Verification method of wind generating set and computer equipment

Abstract

The invention provides a verification method of a wind generating set and computer equipment, and the method comprises the steps: obtaining the estimated wind speed data of the position of the wind generating set based on the wind resource data of a wind power plant where the to-be-verified wind generating set is located; based on the estimated wind speed data and actually measured wind speed data at the wind generating set, determining a wind speed relationship between the actually measured wind speed and the estimated wind speed; based on the wind speed relation, target wind speed data actually measured at the wind generating set is corrected, and corrected wind speed data is obtained; and verifying the power characteristics of the wind generating set based on the corrected wind speed data. According to the verification method of the wind generating set and the computer equipment, the problems that errors exist in the wind speed data and the accuracy of the verification result is difficult to guarantee are solved, the errors of the actually-measured wind speed data can be improved, the corrected wind speed data are closer to the real wind speed condition, and the verification accuracy of the power characteristics of the set is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of wind power generation, and more specifically, to a verification method and computer equipment for a wind power generator set. Background Art

[0002] With the development of wind power technology, the requirements for the power characteristics of wind turbines are becoming higher and higher. The power characteristics can reflect the power generated by the unit at different wind speeds and / or rotation speeds, and are an important aspect of evaluating the performance of wind turbines.

[0003] In practical applications, due to the limitations of measurement equipment, measurement environment and cost, it is difficult to evaluate and verify the power characteristics of hundreds of units running in a wind farm. For example, in actual projects, the anemometer at the tail of the unit nacelle can be used as the source of wind speed data in power characteristic verification, so that the power characteristics of the unit can be verified quickly and conveniently.

[0004] However, the wind speed measured at the tail of the nacelle is easily affected by the wake of the wind turbine, and there may be a certain error. In addition, as the unit's operating years increase, the wind meter at the tail of the nacelle may also deviate. Therefore, the application of this method is subject to certain restrictions, and it is difficult to ensure the accuracy of the verification of power characteristics. Summary of the invention

[0005] In view of the problem that the wind speed data used for power characteristic verification of a wind turbine generator set in the related art has errors and it is difficult to ensure the accuracy of the verification result, the present disclosure provides a verification method and computer equipment for a wind turbine generator set.

[0006] A first aspect of the present disclosure provides a power verification method for a wind turbine generator set, the power verification method comprising: obtaining estimated wind speed data of the location of the wind turbine generator set based on wind resource data of a wind farm where the wind turbine generator set to be verified is located; determining a wind speed relationship between the measured wind speed at the wind turbine generator set and the estimated wind speed based on the estimated wind speed data and the measured wind speed data at the wind turbine generator set; based on the wind speed relationship, correcting target wind speed data measured at the wind turbine generator set to obtain corrected wind speed data; and verifying the power characteristics of the wind turbine generator set based on the corrected wind speed data.

[0007] Optionally, the wind speed relationship is determined in the following manner: the estimated wind speed data and the measured wind speed data are counted according to multiple time lengths to obtain a wind speed statistical data set, wherein the wind speed statistical data set includes multiple wind speed statistical data corresponding one by one to the multiple time lengths; based on the wind speed statistical data set, a target time length among the multiple time lengths is determined, wherein the wind speed statistical data corresponding to the target time length can reflect the data set characteristics of the wind speed statistical data set; based on the wind speed statistical data corresponding to the target time length, the wind speed relationship is determined.

[0008] Optionally, the wind speed statistical data include first statistical data corresponding to the estimated wind speed data and second statistical data corresponding to the measured wind speed data, wherein the target time length is determined in the following manner: for each time length of the multiple time lengths, data feature analysis is performed on the first statistical data and the second statistical data respectively to obtain a first data feature value of the first statistical data and a second data feature value of the second statistical data; and the time length in which the operation of the first data feature value and the second data feature value meets a preset condition is taken as the target time length.

[0009] Optionally, the first statistical data is determined in the following manner: based on the estimated wind speed data, according to each time length in the multiple time lengths, a plurality of first wind speed average values ​​corresponding to each time length are determined, wherein the estimated wind speed data is data obtained based on wind speed data having a height closest to the hub height of the wind turbine generator set; according to the multiple first wind speed average values ​​in random order, a wind speed statistical data set including the first statistical data is obtained, wherein the second statistical data is determined in the following manner: based on the measured wind speed data, according to each time length in the multiple time lengths, a plurality of second wind speed average values ​​corresponding to each time length are determined, wherein the measured wind speed data is wind speed data measured at the nacelle of the wind turbine generator set; according to the multiple second wind speed average values ​​in random order, a wind speed statistical data set including the second statistical data is obtained.

[0010] Optionally, data feature analysis is performed on the first statistical data and the second statistical data by the following method: data randomness analysis is performed on the first statistical data and the second statistical data respectively, the Hurst coefficient corresponding to the first statistical data is used as the first data feature value, and the Hurst coefficient corresponding to the second statistical data is used as the second data feature value.

[0011] Optionally, the power characteristics of the wind turbine generator set are verified in the following manner: target power data corresponding to the target wind speed data is acquired; and a power curve of the wind turbine generator set is obtained based on the correspondence between the target power data and the corrected wind speed data to verify the power characteristics of the wind turbine generator set.

[0012] Optionally, the power verification method further includes: eliminating wake interference data in the wind resource data based on the layout positions of the wind turbines in the wind farm; and determining the estimated wind speed data based on the eliminated wind resource data.

[0013] Optionally, the estimated wind speed data is obtained in the following manner: performing flow field simulation based on the wind resource data to obtain a flow field model of the wind farm; determining the wind farm wind speed data of the wind farm based on the flow field model; and using the data related to the wind turbine generator set in the wind farm wind speed data as the estimated wind speed data.

