Method, System, Electronic Device and Storage Medium for Evaluating Wake Loss of Wind Turbine Generator

Abstract

The present invention relates to the technical field of wind power generation, and in particular provides a method, system, electronic device and storage medium for evaluating wake loss of a wind turbine generator set, including the following steps: obtaining the time-series data of wind conditions, the layout data of wind turbines and the SCADA data of a wind farm; constructing a wake velocity loss evaluation model and correcting the wake velocity loss evaluation model based on the time-series data of wind conditions; using the corrected wake velocity loss evaluation model to output the evaluation result of wake velocity loss; constructing a wake power loss evaluation model and outputting the evaluation result of wake power loss based on the SCADA data; and outputting the wake loss evaluation result based on the evaluation results of velocity loss and power loss. The purpose is to quantify the loss caused by the wake effect in a wind farm under the temporal variation of wind conditions, and provide credible guiding suggestions for optimizing the layout of the wind farm and reducing the loss of the wake effect.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular, to a method, a system, an electronic device and a storage medium for evaluating wake losses of a wind turbine generator set. Background Art

[0002] In the field of wind power generation, the wake effect between wind turbines is a key research hotspot. The wake effect refers to the region where the wind speed decreases formed downstream when the upstream wind turbine extracts energy from the wind, resulting in the downstream wind turbine operating under the influence of a wind speed lower than the free flow wind speed, thereby causing serious wake losses. Research shows that wake losses may reduce the efficiency of a wind farm by about 10%. In addition, the change in the wind speed in front of the downstream wind turbine will also affect the characteristics of the wind turbine such as torque, thrust, and output power.

[0003] Currently, the spatial resources of a wind farm are limited, so there is inevitably the problem of wake losses caused by the relatively close spacing between different array wind turbine generator sets; however, due to the change in the incoming wind condition over time series, the fluctuation of speed and power is also exacerbated, making it difficult to quantify the losses caused by wake interference; for this reason, we propose a method, a system, an electronic device and a storage medium for evaluating wake losses of a wind turbine generator set. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, a system, an electronic device and a storage medium for evaluating wake losses of a wind turbine generator set, so as to quantify the losses caused by the wake effect in a wind farm under the time series change of wind conditions, and provide credible guiding suggestions for optimizing the layout of the wind farm and reducing the wake effect losses.

[0005] The first aspect of the technical solution of the present invention provides a method for evaluating wake losses of a wind turbine generator set, including the following steps:

[0006] Obtain the time series data of the wind condition, the layout data of the wind turbine arrangement and the SCADA data of a wind farm;

[0007] Construct a wake speed loss evaluation model, and correct the wake speed loss evaluation model based on the time series data of the wind condition;

[0008] Use the corrected wake speed loss evaluation model to output the wake speed loss evaluation result;

[0009] Construct a wake power loss evaluation model, and output the wake power loss evaluation result based on the SCADA data:

[0010] Output the wake loss evaluation result based on the speed loss evaluation result and the power loss evaluation result.

[0011] Further, a wake velocity loss evaluation model is constructed and corrected based on the wind condition time series data, including:

[0012] Correct the wake velocity loss evaluation model based on the wind condition time series data and the included angle between the wind turbine hub, and obtain the corrected incoming wind speed of the wind turbine.

[0013] Calculate the dimensionless value of the wake velocity of a single wind turbine based on the corrected incoming wind speed of the wind turbine.

[0014] Further, the expression for calculating the dimensionless value of the wake velocity of a single wind turbine based on the corrected incoming wind speed of the wind turbine is:

[0015]

[0016] In the formula, u(x, y, z) represents the dimensionless value of the wake velocity of a single wind turbine; (x, y, z) represents a three-dimensional coordinate system centered on the wind turbine hub; u ref (t) represents the incoming wind speed at the reference height that changes with time t; z ref represents the reference height; z hub represents the wind turbine hub height; σ y represents the standard deviation in the vertical direction; C represents the velocity shape characteristic parameter; coSθ represents the cosine value of the included angle between the incoming wind and the wind turbine hub plane; a represents the axial induction factor; represents the Gaussian function, which is used to describe the distribution characteristics of the wind turbine wake velocity in the vertical direction.

