performance of the wind turbine is formed. This method overcomes the limitations of traditional evaluation methods and can more accurately reflect the complexity and uncertainty of the operating status of the wind turbine.

[0062] In order to better understand the process of the method for determining the above evaluation set, the implementation method flow of determining the above evaluation set is described below in combination with an optional embodiment, but it is not used to limit the technical solution of the embodiment of the present application.

[0063] In this embodiment, a method for determining an evaluation set is provided, including:

[0064] Step 1: Data preparation;

[0065] Collect SCADA data such as wind speed and power of wind turbines.

[0066] Step 2: Data cleaning;

[0067] First, simple elimination is performed to eliminate the null value points, wind speed less than the cut-in wind speed data points, and wind speed greater than the cut-out wind speed data points in the data. Then, using quartile processing, the data after simple elimination are arranged in order of wind speed from small to large, and the data are divided into several intervals at intervals of 0.1m / s. The data in each interval is cleaned using the quartile algorithm. Finally, CFSFDP clustering is performed, and the data is normalized before clustering, and then cleaned using CFSFDP clustering.

[0068] Step 3: Establish a wind turbine power generation performance evaluation system;

[0069] For example, based on the annual power entropy En of the wind turbine, the annual power excess entropy He of the wind turbine, the wind speed power response R, the wind energy utilization coefficient η, and the power accessibility λp , time availability λ T and energy availability λ W Starting from a total of 7 indicators, a wind turbine generation performance evaluation system is established, and the calculation formulas are shown in equations (1)-(12). Based on this evaluation system, the various indicator values ​​of the wind turbine are calculated.

[0070]

[0071] Where n is the number of intervals into which the data is divided according to wind speed; En(i) is the power entropy of the i-th wind speed interval; w i is the weight of each wind speed interval.

[0072]

[0073] Wherein, N is the number of data samples; N(i) is the number of wind speed power data in each wind speed interval.

[0074]

[0075] Among them, p k is the kth power value in the i-th wind speed interval; is the mean power in the i-th wind speed interval.

[0076]

[0077] Where He(i) is the excess entropy of the i-th wind speed interval.

[0078]

[0079] Among them, S 2 (i) is the power variance in the i-th wind speed interval.

[0080] R = corrcoef(C v ,C p ) (6);

[0081] Among them, C v is the wind speed variance coefficient of the entire wind speed range; C p is the power variance coefficient for the entire wind speed range.

[0082] C v =[C v1 ,C v2 ,...,C vn ] (7);

[0083] Among them, C vn is the standard deviation of wind speed in the nth wind speed interval.

[0084] C p =[Cp1 ,C p2 ,...,C pn ] (8);

[0085] Among them, C pn is the power standard deviation in the nth wind speed interval.

[0086]

[0087] Where K is the total number of wind speed power groups before the rated wind speed in the data; P i is the ith actual power before rated wind speed; ρ is the air density; A is the area swept by the blade; V i is the i-th wind speed value before the rated wind speed.

[0088]

[0089] Among them, W theory is the annual theoretical power generation; W design The annual designed power generation.

[0090]

[0091] Among them, T1 is the duration that the wind turbine is in the power generation state; T0 is the total duration of the statistical period.

[0092]

[0093] Among them, W real is the actual annual power generation of the wind turbine.

[0094] Step 4: Construct a wind turbine performance evaluation model; first, based on the concept of degradation, according to the wind turbine operation status and relevant experience, use formulas (13)-(15) to normalize the index values ​​obtained in step 3 to obtain the degradation vector l; secondly, use 4 evaluation elements as the evaluation set for wind turbine performance evaluation, that is, P = {P1, P2, P3, P4} = {excellent, good, medium, poor} = {E, G, W, S}. Use triangular and semi-trapezoidal functions as membership functions, determine the fuzzy decomposition interval of the membership function according to relevant criteria and expert experience, and establish the membership function corresponding to each state for each indicator; then, according to the degradation vector l and the membership function, obtain the membership set V of P1, P2, P3, P4. Ui =[V Ui 1 ,V Ui 2 ,V Ui 3 ,V Ui 4 ], where V Uim The wind turbine index Ui belongs to P m Membership degree; Finally, according to the evaluation value matrix of m wind turbines, the constant weight of each indicator is determined by the entropy weight method And use the variable weight theory to correct it and get the final weight w j , the calculation formula is as follows: (16)-(18). Using the evaluation matrix V of all indicators of wind turbines U* =[V U1 ,V U2 ,V U3 ,V U4 ,V U5 ,V U6 ,V U7 ] T According to formula (19), we can get the evaluation set V U ,Finally, according to the maximum membership principle, the power generation performance evaluation results of the wind turbine are obtained.