[0014] Optionally, the power verification method further includes: comparing the verified power characteristic with a preset standard power characteristic to obtain a power label value, wherein the power label value represents the difference between the verified power characteristic and the standard power characteristic.

[0015] Optionally, the wind speed relationship is expressed as a relationship function between the measured wind speed and the estimated wind speed at the wind turbine generator set, wherein the power verification method further includes: detecting the operating status of the wind turbine generator set based on the power label value and the function coefficient of the relationship function.

[0016] Optionally, the operating status of the wind turbine generator set is detected by at least one of the following methods: in response to the power tag value satisfying a first preset condition and the function coefficient satisfying a second preset condition, it is determined that the power characteristics of the wind turbine generator set are normal and the wind measuring equipment of the wind turbine generator set is operating normally; in response to the power tag value not satisfying the first preset condition and the function coefficient not satisfying the second preset condition, it is determined that the power characteristics of the wind turbine generator set are abnormal and the wind measuring equipment of the wind turbine generator set is operating abnormally; in response to the power tag value satisfying the first preset condition and the function coefficient not satisfying the second preset condition, it is determined that the wind measuring equipment of the wind turbine generator set is operating normally; in response to the power tag value not satisfying the first preset condition and the function coefficient satisfying the second preset condition, it is determined that the power generation equipment of the wind turbine generator set is operating abnormally.

[0017] A first aspect of the present disclosure provides a computer device, comprising: at least one processor; and at least one memory storing computer executable instructions, wherein the computer executable instructions, when executed by the at least one processor, prompt the at least one processor to execute a power verification method for a wind turbine generator set according to an exemplary embodiment of the present disclosure.

[0018] According to the verification method and computer equipment of the wind turbine generator set disclosed in the present invention, the wind speed relationship between the measured wind speed and the estimated wind speed can be determined based on the estimated wind speed data at the location of the wind turbine generator set and the measured wind speed data at the wind turbine generator set. Therefore, based on the wind speed relationship, the target wind speed data measured at the wind turbine generator set can be corrected, and the power characteristics of the wind turbine generator set can be verified based on the corrected wind speed data. In this way, the problem of errors in the measured wind speed data can be improved, so that the corrected wind speed data is closer to the actual wind speed situation, thereby improving the accuracy of the verification of the power characteristics of the unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Detailed description is given of a flow chart of a power verification method for a wind turbine generator system according to an exemplary embodiment of the present disclosure.

[0020] Figure 2 The present invention is a flowchart showing the steps of obtaining estimated wind speed data in a power verification method of a wind turbine generator set according to an exemplary embodiment of the present disclosure.

[0021] Figure 3 is a schematic diagram showing an example distribution of wind turbines in a wind farm in a power verification method of wind turbines according to an exemplary embodiment of the present disclosure.

[0022] Figure 4 is a schematic diagram showing an example of a wind spectrum diagram of a wind farm in a power verification method of a wind turbine generator system according to an exemplary embodiment of the present disclosure.

[0023] Figure 5 It is a flowchart diagram showing the steps of removing wake interference data in a power verification method of a wind turbine generator set according to an exemplary embodiment of the present disclosure.

[0024] Figure 6 1 is a flow chart showing steps of determining a wind speed relationship in a power verification method of a wind turbine generator system according to an exemplary embodiment of the present disclosure.

[0025] Figure 7 1 is a flow chart showing steps of determining a target time length in a power verification method of a wind turbine generator system according to an exemplary embodiment of the present disclosure.

[0026] Figure 8is a schematic diagram showing an example curve of a wind speed relationship in a power verification method of a wind turbine generator system according to an exemplary embodiment of the present disclosure.

[0027] Fig. 9 1 is a flow chart showing steps of verifying power characteristics of a wind turbine generator set in a power verification method of a wind turbine generator set according to an exemplary embodiment of the present disclosure.

[0028] Fig.10 and Fig.11 Schematic diagrams showing a power curve of a wind turbine generator set that does not adopt the power verification method according to an exemplary embodiment of the present disclosure and a power curve of a wind turbine generator set that adopts the power verification method, respectively.

[0029] Fig.12 1 and 2 are schematic diagrams showing comparison of power curves of a wind turbine generator set not adopting the power verification method according to an exemplary embodiment of the present disclosure and a wind turbine generator set adopting the power verification method. DETAILED DESCRIPTION

[0030] The following specific embodiments are provided to help the reader obtain a comprehensive understanding of the methods, devices and / or systems described herein. However, after understanding the disclosure of the present application, various changes, modifications and equivalents of the methods, devices and / or systems described herein will be clear. For example, the order of operations described herein is only an example and is not limited to those orders set forth herein, but can be changed as will be clear after understanding the disclosure of the present application, except for operations that must occur in a specific order. In addition, for greater clarity and simplicity, the description of features known in the art may be omitted.

[0031] The features described herein can be implemented in different forms and should not be construed as being limited to the examples described herein. Rather, the examples described herein have been provided to illustrate only some of the many possible ways to implement the methods, devices, and / or systems described herein, which will be clear after understanding the disclosure of the present application.

[0032] As used herein, the term "and / or" includes any one of the associated listed items and any combination of any two or more.

[0033] Although terms such as "first", "second", and "third" may be used herein to describe various members, components, regions, layers, or portions, these members, components, regions, layers, or portions should not be limited by these terms. Instead, these terms are only used to distinguish one member, component, region, layer, or portion from another member, component, region, layer, or portion. Therefore, without departing from the teachings of the examples described herein, the first member, first component, first region, first layer, or first portion referred to in the examples may also be referred to as the second member, second component, second region, second layer, or second portion.

[0034] In the specification, when an element (such as a layer, a region, or a substrate) is described as being “on”, “connected to”, or “coupled to” another element, the element may be directly “on”, “connected to”, or “coupled to” another element, or one or more other elements may be present therebetween. Conversely, when an element is described as being “directly on”, “directly connected to”, or “directly coupled to” another element, there may be no other elements present therebetween.