[0017] Further, use the corrected wake velocity loss evaluation model to output the wake velocity loss evaluation results, including:

[0018] Based on the wind turbine arrangement layout data, consider the velocity attenuation degree between the wind turbines at different positions and spacings in the wind farm to evaluate the wake velocity loss. The upstream wind turbines in the wind farm use the data collected by the wind measurement tower as the incoming wind speed, and the downstream wind turbines in the wind farm use the wake wind speed at intervals upstream of the wind turbines as the incoming wind boundary condition;

[0019] Calculate the dimensionless values of the wake velocities of each wind turbine in the wind farm, and obtain the initial wind turbines with serious wake losses according to the preset value of the wake velocity loss.

[0020] Further, using the corrected wake velocity loss evaluation model to output the wake velocity loss evaluation results, also includes:

[0021] Obtain the time when the initial wind turbine is affected by the wake loss, obtain the final number of wind turbines with serious wake losses according to the preset value of the wake velocity loss time ratio, and output the wake velocity loss evaluation level of the wind farm according to the final number of wind turbines.

[0022] Further, a wake power loss evaluation model is constructed, and a wake power loss evaluation result is output based on SCADA data, including:

[0023] Obtain the actual output power of the wind turbine based on SCADA data, and obtain the theoretical output power of the wind turbine based on the data of the anemometer tower;

[0024] Calculate the influence degree of the wind condition time series data on the dynamic power loss in different time periods, and output the power loss evaluation level of the wind farm.

[0025] Further, a wake loss evaluation result is output based on the speed loss evaluation result and the power loss evaluation result, including:

[0026] Use the speed loss evaluation result as the evaluation basis, and use the power loss evaluation result to assist in verifying the speed loss evaluation result.

[0027] The second aspect of the technical solution of the present invention provides a wind turbine wake loss evaluation system, including the wind turbine wake loss evaluation method described in the first aspect of the technical solution of the present invention. This evaluation system includes:

[0028] A data acquisition module configured to obtain the wind condition time series data, the wind turbine arrangement data and the SCADA data of the wind farm;

[0029] A wake speed loss evaluation module configured to output a wake speed loss evaluation result by using the corrected wake speed loss evaluation model;

[0030] A wake power loss evaluation model configured to output a wake power loss evaluation result based on SCADA data;

[0031] A verification module configured to assist in verifying the wake speed loss evaluation result based on the wake power loss evaluation result and output a wake loss evaluation result.

[0032] The third aspect of the technical solution of the present invention provides an electronic device, which includes: a processor and a memory communicatively connected to the processor; wherein, the memory stores instructions executable by the processor, and the instructions are executed by the processor so that the processor can execute the steps of the wind turbine wake loss evaluation method described in the first aspect of the technical solution of the present invention.

[0033] The fourth aspect of the technical solution of the present invention provides a computer-readable storage medium, on which a program for implementing the wind turbine wake loss evaluation method is stored, and the program for implementing the wind turbine wake loss evaluation method is executed by a processor to implement the steps of the wind turbine wake loss evaluation method described in the first aspect of the technical solution of the present invention.

