Torque coefficient determination method, device and equipment of wind turbine generator and medium

Abstract

The invention discloses a method, a device and equipment for determining a torque coefficient of a wind turbine generator and a medium. The method comprises the steps that to-be-processed data of a target wind turbine generator are obtained, the to-be-processed data are preprocessed to obtain to-be-corrected data, and the to-be-processed data at least comprise wind speed data, rotating speed data and power data; based on the factory power curve of the target wind turbine generator, correcting wind speed data in the to-be-corrected data to obtain target processing data; based on the target processing data and a wind turbine generator torque coefficient calculation model, determining a target torque gain coefficient from a plurality of preset torque gain coefficients of the target wind turbine generator; and determining a target torque coefficient based on a product of the target torque gain coefficient and a preset torque coefficient of the target wind turbine generator. According to the scheme, the setting accuracy of the torque coefficient can be improved, and then the overall operation effect of the wind turbine generator is improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of wind turbine control, and in particular to a method, device, equipment, and medium for determining a torque coefficient of a wind turbine. Background Art

[0002] With the growing demand for renewable energy, the development and utilization of wind energy, a clean, renewable energy source, has garnered widespread attention. As key equipment for wind energy conversion, the performance optimization of wind turbines is directly related to wind energy utilization efficiency. The torque coefficient, a key performance metric for evaluating wind turbines, significantly impacts the turbine's speed, power generation, and overall operational performance. Therefore, it's crucial to continuously determine the optimal torque coefficient for wind turbines to optimize their operational performance.

[0003] The existing torque coefficient relies on the factory settings of the wind turbine, which can ensure the optimal torque coefficient of the wind turbine under ideal conditions. However, the actual wind farm environment is complex and changeable, making it difficult to fully cover all operating conditions. This results in low accuracy in determining the torque coefficient, which in turn affects the operating state of the wind turbine. Summary of the Invention

[0004] The present invention provides a method, device, equipment and medium for determining the torque coefficient of a wind turbine generator set, which can improve the setting accuracy of the torque coefficient and thus enhance the overall operating effect of the wind turbine generator set.

[0005] In a first aspect, an embodiment of the present invention provides a method for determining a torque coefficient of a wind turbine generator set, comprising:

[0006] Acquire data to be processed of a target wind turbine generator set, and pre-process the data to be processed to obtain data to be corrected, wherein the data to be processed includes at least wind speed data, rotation speed data, and power data;

[0007] Based on the factory power curve of the target wind turbine generator set, the wind speed data in the data to be corrected is corrected to obtain target processed data;

[0008] Determining a target torque gain coefficient from a plurality of preset torque gain coefficients of the target wind turbine group based on the target processed data and a wind turbine group torque coefficient calculation model;

[0009] A target torque coefficient is determined based on a product of the target torque gain coefficient and a preset torque coefficient of the target wind turbine generator set.

[0010] In a second aspect, an embodiment of the present invention provides a device for determining a torque coefficient of a wind turbine generator set, comprising:

[0011] An acquisition module is used to acquire data to be processed of a target wind turbine group and pre-process the data to be processed to obtain data to be corrected, wherein the data to be processed includes at least wind speed data, rotation speed data and power data;

[0012] a correction module, configured to correct the wind speed data in the data to be corrected based on the factory power curve of the target wind turbine generator set to obtain target processed data;

[0013] a selection module, configured to determine a target torque gain coefficient from a plurality of preset torque gain coefficients of the target wind turbine generator set based on the target processed data and a wind turbine generator set torque coefficient calculation model;

[0014] The determination module is configured to determine a target torque coefficient based on a product of the target torque gain coefficient and a preset torque coefficient of the target wind turbine generator set.

[0015] In a third aspect, an embodiment of the present invention provides an electronic device, including:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the method according to the first aspect.

[0019] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.

[0020] The technical solution of the embodiment of the present invention obtains the data to be processed of the target wind turbine group, and pre-processes the data to be processed to obtain the data to be corrected, wherein the data to be processed includes at least wind speed data, rotation speed data, and power data; based on the factory power curve of the target wind turbine group, the wind speed data in the data to be corrected is corrected to obtain the target processed data; based on the target processed data and the wind turbine group torque coefficient calculation model, the target torque gain coefficient is determined from multiple preset torque gain coefficients of the target wind turbine group; and the target torque coefficient is determined based on the product of the target torque gain coefficient and the preset torque coefficient of the target wind turbine group. This solution can select a target torque gain coefficient that is suitable for the operating condition from multiple preset torque gain coefficients according to the operating condition, namely wind speed, rotation speed, and power, and then determine the target torque coefficient that is suitable for the operating condition, which can improve the setting accuracy of the torque coefficient and thus improve the overall operating effect of the wind turbine group.