[0035] The terms used herein are only used to describe various examples and are not intended to limit the disclosure. Unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. The terms "comprise", "include" and "have" indicate the presence of the described features, quantities, operations, components, elements and / or combinations thereof, but do not exclude the presence or addition of one or more other features, quantities, operations, components, elements and / or combinations thereof.

[0036] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by a person of ordinary skill in the art to which the present disclosure belongs after understanding the present disclosure. Unless explicitly defined as such herein, terms (such as those defined in a general dictionary) should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and should not be interpreted in an idealized or overly formal manner.

[0037] Furthermore, in the description of examples, when it is considered that a detailed description of a well-known related structure or function would cause vague interpretation of the present disclosure, such a detailed description will be omitted.

[0038] As mentioned above, in the related art, the wind speed data used for verifying the power characteristics of a wind turbine generator set has errors, making it difficult to ensure the accuracy of the verification results.

[0039] As an example, the power characteristics of a wind turbine generator set can be evaluated through a power curve. As an important parameter for evaluating the performance of a wind turbine generator set, the power curve can reflect the power generated by the wind turbine generator set at different wind speeds and rotation speeds.

[0040] In actual engineering, the measured wind speed at the tail of the nacelle is easily affected by the wake of the wind rotor, and there may be certain errors, and the anemometer at the tail of the nacelle may also deviate.

[0041] In some cases, in order to overcome these problems, other wind measurement methods can also be used, such as terrain tower wind measurement or laser radar wind measurement, which can provide more accurate wind speed data and help improve the accuracy of power curve evaluation and verification. In addition, statistical analysis methods can be used to analyze and evaluate power curves to determine their accuracy and reliability.

[0042] However, for wind turbines that are already in operation, it is difficult to install lidars in batches and erect towers to measure wind for each unit. Therefore, the method of installing lidars or erecting wind towers to obtain the transfer function between the true wind speed and the nacelle anemometer is costly, time-consuming, and inefficient.

[0043] In view of the above problems, the present disclosure provides a power verification method and a computer device for a wind turbine generator set to solve or at least alleviate the above problems.

[0044] According to a first aspect of an exemplary embodiment of the present disclosure, a power verification method for a wind turbine generator set is provided. The power verification method can be performed by a computer device with computing capabilities, and the computer device can be, for example, a terminal device or a server, wherein the terminal device can be, for example, a tablet computer, a laptop computer, a digital assistant, etc.; the server can be an independent server, a server cluster, a cloud computing platform, or a virtualization center.

[0045] In an example application scenario, the computer device can obtain the estimated wind speed data at the location of the wind turbine generator set based on the wind resource data of the wind farm where the wind turbine generator set to be verified is located, and determine the wind speed relationship between the measured wind speed at the wind turbine generator set and the estimated wind speed based on the estimated wind speed data and the measured wind speed data at the wind turbine generator set. The computer device can also correct the target wind speed data measured at the wind turbine generator set based on the wind speed relationship to obtain the corrected wind speed data, and verify the power characteristics of the wind turbine generator set based on the corrected wind speed data.

[0046] Here, the computer equipment can be set up at a wind turbine or wind farm, for example, and can be communicatively connected to a measuring device, control system or data center of the wind turbine or wind farm, so as to obtain the unit operation data, wind speed data, etc. required to execute the above method.

[0047] According to the power verification method of the wind turbine generator set disclosed in the present invention, the measured wind speed data can be corrected by determining the wind speed relationship between the measured wind speed and the estimated wind speed at the wind turbine generator set, thereby improving the problem of errors or deviations in the measured wind speed data, making the corrected wind speed data closer to the actual wind speed situation, thereby improving the accuracy of the power characteristic verification of the unit.

[0048] The power verification method of the wind turbine generator set according to the present disclosure may include the following steps:

[0049] like Figure 1 As shown, in step S110, the estimated wind speed data at the location where the wind turbine generator set is located can be obtained based on the wind resource data of the wind farm where the wind turbine generator set to be verified is located.

[0050] In this step, the wind resource data may be, for example, data measured by a wind measuring device, or data processed or analyzed based on the measured data. As an example, measurement data within a predetermined time period (e.g., the last year) may be derived from an anemometer of a wind tower used for wind power prediction in a wind farm. The measurement data may include, for example, information such as wind speed, wind direction, and measurement height. The measurement data may be statistically processed, for example, the average value may be calculated based on, for example, 1 minute (min), to obtain the final wind resource data.

[0051] The estimated wind speed data may be, for example, wind speed data at the wind turbine generator set to be verified, calculated or analyzed based on the wind resource data of the wind farm. A plurality of wind turbine generator sets (e.g. Figure 3 As shown), the wind turbine generator set to be verified can be any one of them, and the wind field conditions of the wind turbine generator set to be verified can be determined based on the overall wind field conditions of the wind farm. As an example, in this step S110, the estimated wind speed data can be obtained in the following manner:

[0052] like Figure 2 As shown, in step S210, flow field simulation can be performed based on wind resource data to obtain a flow field model of the wind farm.

[0053] As an example, based on wind resource data, computational fluid dynamics (CFD) simulation methods can be used to solve the Navier-Stokes equations in the wind farm area to obtain a flow field model in the area of ​​interest. The flow field model can reflect the wind field conditions in the area of ​​interest of the wind farm.

[0054] Although the example process of obtaining the flow field model is described here by taking the CFD simulation method as an example, it is not limited to this and can also be implemented by other simulation methods. As long as the flow field model can be obtained, the present disclosure does not impose any special restrictions on this.

[0055] In step S220, wind farm wind speed data of the wind farm may be determined based on the flow field model.