[0034] The technical solution of the embodiment of the present invention has at least the following advantages and beneficial effects:

[0035] In order to realize the quantification of the loss caused by the wake effect in the wind farm under the time-series change of the wind condition, the present invention introduces the time-series change of the wind condition into the wake evaluation model, combines the data collected by the SCADA system and the anemometer tower, and proposes a universal method for evaluating the wake loss of wind turbines; this method can analyze the speed loss and power loss caused by wake interference under the change of wind speed and wind direction over time through the dynamic wake loss evaluation combining the speed loss evaluation model and the power loss evaluation model, evaluate the dynamic wake loss under the time-series change of the wind farm from both theoretical and practical perspectives, realize the dynamic and comprehensive evaluation of the loss brought by the wake effect in the wind farm, and can more accurately quantitatively analyze the speed loss and power loss caused by wake interference under the change of wind speed and wind direction over time, thereby providing a more comprehensive and more credible method for the evaluation of wake loss, and further providing a relatively high-accuracy theoretical support for the layout optimization of the wind farm and the evaluation of dynamic wake loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the following will briefly introduce the drawings required to be used in the embodiment of the present invention. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0037] Figure 1 It is a partial flow schematic diagram of the method for evaluating the wake loss of wind turbines provided by the embodiment of the present invention;

[0038] Figure 2 It is a detailed flow schematic diagram of the method for evaluating the wake loss of wind turbines provided by the embodiment of the present invention;

[0039] Figure 3 It is a schematic diagram of the layout of the wind farm units provided by the embodiment of the present invention;

[0040] Figure 4 It is a schematic diagram of the characteristic curve of the United Power wind turbine provided by the embodiment of the present invention;

[0041] Figure 5 It is a schematic diagram of the characteristic curve of the Acciona wind turbine provided by the embodiment of the present invention;

[0042] Figure 6 It is a schematic diagram of the derivation principle of the wake speed loss evaluation model provided by the embodiment of the present invention;

[0043] Figure 7 It is a schematic diagram of the curve of the monthly output power provided by the embodiment of the present invention;

[0044] Figure 8 Schematic diagram of the curve of the characteristic quantity of the power loss evaluation criterion provided by the embodiment of the present invention;

[0045] Figure 9 Schematic diagram of the structure of the wake loss evaluation system of the wind turbine provided by the embodiment of the present invention. Detailed implementation manners

[0046] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Generally, the components of the embodiments of the present invention described and illustrated in the drawings here may be arranged and designed in various different configurations.

[0047] It should be understood that the "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the said words may be replaced by other expressions.

[0048] As shown in this specification and the claims, unless the context clearly indicates an exception, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0049] Flowcharts are used in this specification to illustrate the operations performed by the systems according to the embodiments of this specification. It should be understood that the operations before or after do not necessarily have to be executed precisely in sequence. On the contrary, they may be executed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or several operations may be removed from these processes.

[0050] In a wind farm, wake losses mainly include velocity losses and power losses. Research has shown that since power is positively correlated with wind speed, velocity losses can not only characterize the wake loss characteristics but also serve as the basis for characterizing wake losses by calculating the output power. There are differences in the losses caused by the wake effect between adjacent wind turbines with different positional relationships and distances in the wind farm. The existing assessment methods for wake losses mainly include numerical simulation, wind tunnel tests, field tests, and wake models. Each of these four methods has its own characteristics and applicable scenarios. However, due to the dynamic changes in the incoming wind conditions in the wind farm over time series, the characteristics of the wind conditions changing over time also cause the flow field interference characteristics between the turbines to change dynamically. The existing technologies are not applicable to the assessment of dynamic wake losses in the wind farm. Therefore, based on the influence of the temporal variation of wind conditions on wake losses, the inventor proposed a method and system for assessing wake losses of wind turbines. Taking a certain onshore wind farm as an example, this onshore wind farm has a total of 50 1.5MW wind turbines with a total installed capacity of 75MW, among which 17 are 1.5MW wind turbines produced by United Power and the remaining 33 are 1.5MW wind turbines produced by Acciona. The feasibility of the method for assessing wake losses of wind turbines provided in this application is verified.