[0021] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 This is a flow chart of a method for determining a torque coefficient of a wind turbine generator set provided in accordance with the first embodiment of the present invention;

[0024] Figure 2 This is a flow chart of a method for determining a torque coefficient of a wind turbine generator set according to a second embodiment of the present invention;

[0025] Figure 3 This is a flow chart of a method for determining a torque coefficient of a wind turbine generator set according to a third embodiment of the present invention;

[0026] Figure 4 2 is a schematic structural diagram of a device for determining a torque coefficient of a wind turbine generator set according to a fourth embodiment of the present invention;

[0027] Figure 5 It is a schematic structural diagram of an electronic device implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," and the like in the present invention are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatus.

[0030] Example 1

[0031] Figure 1 This is a flowchart of a method for determining the torque coefficient of a wind turbine generator set according to a first embodiment of the present invention. This embodiment is applicable to determining the torque coefficient of a wind turbine generator set. The method can be performed by a device for determining the torque coefficient of a wind turbine generator set. The device can be implemented in software and / or hardware and integrated into an electronic device. Furthermore, the electronic device includes, but is not limited to, computers, laptop computers, servers, and the like.

[0032] like Figure 1 As shown, the method includes:

[0033] S110 , obtaining data to be processed of a target wind turbine generator system, and preprocessing the data to be processed to obtain data to be corrected, wherein the data to be processed at least includes wind speed data, rotation speed data, and power data.

[0034] The target wind turbine may be a wind turbine for which a torque coefficient is to be determined. The torque coefficient may be a dimensionless parameter that measures the ability of a wind turbine to capture wind energy and convert it into mechanical torque, and is one of the core indicators for wind turbine performance analysis.

[0035] The data to be processed may be data to be processed for determining a torque coefficient, and may include at least wind speed data, rotational speed data, and power data. The wind speed data may include the wind speed of the target wind turbine; the rotational speed data may include the low-speed rotor speed and filtered real-time high-speed shaft speed of the target wind turbine; and the power data may include the power of the target wind turbine.

[0036] In this step, wind speed data, rotational speed data, and power data of the target wind turbine can be collected under operating conditions below the rated wind speed and with constant humidity and temperature, thereby obtaining the target wind turbine data to be processed. The wind speed data, rotational speed data, and power data can be obtained using corresponding sensors, which are not limited here.

[0037] When the target wind turbine's unprocessed data is obtained, the unprocessed data can be preprocessed, such as removing abnormal data from the unprocessed data or supplementing missing data from the unprocessed data. The preprocessed data can be the data obtained by preprocessing the unprocessed data, which needs to be corrected later.

[0038] S120 : Based on the factory power curve of the target wind turbine generator set, correct the wind speed data in the data to be corrected to obtain target processed data.

[0039] The factory power curve may be a power curve set at the factory for the target wind turbine, including the factory-set power corresponding to the target wind turbine at each factory-set wind speed. The factory power curve may be considered to be the wind speed-power curve for optimal generator operating performance of the target wind turbine.

[0040] In this step, a statistical method (SM) can be used, taking the wind speed data in the data to be corrected and the factory power curve of the target wind turbine as input. Assuming that the available operating point in the factory power curve is the operating point with the best generator operating performance, the wind speed data in the data to be corrected is corrected so that the wind speed data in the data to be corrected is close to the wind speed data under the condition of the best generator operating performance, thereby obtaining the corrected wind speed data. The statistical method can evaluate the efficiency of the wind turbine by estimating the wind speed entering the wind rotor. This method relies only on the factory power curve and can therefore also be used in wind farms without a weather station. The available operating point is the point in the factory power curve.

[0041] After correcting the wind speed data in the data to be corrected, the data other than the wind speed data in the data to be corrected, namely the speed data and power data, and the corrected wind speed data, are determined as target processing data, which is the data ultimately used to determine the torque coefficient.

[0042] S130 : Determine a target torque gain coefficient from a plurality of preset torque gain coefficients of the target wind turbine generator set based on the target processed data and a wind turbine generator set torque coefficient calculation model.

[0043] The preset torque gain coefficient may be a torque gain coefficient pre-set for the target wind turbine. There may be multiple preset torque gain coefficients, which are not limited here. The target torque gain coefficient may be a torque gain coefficient used to determine the torque coefficient of the target wind turbine.

[0044] The torque gain coefficient can be understood as a parameter in the wind turbine control system that describes the proportional relationship between the input control signal and the generator output torque, and is used to reflect the control system's sensitivity to torque regulation.