[0056] In this step, when the flow field model is determined, the overall estimated data of the wind farm at any time can be obtained by substituting it into the grid of the wind farm area, such as the wind spectrum in the wind farm (for example, Figure 4 As shown in the figure), average wind speed and power generation at corresponding moments at different machine locations, etc.

[0057] In step S230, data related to wind turbine generator sets in the wind speed data of the wind farm may be used as estimated wind speed data.

[0058] In this step, wind speed data related to the unit to be verified may be extracted from the wind speed data of the wind farm to serve as the above-mentioned estimated wind speed data.

[0059] Through the above method, the estimated wind speed data of a specific unit can be obtained from the estimated data of the wind farm as a whole. Subsequent wind speed correction based on such estimated wind speed data can make the corrected wind speed data more consistent with the overall wind resource conditions of the wind farm, and therefore closer to the actual wind speed.

[0060] In addition, as an example, according to an exemplary embodiment of the present disclosure, the power verification method may further include the following steps: Figure 5 As shown, in step S510, wake interference data in wind resource data may be eliminated based on the layout of wind turbines in the wind farm; and in step S520, estimated wind speed data may be determined based on the eliminated wind resource data.

[0061] In the above steps, the wind resource data in the corresponding sector can be corrected based on the wind turbine wake model such as the Park wake model and with reference to the layout position of the wind turbine generator set in the wind farm, so that the interference of the wake can be removed from the wind resource data. For example, based on the location of the wind turbine generator set around the wind tower, it can be determined whether there is a wind turbine generator set within a preset distance range from the wind tower. If there is a wind turbine generator set, the data of the location of the wind turbine generator set can be removed from the wind resource data. Here, the preset distance range can be, for example, less than 5 times the diameter of the turbine rotor.

[0062] The above steps may be performed, for example, before performing step S210, so as to eliminate the interference of the wind turbine wake near the wind tower, making the estimated wind speed data finally obtained more accurate.

[0063] In step S120, a wind speed relationship between the measured wind speed at the wind turbine generator set and the estimated wind speed may be determined based on the estimated wind speed data and the measured wind speed data at the wind turbine generator set.

[0064] In this step, the measured wind speed data may be data measured at the unit by a wind measuring device, for example. By analyzing and comparing the estimated wind speed data and the measured wind speed data, a potential relationship between the two may be obtained, which is used to correct the subsequently measured wind speed data.

[0065] As an example, in step S120, the wind speed relationship may be determined in the following manner:

[0066] like Figure 6 As shown, in step S610, the estimated wind speed data and the measured wind speed data may be counted according to multiple time lengths to obtain a wind speed statistical data set.

[0067] Here, the wind speed statistical data set may include a plurality of wind speed statistical data corresponding one-to-one to a plurality of time lengths.

[0068] As an example, the wind speed statistical data includes first statistical data corresponding to the estimated wind speed data and second statistical data corresponding to the measured wind speed data. Accordingly, the wind speed statistical data set may include a wind speed statistical data set of the first statistical data and a wind speed statistical data set of the second statistical data.

[0069] For example, in step S610, the first statistical data can be determined in the following manner: based on the estimated wind speed data, according to each time length in multiple time lengths, multiple first wind speed average values ​​corresponding to each time length are determined; based on the multiple first wind speed average values ​​in random order, a wind speed statistical data set including the first statistical data is obtained.

[0070] Here, the estimated wind speed data may be data obtained based on wind speed data at a height closest to the hub height of the wind turbine generator set, and the wind speed data may be obtained by, for example, measuring a wind tower in a wind farm. The multiple time lengths may include, for example, but are not limited to, 10 minutes, 60 minutes, 120 minutes, and 240 minutes.

[0071] As an example, the wind tower of a wind farm can measure wind speed data at different storey heights. The estimated wind speed data can be estimated based on the measured data of the storey height closest to the hub height of the unit to be verified. The example method of estimation has been referenced above. Figure 2 Described in detail.

[0072] Multiple time lengths can be set according to actual needs, and each time length can correspond to multiple average wind speed data points (for example, at least 200 data points). For example, the time length can be used as a time window, and multiple first wind speed average values ​​corresponding to the time length can be calculated based on the time series wind speed data in a sliding window manner. For each time length, after obtaining multiple first wind speed average values, the arrangement order of these first wind speed average values ​​can be randomized (for example, the data order of these first wind speed average values ​​is randomly exchanged) to disrupt the trend characteristics of these first wind speed average values.

[0073] As an example, through the above processing, we can obtain data such as 10-minute average wind speed, 60-minute average wind speed, 120-minute average wind speed, 240-minute average wind speed, etc., and each time length has at least 200 data points of average wind speed, and these data points are randomly sorted.

[0074] Similarly, in step S610, the second statistical data can be determined in the following manner: based on the measured wind speed data, according to each time length in multiple time lengths, multiple second wind speed average values ​​corresponding to each time length are determined; based on the multiple second wind speed average values ​​in random order, a wind speed statistical data set including the second statistical data is obtained.

[0075] Here, the measured wind speed data may be, for example, wind speed data measured at a nacelle of a wind turbine generator set.

[0076] As described above, multiple time lengths can be set according to actual needs, including but not limited to 10 minutes, 60 minutes, 120 minutes and 240 minutes, and each time length can correspond to multiple average wind speed data points (for example, at least 200 data points). For example, the time length can be used as a time window, and multiple first wind speed average values ​​corresponding to the time length can be calculated based on the time series wind speed data in a sliding window manner. For each time length, after obtaining multiple second wind speed average values, the arrangement order of these second wind speed average values ​​can be randomized (for example, the data order of these second wind speed average values ​​is randomly exchanged) to disrupt the trend characteristics of these second wind speed average values.

[0077] As an example, through the above processing, we can obtain data such as 10-minute average wind speed, 60-minute average wind speed, 120-minute average wind speed, 240-minute average wind speed, etc., and each time length has at least 200 data points of average wind speed, and these data points are randomly sorted.