[0095] For the smaller the better indicator:

[0096]

[0097] Among them, l(x) is the degradation degree of the indicator; x is the actual value of the indicator; [α1, β1] is the operating limit of the indicator.

[0098] For intermediate indicators:

[0099]

[0100] Among them, [β1, α2] is the optimal operating range of the indicator; α2 and β3 are the operating limits of the indicator

[0101] For the larger the better indicator:

[0102]

[0103] Among them, e j is the entropy value of index j.

[0104]

[0105] Among them, p ij is the proportion of the index value of the i-th wind turbine under index j.

[0106]

[0107] Among them, r ij is the evaluation value of index j of the i-th wind turbine.

[0108]

[0109] Among them, l j is the degradation degree of index j; T is the variable weight coefficient, which can be obtained based on experience.

[0110]

[0111] Among them, V Uj is the membership set of index j.

[0112] In the embodiment of the present application, first, the SCADA data of the wind turbine is cleaned to provide more accurate data for the power generation performance evaluation of the wind turbine and ensure the accuracy of the analysis results; then, a wind turbine power generation performance evaluation system is constructed to achieve a more comprehensive, accurate and objective quantitative evaluation of the power generation performance of the wind turbine; finally, based on the membership function, the entropy weight method and the variable weight theory, a wind turbine power generation performance evaluation model is constructed, which can reflect the uncertainty of the membership degree of the wind turbine power generation performance and effectively present randomness and fuzziness. The combination of the entropy weight method and the variable weight theory can better discover the degradation of the wind turbine power generation performance and better reflect its actual operating status, thereby accurately and objectively quantifying the power generation performance of the wind turbine.

[0113] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0114] In this embodiment, a device for determining an evaluation set is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated hereafter. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0115] Figure 3 is a structural block diagram of a device for determining an evaluation set according to an embodiment of the present application, such as Figure 3 As shown, the device comprises:

[0116] A first determination module 32 is used to determine an indicator set for evaluating the power generation performance of a wind turbine, and to obtain an indicator value corresponding to each indicator in the indicator set;

[0117] The second determination module 34 is used to determine the degradation degree corresponding to each indicator according to the indicator value corresponding to each indicator, and determine an evaluation set for evaluating the power generation performance of the wind turbine, wherein the evaluation set includes: a plurality of evaluation elements;

[0118] A third determination module 36 is used to determine a membership set corresponding to each indicator according to the degradation degree and the membership function, wherein the membership set is used to indicate the membership of each indicator to each evaluation element;

[0119] The fourth determination module 38 is used to determine an evaluation set of power generation performance of the wind turbine according to the membership set.

[0120] Through the above device, an index set for evaluating the power generation performance of a wind turbine is determined, and an index value corresponding to each index in the index set is obtained; the degradation degree corresponding to each index is determined according to the index value corresponding to each index, and an evaluation set for evaluating the power generation performance of the wind turbine is determined, wherein the evaluation set includes: multiple evaluation elements; the membership set corresponding to each index is determined according to the degradation degree and the membership function, wherein the membership set is used to indicate the membership of each index to each evaluation element; the evaluation set of the power generation performance of the wind turbine is determined according to the membership set. That is, in the embodiment of the present application, the fuzzy evaluation method based on fuzzy mathematics evaluates the power generation performance of the wind turbine, and the results in the evaluation set include not only affirmation and negation, but also a transition stage in an intermediate state, which can better reflect its true operating state, thereby realizing accurate and objective quantification of the power generation performance of the wind turbine. Therefore, the problem of low accuracy in the evaluation results of the power generation performance of the wind turbine in the prior art can be solved, and the effect of improving the accuracy can be achieved.