[0051] Please refer to Figure 1 and Figure 2 As shown, the first aspect of the technical solution of the present invention provides a method for assessing wake losses of wind turbines, including the following steps:

[0052] Step S100: Obtain the time series data of wind conditions, the layout data of wind turbine arrangements, and the SCADA data of the wind farm. Specifically, the SCADA system data is used to statistically calculate the actual output power of the wind farm. The time series data of wind conditions is collected by a wind measurement tower. The incoming wind speed collected by the wind measurement tower can be used to calculate the theoretical output power of the wind farm by comparing with the power characteristic curve of the wind turbine, and the loss characteristics of the wind farm can be further analyzed through comparison of different time periods. The SCADA data includes at least the wind speed, wind direction, and output power information under the operating conditions of the wind turbine, and the SCADA data for single analysis is at least 1 month. It should be noted that when assessing the dynamic wake velocity loss of the wind farm based on the difference in the incoming flow velocity between the upstream and downstream turbines, adjacent upwind and downwind wind turbines need to be selected according to the arrangement of the machine positions in the target wind farm to assess the wake loss. At the same time, considering the influence of altitude and the regional division during the construction period of the wind farm, in this embodiment, the wind turbines are divided into different groups for research. The layout of the wind farm turbines is as Figure 3 shown; when calculating the velocity using the wake velocity loss assessment model, the incoming wind speed needs to be compared with the axial thrust coefficient curve to determine the value of the axial thrust coefficient. When assessing the dynamic wake power loss of the wind farm, the calculation of the theoretical power of the whole field needs to be completed based on the power characteristic curve of the turbine. The characteristic curves of the two types of wind turbines in this embodiment are asFigure 4 and Figure 5 as shown;

[0053] Step S200: Construct a wake velocity loss evaluation model and correct the wake velocity loss evaluation model based on the wind condition time series data;

[0054] Step S200 specifically includes:

[0055] Step S210: Correct the wake velocity loss evaluation model based on the wind condition time series data and the included angle between the wind and the fan hub to obtain the corrected incoming wind speed of the fan; specifically, in this embodiment, the model is corrected considering the time series change of the wind condition. The change of the wind condition is mainly divided into two aspects: wind direction and wind speed; the wake velocity loss evaluation model considers the Gaussian function distribution characteristics of the wake velocity and uses the law of conservation of momentum to convert the influence of the wind shear effect into the velocity deficit. The derivation principle of the wake velocity loss evaluation model is as Figure 6 shown. For the wind direction, assume that the incoming wind at a certain moment is incident on the fan in the direction of Figure 6 , and the incoming wind speed at this time should be u ref cosθ, where θ is the included angle between the incoming wind and the fan hub plane, which can be obtained by combining the position of the wind turbine nacelle and the wind direction; on the other hand, due to the volatility of the wind speed, the wind speeds at different times are different. In the prior art, it is obviously unreasonable to use a certain determined wind speed for the wake loss assessment of the wind farm; therefore, the actual wind speed acting on the fan hub plane in this embodiment is corrected to u ref (t)cosθ, where t represents the time series, and u ref (t) is the incoming wind speed at the reference height changing with the time series;

[0056] Step S220: Calculate the non-dimensional value of the wake velocity of a single fan based on the corrected incoming wind speed of the fan. Its expression is:

[0057]

[0058] In the formula, u(x, y, z) represents the non-dimensional value of the wake velocity of a single fan; (x, y, z) represents a three-dimensional coordinate system centered on the fan hub; u ref (t) represents the incoming wind speed at the reference height changing with time t; z ref represents the reference height; z hub represents the fan hub height; σ y represents the standard deviation in the vertical direction; C represents the velocity shape characteristic parameter; cosθ represents the cosine value of the included angle between the incoming wind and the fan hub plane; a represents the axial induction factor; The Gaussian function is used to describe the distribution characteristics of the wake velocity of the wind turbine in the vertical direction; in this embodiment, the hub center of the wind turbine is used as the coordinate origin, the x-direction represents the axial direction extending from the incoming wind direction to the wake area; the y-direction represents the horizontal direction radially perpendicular to the x-axis; the z-direction represents the vertical direction perpendicular to the xoy plane, and a three-dimensional coordinate system is established; it should be noted that only the modified implementation method of the wake velocity loss evaluation model is provided in this embodiment. For the specific derivation process of the wake velocity loss evaluation model, reference can be made to the invention patent with the publication number CN110009736A;