[0078] In the above process, by randomly exchanging the data order of statistical data (including first statistical data and second statistical data) corresponding to multiple time lengths, the randomness of the data in the wind speed statistical data set can be improved, which can be beneficial to subsequent random analysis to determine the target time length for which the data is more representative.

[0079] In step S620, a target time length among multiple time lengths may be determined based on the wind speed statistical data set.

[0080] Here, the wind speed statistics data corresponding to the target time length can reflect the data set characteristics of the wind speed statistics data set. Specifically, the purpose of determining the target time length is to find the time length in the wind speed statistics data that can represent the overall characteristics of the data set. The wind speed relationship established based on the target time length is more universal and general, and can be applied to the relationship between measured data and estimated wind speeds of other time lengths.

[0081] As an example, the wind speed statistical data may include first statistical data corresponding to the estimated wind speed data and second statistical data corresponding to the measured wind speed data. In this example, in step S620, the target time length may be determined in the following manner:

[0082] like Figure 7 As shown, in step S710, data feature analysis may be performed on the first statistical data and the second statistical data for each of the multiple time lengths to obtain a first data feature value of the first statistical data and a second data feature value of the second statistical data.

[0083] As an example, in step S710, data feature analysis can be performed on the first statistical data and the second statistical data by the following method: data randomness analysis is performed on the first statistical data and the second statistical data respectively, the Hurst coefficient corresponding to the first statistical data is used as the first data feature value, and the Hurst coefficient corresponding to the second statistical data is used as the second data feature value.

[0084] Here, the data randomness analysis may be, for example, a rescaled range analysis (R / S analysis) (also referred to as “cumulative deviation analysis”). Specifically, the R / S analysis may be performed in the following manner:

[0085] For each time length, the first data sequence of the first statistical data and the second data sequence of the second statistical data can be divided into subsequences of different lengths.

[0086] Specifically, the first data sequence and the second data sequence corresponding to each time length can be divided into subsequences of length N, respectively, where N is a positive integer greater than 1. For example, in order to facilitate data processing and calculation, the length of the subsequence can be a power of 2, for example, N can be 8, 16, 32, 64, 128, 256, etc. In this case, for multiple first statistical data (or second statistical data) at any time length, 8 data can be divided into the first subsequence, the next 16 data can be divided into the second subsequence, the next 32 data can be divided into the third subsequence, etc., according to the order of data in the first data sequence (or the second data sequence), until all data in the first data sequence (or the second data sequence) are divided.

[0087] A cumulative deviation sequence (also referred to as an R / S sequence) may be calculated for each subsequence.

[0088] Specifically, the mean of each subsequence can be calculated based on the following formula (1):

[0089] M = (X(1) + X(2) + ... + X(N)) / N (1)

[0090] Wherein, M represents the mean of the subsequence, X(i) represents the i-th data in the subsequence, where i=1, 2, ..., N, and N represents the length of the subsequence (ie, the number of data contained in the subsequence).

[0091] Based on the above formula (1), the deviation sequence of each subsequence can be calculated based on the following formula (2):

[0092] D(i) = X(i) – M (2)

[0093] Wherein, D(i) represents the deviation sequence of the i-th data in the subsequence.

[0094] Based on the above equations (1) and (2), the cumulative deviation sequence (i.e., R / S sequence) of each subsequence can be calculated based on the following equations (3) and (4):

[0095] R(i) = D(1) + D(2) + ... + D(i) (3)

[0096] S(i) = max(R(1), R(2), ..., R(i)) - min(R(1), R(2), ..., R(i)) (4)

[0097] The cumulative deviation sequence of the subsequence can be expressed as R(i) / S(i).

[0098] Based on the above formulas (1) to (4), the R / S ratio of each subsequence can be obtained by statistics. Based on the R / S ratio, a log-log (R / S) graph between the subsequence length and the corresponding deviation and the extreme value of the sequence can be drawn, so that the Hurst coefficient can be estimated according to the slope in the log-log (R / S) graph as the data characteristic value of the corresponding statistical data. For example, a log-log (R / S) graph can be drawn based on the R(i) / S(i) sequence of each subsequence, and the slope of the log-log (R / S) graph can be determined as the Hurst coefficient.

[0099] As an example, the Hurst coefficients of the first statistical data and the second statistical data at various time lengths may be shown in the following Table 1:

[0100] Table 1

[0101]

[0102] In step S720, the time length during which the operation of the first data characteristic value and the second data characteristic value meets the preset condition may be used as the target time length.

[0103] In this step, the preset conditions can be used to screen the time lengths. For example, the time lengths with greater randomness of the first data characteristic value and the second data characteristic value can be screened out as the target time length.

[0104] As an example, the preset condition may be that the sum of the first data characteristic value and the second data characteristic value is maximum. In this example, the time length when the sum of the first data characteristic value and the second data characteristic value is maximum may be used as the target time length.

[0105] Based on the results of randomness analysis, the larger the first data feature value and the larger the second data feature value, the stronger the randomness of the event, and the more universal the features of the corresponding data set are, and the more suitable they are for application to other data sets.

[0106] Taking Table 1 above as an example, among the data of the four sampling frequencies (or time lengths) of 10min, 60min, 120min and 240min, since the maximum value of the Hurst coefficient can be 0.5, the sampling frequency with the sum of the Hurst coefficient of the wind tower and the Hurst coefficient of the unit closest to 1 can be selected as the target time length, that is, 240min can be selected as the target time length.

[0107] Through the above Figure 7The method shown can find the most representative and universal target time length for wind speed statistical data in each time length, so that the subsequent wind speed relationship can be established based on this time length. By finding the target time length, the subsequent wind speed relationship can be more widely consistent with the measured wind speed and the estimated wind speed measured at different times, so that the wind speed correction is more accurate and closer to the actual wind speed situation.