[0045] A wind turbine torque coefficient calculation model can be a mathematical model used to quantify a wind turbine's ability to convert wind energy into torque under specific operating conditions. Its core is to establish, through theoretical derivation, empirical fitting, or numerical simulation, a preset torque gain coefficient for a target wind turbine, the relationship between the preset torque coefficient and the rotational speed and generator torque, and the relationship between the preset torque coefficient and key influencing parameters such as the tip speed ratio and the maximum wind energy utilization coefficient. The preset torque coefficient can be a pre-set torque coefficient for the target wind turbine, which is not limited here.

[0046] In this step, the target processing data can be substituted into the wind turbine torque coefficient calculation model as a known parameter to determine the probability distribution of the wind energy utilization coefficient and the maximum value of the wind energy utilization coefficient of the target wind turbine under different preset torque gain coefficients; the probability distribution of the wind energy utilization coefficient and the maximum value of the wind energy utilization coefficient under each preset torque gain coefficient are used as evaluation indicators, and the entropy weight fuzzy comprehensive evaluation method is used to evaluate each preset torque gain coefficient to determine the evaluation result value of each preset torque gain coefficient; the preset torque gain coefficient corresponding to the largest evaluation result value is determined as the target torque gain coefficient.

[0047] The probability distribution of the wind energy utilization coefficient can be understood as the probability distribution of the wind energy utilization coefficient, reflecting the probability characteristics of different values ​​of the wind energy utilization coefficient under actual wind resource conditions and turbine operating conditions. Its distribution is determined by the wind speed distribution, the tip speed ratio distribution, and the relationship between the tip speed ratio and the wind energy utilization coefficient. The maximum wind energy utilization coefficient can be understood as the maximum value of the wind energy utilization coefficient. The wind energy utilization coefficient quantitatively measures the efficiency of a wind turbine in converting wind energy into mechanical energy and then into electrical energy.

[0048] S140 . Determine a target torque coefficient based on a product of the target torque gain coefficient and a preset torque coefficient of the target wind turbine generator set.

[0049] In this step, the target torque coefficient may be determined as the product of the target torque gain coefficient and the preset torque coefficient of the target wind turbine generator set.

[0050] Each time the target wind turbine group's data to be processed is obtained, the data to be processed can be processed according to the process of this embodiment to determine the target torque coefficient of the target wind turbine group, so that the target wind turbine group can be dynamically set based on the target torque coefficient.

[0051] The technical solution of the embodiment of the present invention obtains the data to be processed of the target wind turbine group, and pre-processes the data to be processed to obtain the data to be corrected, wherein the data to be processed includes at least wind speed data, rotation speed data, and power data; based on the factory power curve of the target wind turbine group, the wind speed data in the data to be corrected is corrected to obtain the target processed data; based on the target processed data and the wind turbine group torque coefficient calculation model, the target torque gain coefficient is determined from multiple preset torque gain coefficients of the target wind turbine group; and the target torque coefficient is determined based on the product of the target torque gain coefficient and the preset torque coefficient of the target wind turbine group. This solution can select a target torque gain coefficient that is suitable for the operating condition from multiple preset torque gain coefficients according to the operating condition, namely wind speed, rotation speed, and power, and then determine the target torque coefficient that is suitable for the operating condition, which can improve the setting accuracy of the torque coefficient and thus improve the overall operating effect of the wind turbine group.

[0052] Example 2

[0053] Figure 2 This is a flow chart of a method for determining the torque coefficient of a wind turbine set provided according to the second embodiment of the present invention. This embodiment is based on the above-mentioned first embodiment and further refines the method for determining the target torque gain coefficient from multiple preset torque gain coefficients of the target wind turbine set based on the target processing data and the wind turbine set torque coefficient calculation model.

[0054] like Figure 2 As shown, the method includes:

[0055] S110 , obtaining data to be processed of a target wind turbine generator system, and preprocessing the data to be processed to obtain data to be corrected, wherein the data to be processed at least includes wind speed data, rotation speed data, and power data.

[0056] S120 : Based on the factory power curve of the target wind turbine generator set, correct the wind speed data in the data to be corrected to obtain target processed data.

[0057] S131. Determine the probability distribution of the wind energy utilization coefficient and the maximum value of the wind energy utilization coefficient of the target wind turbine generator set under each preset torque gain coefficient based on the target processing data and the wind turbine generator set torque coefficient calculation model.

[0058] In this step, the wind turbine torque coefficient calculation model can be constructed in the following way:

[0059] The tip speed ratio is defined as follows:

[0060]

[0061] Among them, λ is the tip speed ratio; ω is the low-speed end wind rotor speed; R is the wind rotor radius; v is the wind speed.