[0059] Step S300: Output the wake velocity loss evaluation result by using the modified wake velocity loss evaluation model;

[0060] Step S300 specifically includes:

[0061] Step S310: Based on the wind turbine arrangement layout data, evaluate the wake velocity loss considering the velocity attenuation degree between wind turbines at different positions and spacings in the wind farm. The upstream wind turbines in the wind farm use the data collected by the anemometer tower as the incoming wind speed, and the downstream wind turbines in the wind farm use the wake wind speed at intervals upstream of the wind turbines as the incoming wind boundary condition;

[0062] Step S320: Calculate the dimensionless value of the wake velocity of each wind turbine in the wind farm, and obtain the initial wind turbines with serious wake losses according to the preset value of the wake velocity loss;

[0063] Step S330: Obtain the time when the initial wind turbines are affected by the wake loss, obtain the final number of wind turbines with serious wake losses according to the preset value of the wake velocity loss time ratio, and evaluate the wake velocity loss level of the output wind farm according to the final number of wind turbines; specifically, the percentage of the final number of wind turbines in the total number of wind turbines in the whole wind farm is used as the evaluation feature quantity of the wake velocity loss. In this embodiment, the velocity loss degree of the wind farm is divided into five levels, namely, the percentage less than 5% is small, the percentage between 5% - 10% is relatively small, the percentage between 10% - 15% is general, the percentage between 15% - 20% is relatively large, and the percentage greater than 20% is large;

[0064] The following provides a specific implementation method for evaluating the wake velocity loss of the target wind farm:

[0065] Based on the wind turbine layout and unit characteristic response curves of the target wind farm, experiments on the velocity loss characteristics were carried out according to the proposed dynamic wake velocity loss evaluation method and evaluation criteria. When selecting wind condition data for discussing wake velocity loss, the selection of wind direction referred to the prevailing wind direction of the wind farm (340° - 10°, with the due north direction as 0° and the clockwise direction as the positive direction), and at the same time, the incoming flow wind directions were made to have certain differences as much as possible to improve the significance of the evaluation results for engineering practice; the selection of wind speed considered the wind speed range (10 - 13 m / s) where the axial thrust coefficient of the wind turbine unit changes greatly to clarify the influence of unit parameters on the evaluation results; the data of the anemometer tower were recorded every minute, and a 10-minute data segment was selected to evaluate the dynamic wake velocity loss of the wind turbine unit. The wind shear coefficient corresponding to this time period was 0.12, and the specific wind condition parameters are shown in Table 4:

[0066]

[0067] Table 4 Wind condition parameters corresponding to different time conditions

[0068] Table 4 records the wind condition data corresponding to the 10-minute data segment. It should be noted that when the incoming flow wind direction changes, the relative position relationship between the upwind and downwind wind turbines will also change accordingly, which corresponds to the value of the coordinate distance in the wake model; therefore, when studying the wind turbines at the boundary of the wind farm, since there are no wind turbines upwind of the unit, it is directly determined as a unit that is not severely affected by the wake (that is, the corresponding u(x, y, z) is regarded as 1);

[0069] Step S400: Construct a wake power loss evaluation model and output the wake power loss evaluation result based on the SCADA data;

[0070] Step S400 specifically includes:

[0071] Step S410: Obtain the actual output power of the wind turbine based on the SCADA data and obtain the theoretical output power of the wind turbine based on the anemometer tower data;