[0108] In step S630, a wind speed relationship may be determined based on wind speed statistical data corresponding to the target time length.

[0109] In this step, when the target time length is determined, a wind speed relationship between the estimated wind speed data and the measured wind speed data can be constructed based on the wind speed statistical data corresponding to the target time length.

[0110] As an example, the wind speed relationship can be expressed as a function of the relationship between the measured wind speed and the estimated wind speed at the wind turbine generator set. The wind speed relationship between the estimated wind speed data and the measured wind speed data under the target time length can be obtained by data fitting. For example, taking the wind speed data under 240 minutes in the example corresponding to Table 1 above as an example, the following can be obtained: Figure 8 The wind speed fitting curve shown is used to obtain the wind speed relationship.

[0111] As an example, a fitting method such as the least squares method may be used to fit the wind speed relationship between the measured wind speed and the estimated wind speed, which may be in the form of, for example, the following formula (5):

[0112] V CDF =a×V 机舱 +b (5)

[0113] Among them, V CDF Represents the estimated wind speed, V 机舱 represents the measured wind speed, and a and b represent the fitting coefficients.

[0114] The wind speed relationship determined in the above manner can be used as a general relationship and applied to subsequent measured wind speed data, so that when the measured wind speed data is obtained later, it can be quickly corrected to make the corrected wind speed data more accurate and reduce measurement deviation.

[0115] Although the above describes an example of obtaining a wind speed relationship by taking linear fitting as an example, it is not limited thereto, and other fitting methods and fitting forms may also be used. In addition, although the above describes that the wind speed relationship is expressed as a relationship function, it is not limited thereto, and the wind speed relationship may also be expressed in the form of a data mapping table, for example.

[0116] Return to reference Figure 1In step S130, the target wind speed data measured at the wind turbine generator set may be corrected based on the wind speed relationship to obtain corrected wind speed data.

[0117] When the wind speed relationship is determined, the measured target wind speed data can be substituted into the wind speed relationship to obtain the estimated wind speed, so that the estimated wind speed can be used as the corrected wind speed.

[0118] For example, the measured wind speed data at 10 min in the example corresponding to Table 1 above can be substituted into the wind speed relationship shown in the above formula (5) to obtain the estimated wind speed data, that is, the corrected wind speed data, so as to correct the measurement data of the nacelle anemometer.

[0119] In step S140, the power characteristics of the wind turbine generator set may be verified based on the corrected wind speed data.

[0120] After obtaining the corrected wind speed data, any power characteristic verification method can be used to verify the power characteristics based on the wind speed data. As an example, in step S140, the power characteristics of the wind turbine generator set can be verified in the following manner:

[0121] like Fig. 9 As shown, in step S910, target power data corresponding to the target wind speed data may be acquired.

[0122] For example, the output power data corresponding to the time of the target wind speed data may be extracted from the operation data of the wind turbine generator set (eg SCADA data) as the target power data.

[0123] In step S920, a power curve of the wind turbine generator set may be obtained based on the corresponding relationship between the target power data and the corrected wind speed data to verify the power characteristics of the wind turbine generator set.

[0124] Since the corrected wind speed data is obtained by correcting the target wind speed data, a corrected power curve can be drawn based on the target power data and the corrected wind speed data to verify the power characteristics.

[0125] As an example, the original power curve obtained based on the target power data and the target wind speed data can be as follows: Fig.10 As shown, the modified power curve obtained based on the target power data and the modified wind speed data can be as follows Fig.11 In addition, Fig.12 In the figure, the grey scattered points are the power data before correction (ie, the target power data), and the red scattered points are the power data after correction.

[0126] It can be seen that since the target wind speed data has been corrected, the corrected power curve is more accurate than the original power curve and is more consistent with the actual output power curve of the unit, thereby obtaining a more accurate power characteristic verification result.

[0127] In addition, as an example, the power verification method according to an embodiment of the present disclosure may also include: comparing the verified power characteristics with the preset standard power characteristics to obtain a power label value, wherein the power label value can characterize the difference between the verified power characteristics and the standard power characteristics.

[0128] Here, the power characteristic may be represented by the power generation of the power curve, and the power generation of the power curve may be calculated by, for example, the area under the curve.

[0129] Specifically, a predetermined reference power curve can be obtained, and the reference power curve power generation AEP can be calculated based on the corrected wind speed data within a predetermined time period. 0 Similarly, the actual power curve power generation AEP can be calculated based on the modified power curve and the modified wind speed data in the same time period. v Here, the reference power curve may be, for example, a standard power curve determined during the unit design process.

[0130] Based on the above reference power curve, the power generation AEP 0 and actual power curve AEP v , we can get the label value of the power curve K = AEP v / AEP 0 ×100%.

[0131] Although an example method for calculating the label value of the power curve is given here, it is not limited thereto, and the label value may also be represented in other forms, such as a difference value.

[0132] In addition, as described above, the wind speed relationship is expressed as a relationship function between the measured wind speed and the estimated wind speed at the wind turbine generator set. In this example, the power verification method may also include: detecting the operating status of the wind turbine generator set based on the power label value and the function coefficient of the relationship function.

[0133] As an example, the operating status of the wind turbine generator set may be detected by at least one of the following methods:

[0134] In response to the power tag value satisfying the first preset condition and the function coefficient satisfying the second preset condition, it is determined that the power characteristic of the wind turbine generator set is normal and the wind measuring device of the wind turbine generator set operates normally.