[0121] In an exemplary embodiment, an evaluation set of power generation performance of the wind turbine is determined based on the membership set, including: determining a constant weight of each indicator, and modifying the constant weight according to the degradation degree to obtain a target weight of each indicator; determining an evaluation matrix of each indicator according to the target weight of each indicator and the membership set corresponding to each indicator; and determining an evaluation set of power generation performance of the wind turbine according to the evaluation matrix of each indicator.

[0122] In an exemplary embodiment, the fourth determination module 38 is used to obtain sample index values ​​corresponding to each indicator in the index set, wherein the sample index values ​​are index values ​​corresponding to the sample wind turbines; determine sample evaluation values ​​corresponding to each indicator of each wind turbine according to the sample index values; determine the proportion of the index value of each sample wind turbine under each indicator according to the sample evaluation values ​​corresponding to each indicator; determine the entropy value of each indicator according to the proportion of the index values; and determine the constant weight of each indicator according to the entropy value of each indicator.

[0123] In an exemplary embodiment, the fourth determination module 38 is used to modify the constant weights by the following formula to obtain the target weight of each indicator:

[0124] Among them, w j is the target weight of the jth indicator in the indicator set, is the constant weight of the jth indicator, l j is the degradation degree of the jth index, T is the variable weight coefficient, l k is the degradation degree of the kth indicator in the indicator set, is the constant weight of the kth indicator, and Z is used to indicate the number of indicators in the indicator set.

[0125] In an exemplary embodiment, the fourth determination module 38 is configured to perform a sum operation on the evaluation matrix of each indicator to obtain an evaluation set.

[0126] In an exemplary embodiment, the second determination module 34 is used to determine the membership of the degradation degree to each evaluation element according to the membership function to determine the membership of each indicator to each evaluation element; and determine the membership set corresponding to each indicator according to the membership of each indicator to each evaluation element.

[0127] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0128] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0129] Optionally, in this embodiment, the storage medium may be configured to store program codes for executing the following steps:

[0130] S1, determining an index set for evaluating the power generation performance of a wind turbine generator set, and obtaining an index value corresponding to each index in the index set;

[0131] S2, determining the degradation degree corresponding to each indicator according to the indicator value corresponding to each indicator, and determining an evaluation set for evaluating the power generation performance of the wind turbine generator set, wherein the evaluation set includes: a plurality of evaluation elements;

[0132] S3, determining a membership set corresponding to each indicator according to the degradation degree and the membership function, wherein the membership set is used to indicate the membership of each indicator to each evaluation element;

[0133] S4, determining an evaluation set of power generation performance of the wind turbine according to the membership set, wherein each element in the evaluation set is used to indicate the membership of the power generation performance of the wind turbine to each evaluation element.

[0134] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0135] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0136] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0137] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0138] S1, determining an index set for evaluating the power generation performance of a wind turbine generator set, and obtaining an index value corresponding to each index in the index set;

[0139] S2, determining the degradation degree corresponding to each indicator according to the indicator value corresponding to each indicator, and determining an evaluation set for evaluating the power generation performance of the wind turbine generator set, wherein the evaluation set includes: a plurality of evaluation elements;

[0140] S3, determining a membership set corresponding to each indicator according to the degradation degree and the membership function, wherein the membership set is used to indicate the membership of each indicator to each evaluation element;

[0141] S4, determining an evaluation set of power generation performance of the wind turbine according to the membership set, wherein each element in the evaluation set is used to indicate the membership of the power generation performance of the wind turbine to each evaluation element.

[0142] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0143] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0144] An embodiment of the present application also provides a computer program, which includes computer instructions, which are stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in any one of the above method embodiments.