[0072] Step S420: Calculate the influence degree of the wind condition time series data on the dynamic power loss in different time periods and output the power loss evaluation grade of the wind farm; specifically, the percentage of the actual output power of the wind turbine in the theoretical output power is used as the evaluation characteristic quantity of the wake power loss. In this embodiment, the power loss degree of the wind farm is divided into five grades, namely, greater than 97% is small, between 94% - 97% is relatively small, between 91% - 94% is general, between 88% - 91% is relatively large, and less than 88% is large;

[0073] Step S500: Output the wake loss evaluation result based on the velocity loss evaluation result and the power loss evaluation result;

[0074] Step S500 specifically includes:

[0075] Step S510: Using the speed loss evaluation result as the evaluation basis, the power loss evaluation result is used to assist in verifying the speed loss evaluation result;

[0076] The following provides the specific implementation method for the wake power loss evaluation of the target wind farm:

[0077] The output power of the wind farm was calculated as an auxiliary verification of the dynamic wake speed loss evaluation result; the total power of 50 wind turbines was analyzed using SCADA data, the wind speed measured by the anemometer tower, and the output power characteristic curve of the wind turbines, and the data points corresponding to the inoperability of the wind turbines due to maintenance and faults were excluded; the characteristics of the output power over different monthly time lengths were visualized in the form of the monthly power generation of the wind farm in 2017 and 2018, such as Figure 7 and Figure 8 shown, Figure 7 records the monthly output power, Figure 8 records the calculation results of the dynamic wake power loss characteristic quantities; in Figure 7 the actual output power and the theoretical output power of the wind farm in the selected months are characterized, and the results show that the minimum values of the output power in 2017 and 2018 are obtained in July and August respectively, the output power of the wind farm is lower in summer (June - August) months, and relatively higher in winter (December - February) months; compared with 2018, the output power of the wind farm is higher in November - December 2017, and the corresponding incoming flow wind speed is also greater; therefore, in order to further quantify the dynamic wake power loss of the wind farm, in Figure 8The relative value of the output power and the theoretical power under the time-series change of wind conditions, i.e., the loss characteristic quantity, is analyzed. During the time period discussed in this embodiment, the maximum dynamic power loss occurred in November 2018, which was 86.9%. The power loss caused by the wake effect in the target wind farm in 2017 was generally smaller than that in 2018. The trend of the change in loss characteristics is not exactly the same as the change in the overall output power of the whole field. Although a larger wind speed can promote the recovery of the wake speed, the time-series change of the wind direction changes the relative positions between different array wind turbines, and the wake interference characteristics also vary accordingly, which is consistent with the above evaluation results of the dynamic wake speed loss. Furthermore, it can be understood that there are cases where the power loss is relatively large or small in some months. The power loss caused by the dynamic wake effect in the target wind farm is relatively serious, and the value of the loss characteristic quantity is in the range of 88-91%, belonging to the "relatively large" level, verifying the evaluation results of the dynamic wake speed loss. Thus, it can be seen that the evaluation method provided in this application realizes a dynamic and comprehensive evaluation of the loss caused by the wake effect in a wind farm, can more accurately quantitatively analyze the speed loss and power loss caused by wake interference due to the change of wind speed and wind direction over time, thereby providing a more comprehensive and reliable method for the evaluation of wake loss, and further providing a theoretical support with higher accuracy for the layout optimization of the wind farm and the evaluation of dynamic wake loss.