[0135] Here, the first preset condition may indicate that the difference between the verified actual power characteristic and the standard power characteristic is within a normal range, and satisfying the first preset condition may indicate that the difference between the actual power characteristic and the standard power characteristic is not too large, and the output power of the unit is within a normal range; not satisfying the first preset condition may indicate that the difference between the actual power characteristic and the standard power characteristic is large, and the output power of the unit is within an abnormal range. The second preset condition may indicate that the difference between the actual wind speed and the estimated wind speed is within a normal range, and satisfying the second preset condition may indicate that the difference between the actual wind speed and the estimated wind speed is not too large, and the measured actual wind speed is relatively accurate; not satisfying the first preset condition may indicate that the difference between the actual wind speed and the estimated wind speed is large, and the measured actual wind speed is inaccurate. As an example, the first preset condition may be that the power label value is between 95% and 105%, and the second estimated condition may be that the a value in formula (4) is between 0.95 and 1.05.

[0136] For example, when the power tag value is between 95% and 105% and the a value in formula (4) is between 0.95 and 1.05, it can be diagnosed that the power curve of the unit is normal and the anemometer is normal.

[0137] In response to the power tag value not satisfying the first preset condition and the function coefficient not satisfying the second preset condition, it is determined that the power characteristic of the wind turbine generator set is abnormal and the wind measuring device of the wind turbine generator set operates abnormally.

[0138] For example, when the power tag value is not within the range of 95%-105% and the a value in equation (4) is not within the range of 0.95-1.05, it can be diagnosed that the power characteristics of the unit cabin are abnormal and the wind measuring equipment may have a large deviation.

[0139] In response to the power tag value satisfying the first preset condition and the function coefficient not satisfying the second preset condition, it is determined that the wind measuring device of the wind turbine generator set operates normally.

[0140] For example, when the power tag value is within the range of 95%-105% and the a value in equation (4) is not within the range of 0.95-1.05, it can be diagnosed that there may be problems with the unit wind deviation, the nacelle anemometer, and the wind vane.

[0141] In response to the power tag value not satisfying the first preset condition and the function coefficient satisfying the second preset condition, it is determined that the power generation equipment of the wind turbine generator set is operating abnormally.

[0142] For example, when the power tag value is not within the range of 95%-105% and the a value in formula (4) is within the range of 0.95-1.05, it can be diagnosed that the unit has abnormal power limitation, abnormal converter power conversion, abnormal generator equipment, etc.

[0143] In the above manner, an abnormality of equipment in the unit can be warned through a relationship function between the power label value and the wind speed on the basis of the corrected power characteristics, so that the faulty equipment can be maintained in time.

[0144] According to the power verification method of the wind turbine generator set of the exemplary embodiment of the present disclosure, the measurement data of the nacelle anemometer of the wind turbine generator set can be corrected in combination with simulation technology such as CFD technology, and then the power characteristics are evaluated. After that, the wind turbine generator set can be classified and diagnosed according to the evaluation parameters.

[0145] In this method, a variety of means can be used to obtain more accurate wind speed data. On the one hand, wind speed data can be obtained through a wind tower as a reference standard; on the other hand, the wind speed can be measured using a cabin anemometer in combination with the simulated flow field, and the data can be corrected to eliminate errors caused by factors such as the wind rotor wake. In this way, the power characteristics of the wind turbine can be evaluated, and abnormal equipment can be found and early warnings can be issued.

[0146] The power verification method of a wind turbine generator set according to an embodiment of the present disclosure can be written as a computer program and stored on a computer-readable storage medium. When the instructions in the computer-readable storage medium are executed by at least one processor, the at least one processor is prompted to perform the power verification method of the wind turbine generator set according to the exemplary embodiment of the present disclosure. Examples of computer-readable storage media include: read-only memory (ROM), random access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or optical disk storage, hard disk drive (HDD), solid state drive (SSD), card storage (such as, multimedia card, secure digital (SD) card or extreme digital (XD) card), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk and any other device, any other device is configured to store computer program and any associated data, data files and data structures in a non-transitory manner and provide the computer program and any associated data, data files and data structures to a processor or computer so that the processor or computer can execute the computer program. In one example, the computer program and any associated data, data files and data structures are distributed on a networked computer system so that the computer program and any associated data, data files and data structures are stored, accessed and executed in a distributed manner by one or more processors or computers.

[0147] The power verification method for a wind turbine generator set according to an embodiment of the present disclosure can be executed by a computer device, which includes: at least one processor; at least one memory storing computer executable instructions, wherein the computer executable instructions, when executed by at least one processor, prompt the at least one processor to execute the power verification method for a wind turbine generator set according to the exemplary embodiment of the present disclosure.

[0148] As an example, the computer device can be a PC, a tablet device, a personal digital assistant, a smart phone, or other device capable of executing the above-mentioned instruction set. Here, the computer device is not necessarily a single electronic device, but can also be any device or circuit that can execute the above-mentioned instructions (or instruction sets) individually or jointly. The computer device can also be part of an integrated control system or system manager, or can be configured as a portable electronic device that is interconnected with a local or remote (e.g., via wireless transmission) interface.

[0149] In a computer device, a processor may include a central processing unit (CPU), a graphics processing unit (GPU), a programmable logic device, a dedicated processor system, a microcontroller or a microprocessor. As an example and not limitation, a processor may also include an analog processor, a digital processor, a microprocessor, a multi-core processor, a processor array, a network processor, etc.

[0150] The processor can execute instructions or codes stored in the memory, wherein the memory can also store data. Instructions and data can also be sent and received through the network via the network interface device, wherein the network interface device can adopt any known transmission protocol.

[0151] The memory may be integrated with the processor, for example, RAM or flash memory is arranged within an integrated circuit microprocessor or the like. In addition, the memory may include a separate device, such as an external disk drive, a storage array, or any other storage device that can be used by a database system. The memory and the processor may be operatively coupled, or may communicate with each other, such as through an I / O port, a network connection, etc., so that the processor can read files stored in the memory.

[0152] In addition, the computer device may also include a video display (such as a liquid crystal display) and a user interaction interface (such as a keyboard, a mouse, a touch input device, etc.) All components of the computer device may be connected to each other via a bus and / or a network.