[0145] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0146] S1, determining an index set for evaluating the power generation performance of a wind turbine generator set, and obtaining an index value corresponding to each index in the index set;

[0147] S2, determining the degradation degree corresponding to each indicator according to the indicator value corresponding to each indicator, and determining an evaluation set for evaluating the power generation performance of the wind turbine generator set, wherein the evaluation set includes: a plurality of evaluation elements;

[0148] S3, determining a membership set corresponding to each indicator according to the degradation degree and the membership function, wherein the membership set is used to indicate the membership of each indicator to each evaluation element;

[0149] S4, determining an evaluation set of power generation performance of the wind turbine according to the membership set, wherein each element in the evaluation set is used to indicate the membership of the power generation performance of the wind turbine to each evaluation element.

[0150] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0151] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0152] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining an evaluation set, characterized in that: include: Determining an index set for evaluating the power generation performance of a wind turbine generator set, and obtaining an index value corresponding to each index in the index set; Determining the degradation degree corresponding to each indicator according to the indicator value corresponding to each indicator, and determining an evaluation set for evaluating the power generation performance of the wind turbine generator set, wherein the evaluation set includes: a plurality of evaluation elements; Determining a membership set corresponding to each indicator according to the degradation degree and the membership function, wherein the membership set is used to indicate the membership of each indicator to each evaluation element; An evaluation set of the power generation performance of the wind turbine is determined according to the membership set, wherein each element in the evaluation set is used to indicate the membership of each evaluation element to which the power generation performance of the wind turbine belongs.

2. The method according to claim 1, characterized in that Determining an evaluation set of power generation performance of the wind turbine generator set according to the membership set includes: Determining a constant weight of each indicator, and modifying the constant weight according to the degradation degree to obtain a target weight of each indicator; Determine an evaluation matrix for each indicator according to the target weight of each indicator and the membership set corresponding to each indicator; An evaluation set of power generation performance of the wind turbine generator set is determined according to the evaluation matrix of each indicator.

3. The method according to claim 2, characterized in that Determine the constant weight of each indicator, including: Obtaining a sample index value corresponding to each index in the index set, wherein the sample index value is an index value corresponding to a sample wind turbine; Determine a sample evaluation value corresponding to each indicator of each wind turbine generator set according to the sample indicator value; Determine the weight of the index value of each sample wind turbine under each index according to the sample evaluation value corresponding to each index; Determine the entropy value of each indicator according to the proportion of the indicator value; The constant weight of each indicator is determined according to the entropy value of each indicator.

4. The method according to claim 2, characterized in that: The constant weight is modified according to the degradation degree to obtain the target weight of each indicator, including: The constant weight is modified by the following formula to obtain the target weight of each indicator: Among them, w j is the target weight of the jth indicator in the indicator set, is the constant weight of the jth indicator, l j is the degradation degree of the jth index, T is the variable weight coefficient, l k is the degradation degree of the kth indicator in the indicator set, is the constant weight of the kth indicator, and Z is used to indicate the number of indicators in the indicator set.

5. The method according to claim 2, characterized in that: Determining an evaluation set of power generation performance of the wind turbine generator set according to the evaluation matrix of each indicator includes: The evaluation matrix of each indicator is summed up to obtain an evaluation set.

6. The method according to claim 1, characterized in that Determining the degradation degree corresponding to each indicator according to the indicator value corresponding to each indicator includes: Determining the membership of the degradation degree to each evaluation element according to the membership function, so as to determine the membership of each indicator to each evaluation element; The membership set corresponding to each indicator is determined according to the membership of each indicator to each evaluation element.

7. A device for determining an evaluation set, characterized in that: include: A first determination module is used to determine an indicator set for evaluating the power generation performance of a wind turbine generator set, and to obtain an indicator value corresponding to each indicator in the indicator set; A second determination module is used to determine the degradation degree corresponding to each indicator according to the indicator value corresponding to each indicator, and determine an evaluation set for evaluating the power generation performance of the wind turbine, wherein the evaluation set includes: a plurality of evaluation elements; A third determination module is used to determine a membership set corresponding to each indicator according to the degradation degree and the membership function, wherein the membership set is used to indicate the membership of each indicator to each evaluation element; The fourth determination module is used to determine an evaluation set of power generation performance of the wind turbine according to the membership set.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 6 when executed.

9. An electronic device, comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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