[0078] In summary, the wind turbine wake loss evaluation method provided in this application has at least the following beneficial effects: The dynamic wake speed loss pays more attention to the influence of wind condition changes on the loss within a small time scale, discusses the wake effect between adjacent wind turbines with different relative position relationships by means of the minute-level data recorded by the anemometer tower, while the dynamic wake power loss targets a longer time length and completes the evaluation of the wind farm by calculating the power of the whole field of wind turbines in different months; The evaluation result of the dynamic speed loss serves as the main basis for the overall evaluation result of the wind farm, while the evaluation result of the dynamic power loss can be used as an auxiliary verification of the overall evaluation result of the wind farm. Compared with the existing research that discusses the influence of a single factor, the dynamic wake loss evaluation method proposed in this invention combines the discussion of speed loss and power loss to form a complete evaluation system; At the same time, the wake model is corrected by introducing the time-series change of wind conditions, comprehensively considering the influence of the external environment and the relative position between wind turbines on the wake speed; The analysis of the target wind farm in the specification shows that during the selected time period, the proportion of wind turbines seriously affected by the dynamic wake reached 18%. When arranging the machine position points, the staggered arrangement of wind turbines is beneficial to reducing the influence of the wake effect. The evaluation results provided in this application can provide a theoretical support with higher accuracy for the layout optimization of the wind farm and the evaluation of dynamic wake loss.

[0079] Please refer to Figure 9As shown in the figure, the technical solution of the second aspect of the present invention provides a wake loss assessment system for a wind turbine generator set, including the wind turbine generator set wake loss assessment method described in the technical solution of the first aspect of the present invention. This assessment system includes:

[0080] A data acquisition module configured to obtain the time-series data of the wind conditions, the data of the layout of the wind turbines, and the SCADA data of the wind farm;

[0081] A wake velocity loss assessment module configured to output a wake velocity loss assessment result by using the corrected wake velocity loss assessment model;

[0082] A wake power loss assessment model configured to output a wake power loss assessment result based on the SCADA data;

[0083] A verification module configured to assist in verifying the wake velocity loss assessment result based on the wake power loss assessment result and output a wake loss assessment result.

[0084] The technical solution of the third aspect of the present invention provides an electronic device, which includes: a processor and a memory communicatively connected to the processor; wherein, the memory stores instructions executable by the processor, and when the instructions are executed by the processor, the processor can execute the steps of the wind turbine generator set wake loss assessment method described in the technical solution of the first aspect of the present invention.

[0085] The technical solution of the fourth aspect of the present invention provides a computer-readable storage medium, on which a program for implementing the wind turbine generator set wake loss assessment method is stored. When the program for implementing the wind turbine generator set wake loss assessment method is executed by a processor, the steps of the wind turbine generator set wake loss assessment method described in the technical solution of the first aspect of the present invention can be implemented.

[0086] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are proposed in this specification, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this specification.

[0087] Meanwhile, this specification uses specific terms to describe the embodiments of this specification. For example, "an embodiment", "one embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that the "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0088] In addition, unless clearly stated in the claims, the order of the processing elements and sequences, the use of numerical letters, or the use of other names in this specification are not used to limit the order of the processes and methods in this specification. Although some currently considered useful embodiments of the invention are discussed through various examples in the above disclosure, it should be understood that such details are only for illustrative purposes. The appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that conform to the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only through software solutions, such as installing the described system on existing servers or mobile devices.

[0089] Similarly, it should be noted that, in order to simplify the presentation of the disclosure in this specification and thus help the understanding of one or more embodiments of the invention, in the previous description of the embodiments of this specification, sometimes multiple features are grouped into one embodiment, drawing, or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than those mentioned in the claims. In fact, the features of the embodiments are fewer than all the features of the individual embodiments disclosed above.

[0090] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification can be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments clearly introduced and described in this specification.