[0153] The specific implementation methods of the present disclosure have been described in detail above. Although some embodiments have been shown and described, those skilled in the art should understand that these embodiments may be modified and varied without departing from the principles and spirit of the present disclosure, the scope of which is defined by the claims and their equivalents. These modifications and variations should also be within the scope of protection of the claims of the present disclosure.

Claims

1. A power verification method for a wind turbine generator set, It is characterized in that The power verification method comprises: Based on the wind resource data of the wind farm where the wind turbine generator set to be verified is located, obtaining the estimated wind speed data at the location where the wind turbine generator set is located; Determine a wind speed relationship between the measured wind speed at the wind turbine generator set and the estimated wind speed based on the estimated wind speed data and the measured wind speed data at the wind turbine generator set; Based on the wind speed relationship, correcting the target wind speed data actually measured at the wind turbine generator set to obtain corrected wind speed data; Based on the corrected wind speed data, the power characteristics of the wind turbine generator set are verified.

2. The power verification method according to claim 1, It is characterized in that The wind speed relationship is determined by: According to multiple time lengths, the estimated wind speed data and the measured wind speed data are counted to obtain a wind speed statistical data set, wherein the wind speed statistical data set includes multiple wind speed statistical data corresponding to the multiple time lengths one by one; Based on the wind speed statistical data set, determining a target time length among the multiple time lengths, wherein the wind speed statistical data corresponding to the target time length can reflect the data set characteristics of the wind speed statistical data set; The wind speed relationship is determined based on wind speed statistical data corresponding to the target time length.

3. The power verification method according to claim 2, It is characterized in that The wind speed statistical data includes first statistical data corresponding to the estimated wind speed data and second statistical data corresponding to the measured wind speed data, wherein the target time length is determined by: For each time length of the multiple time lengths, respectively perform data feature analysis on the first statistical data and the second statistical data to obtain a first data feature value of the first statistical data and a second data feature value of the second statistical data; The time length during which the operation of the first data characteristic value and the second data characteristic value meets a preset condition is used as the target time length.

4. The power verification method according to claim 3, It is characterized in that The first statistical data is determined by: Based on the estimated wind speed data, according to each of the multiple time lengths, determine a plurality of first wind speed average values ​​corresponding to each time length, wherein the estimated wind speed data is data obtained based on wind speed data of a height closest to the hub height of the wind turbine generator set; According to the plurality of first wind speed average values ​​in random order, a wind speed statistical data set including the first statistical data is obtained, The second statistical data is determined by: Based on the measured wind speed data, according to each time length of the multiple time lengths, determine a plurality of second wind speed average values ​​corresponding to each time length, wherein the measured wind speed data is wind speed data measured at the nacelle of the wind turbine generator set; A wind speed statistical data set including the second statistical data is obtained according to the plurality of second wind speed average values ​​in random order.

5. The power verification method according to claim 3, It is characterized in that The data characteristics of the first statistical data and the second statistical data are analyzed by the following method: Data randomness analysis is performed on the first statistical data and the second statistical data respectively, and the Hurst coefficient corresponding to the first statistical data is used as the first data characteristic value, and the Hurst coefficient corresponding to the second statistical data is used as the second data characteristic value.

6. The power verification method according to any one of claims 1 to 5, It is characterized in that The power characteristics of the wind turbine generator set are verified by: Acquiring target power data corresponding to the target wind speed data; Based on the corresponding relationship between the target power data and the corrected wind speed data, a power curve of the wind generator set is obtained to verify the power characteristics of the wind generator set.

7. The power verification method according to any one of claims 1 to 5, It is characterized in that The power verification method further includes: Eliminating wake interference data in the wind resource data based on the layout positions of the wind turbines in the wind farm; The estimated wind speed data is determined based on the eliminated wind resource data.

8. The power verification method according to any one of claims 1 to 5, It is characterized in that The estimated wind speed data is obtained by: Performing flow field simulation based on the wind resource data to obtain a flow field model of the wind farm; Based on the flow field model, determining wind farm wind speed data of the wind farm; The data related to the wind turbine generator set in the wind farm wind speed data is used as the estimated wind speed data.

9. The power verification method according to any one of claims 1 to 5, It is characterized in that The power verification method further includes: The verified power characteristic is compared with a preset standard power characteristic to obtain a power label value, wherein the power label value represents the difference between the verified power characteristic and the standard power characteristic.

10. The power verification method according to claim 9, It is characterized in that The wind speed relationship is expressed as a relationship function between the measured wind speed and the estimated wind speed at the wind turbine generator set, wherein the power verification method further includes: Based on the power tag value and the function coefficient of the relationship function, the operating state of the wind turbine generator set is detected.

11. The power verification method according to claim 10, It is characterized in that The operating status of the wind turbine generator set is detected by at least one of the following methods: In response to the power tag value satisfying a first preset condition and the function coefficient satisfying a second preset condition, determining that the power characteristic of the wind turbine generator set is normal and the wind measuring device of the wind turbine generator set is operating normally; In response to the power tag value not satisfying the first preset condition and the function coefficient not satisfying the second preset condition, determining that the power characteristic of the wind turbine generator set is abnormal and the wind measuring device of the wind turbine generator set is operating abnormally; In response to the power tag value satisfying a first preset condition and the function coefficient not satisfying a second preset condition, determining that the wind measuring device of the wind turbine generator set operates normally; In response to the power tag value not satisfying a first preset condition and the function coefficient satisfying a second preset condition, it is determined that the power generation equipment of the wind turbine generator set is operating abnormally.

12. A computer device, It is characterized in that include: at least one processor; at least one memory storing computer executable instructions, Wherein, when the computer executable instructions are executed by the at least one processor, the at least one processor is prompted to execute the power verification method of the wind turbine generator set according to any one of claims 1-11.

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