Claims

1. A method for evaluating the wake loss of a wind turbine generator, characterized in that The method includes the following steps: Obtain the time-series data of wind conditions, the layout data of wind turbines, and the SCADA data of the wind farm; Construct a wake velocity loss assessment model, and correct the wake velocity loss assessment model based on the time-series data of wind conditions, including: Correct the wake velocity loss assessment model based on the time-series data of wind conditions and the angle between the wind direction and the wind turbine hub to obtain the corrected incoming wind speed of the wind turbine; Calculate the dimensionless value of the wake velocity of a single wind turbine based on the corrected incoming wind speed of the wind turbine. The expression is: In the formula, represents the dimensionless value of the wake velocity of a single wind turbine; represents a three-dimensional coordinate system centered on the wind turbine hub; represents the incoming flow wind speed at the reference height that changes with time ; represents the reference height; represents the wind turbine hub height; represents the standard deviation in the vertical direction; represents the velocity shape characteristic parameter; represents the cosine value of the angle between the incoming flow wind and the wind turbine hub plane; represents the axial induction factor; represents the Gaussian function, which is used to describe the distribution characteristics of the wind turbine wake velocity in the vertical direction; Output the wake velocity loss assessment result by using the corrected wake velocity loss assessment model; Construct a wake power loss assessment model and output the wake power loss assessment result based on the SCADA data; Output the wake loss assessment result based on the velocity loss assessment result and the power loss assessment result.

2. The method for evaluating wake loss of a wind turbine generator set according to claim 1, characterized in that Output the wake velocity loss assessment result by using the corrected wake velocity loss assessment model, including: Based on the layout data of wind turbines, consider the velocity attenuation degree between wind turbines at different positions and spacings in the wind farm to evaluate the wake velocity loss. The upstream wind turbines in the wind farm use the data collected by the wind measurement tower as the incoming wind speed, and the downstream wind turbines in the wind farm use the wake wind speed at intervals upstream of the wind turbines as the incoming wind boundary condition; Calculate the dimensionless value of the wake velocity of each wind turbine in the wind farm, and obtain the initial wind turbines with serious wake losses according to the preset value of the wake velocity loss.

3. The method for evaluating wake loss of a wind turbine generator set according to claim 2, wherein, Output the wake velocity loss assessment result by using the corrected wake velocity loss assessment model, and further include: Obtain the time when the initial wind turbines are affected by wake losses, obtain the final number of wind turbines with serious wake losses according to the preset value of the wake velocity loss time ratio, and output the wake velocity loss assessment level of the wind farm according to the final number of wind turbines.

4. The method for evaluating wake loss of a wind turbine generator set according to any one of claims 1 to 3, characterized in that, Construct a wake power loss assessment model and output the wake power loss assessment result based on the SCADA data, including: Obtain the actual output power of the wind turbine based on the SCADA data, and obtain the theoretical output power of the wind turbine based on the data of the wind measurement tower; Calculate the influence degree of the time-series data of wind conditions on the dynamic power loss in different time periods, and output the power loss assessment level of the wind farm.

5. The method for evaluating wake loss of a wind turbine generator set according to claim 4, characterized in that, Output the wake loss assessment result based on the velocity loss assessment result and the power loss assessment result, including: Use the velocity loss assessment result as the assessment basis, and use the power loss assessment result to assist in verifying the velocity loss assessment result.

6. Wind turbine wake loss assessment system, characterized in that, The wind turbine wake loss assessment system includes the wind turbine wake loss assessment method according to any one of claims 1 to 5. The assessment system includes: A data acquisition module configured to obtain the time-series data of wind conditions, the layout data of wind turbines, and the SCADA data of the wind farm; A wake velocity loss assessment module configured to output the wake velocity loss assessment result by using the corrected wake velocity loss assessment model; A wake power loss assessment model configured to output the wake power loss assessment result based on the SCADA data; A verification module configured to assist in verifying the wake velocity loss assessment result based on the wake power loss assessment result and output the wake loss assessment result.

7. An electronic device, characterized in that, The electronic device includes: a processor and a memory communicatively connected to the processor; wherein, the memory stores instructions executable by the processor, and the instructions are executed by the processor to enable the processor to execute the steps of the wind turbine wake loss assessment method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, A program for implementing the wind turbine wake loss assessment method is stored on the computer-readable storage medium, and the program for implementing the wind turbine wake loss assessment method is executed by a processor to implement the steps of the wind turbine wake loss assessment method according to any one of claims 1 to 5.

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