[0062] The relationship between power and wind speed is as follows:

[0063]

[0064] P=T·ω g

[0065] ω g =ωn

[0066] Where P is power; ρ is the incoming current density; C p is the wind energy utilization coefficient; R is the radius of the wind wheel; v is the wind speed; T is the generator torque; ω g is the real-time speed of the high-speed shaft after filtering; ω is the speed of the wind wheel at the low-speed end; and n is the gearbox speed ratio.

[0067] The torque control target of the target wind turbine is to capture the maximum wind energy. The relationship between the generator torque and the speed is:

[0068]

[0069] Among them, C pmax is the maximum value of wind energy utilization coefficient, λ opt is the optimal tip speed ratio. Other parameters have been explained and will not be repeated here.

[0070] Furthermore, the preset torque coefficient K is:

[0071]

[0072] Assuming the preset torque gain coefficient is α, the generator torque T is re-determined as:

[0073] T=αKω g 2

[0074] The calculation model of wind turbine torque coefficient can be expressed as:

[0075]

[0076] Optionally, the wind turbine torque coefficient calculation model indicates a preset torque gain coefficient, a preset torque coefficient and a relationship between the rotational speed and the generator torque, and a relationship between the preset torque coefficient and a maximum value of the wind energy utilization coefficient.

[0077] In this step, the corrected wind speed and rotational speed data included in the target processed data can be substituted as known parameters into the wind turbine torque coefficient calculation model to determine the maximum wind energy utilization coefficient of the target wind turbine under different preset torque gain coefficients. Furthermore, in combination with the above model, the probability distribution of the wind energy utilization coefficient of the target wind turbine under different preset torque gain coefficients can be determined based on the wind speed distribution, the tip speed ratio distribution, and the relationship between the tip speed ratio and the wind energy utilization coefficient.

[0078] S132. Based on the determined probability distribution of the wind energy utilization coefficient and the determined maximum value of the wind energy utilization coefficient, construct a factor domain corresponding to each preset torque gain coefficient.

[0079] The factor domain corresponding to each preset torque gain coefficient can be expressed as: U={C p (λ),C pmax}. Among them, U is the factor domain; C p (λ) indicates the probability distribution of wind energy utilization coefficient, C p is the wind energy utilization coefficient, λ is the tip speed ratio; C pmax is the maximum value of wind energy utilization coefficient.

[0080] S133. Construct a fuzzy relationship matrix based on the factor domain corresponding to each preset torque gain coefficient.

[0081] In this step, a fuzzy relationship matrix can be constructed based on the factor domain corresponding to each preset torque gain coefficient using a fuzzy relationship matrix construction formula and in combination with a membership function. When constructing the fuzzy relationship matrix, the membership function is used to determine the membership of different evaluation indicators (i.e., elements in the factor domain) to different preset torque gain coefficients.

[0082] In one embodiment, a fuzzy relationship matrix is ​​constructed based on the factor domain corresponding to each preset torque gain coefficient, including:

[0083] Mapping the probability distribution of the wind energy utilization coefficient in the factor domain corresponding to each preset torque gain coefficient into a first membership degree according to the first membership function;

[0084] Mapping the maximum value of the wind energy utilization coefficient in the factor domain corresponding to each preset torque gain coefficient to a second membership degree according to the second membership function;

[0085] A fuzzy relationship matrix is ​​constructed by using the first membership degree and the second membership degree corresponding to each preset torque gain coefficient.

[0086] The constructed fuzzy relationship matrix can be expressed as follows:

[0087]

[0088] Among them, q 11 ,q 21 ,...,q m1 The probability distributions of the wind energy utilization coefficients of the 1st to the mth preset torque gain coefficients are respectively based on the first membership degrees obtained by mapping the first membership function; q 12 ,q 22 ,...,q m2 The maximum values ​​of the wind energy utilization coefficients for the first to mth preset torque gain coefficients are respectively obtained based on the second membership degrees mapped by the second membership function; m is the total number of preset torque gain coefficients. There is no limitation on the first membership function and the second membership function.

[0089] S134. Determine the evaluation result value of each preset torque gain coefficient based on the fuzzy relationship matrix and the fuzzy weight vector.

[0090] In this step, the evaluation result can be used to construct a formula, and the evaluation result value of each preset torque gain coefficient can be determined based on the fuzzy relationship matrix and the fuzzy weight vector.

[0091] In one embodiment, based on the fuzzy relationship matrix and the fuzzy weight vector, determining the evaluation result value of each preset torque gain coefficient includes:

[0092] Performing exponential transformation on the elements in the fuzzy relation matrix to obtain a transformed fuzzy relation matrix;

[0093] The fuzzy weight vector is multiplied by the transformed fuzzy relationship matrix to obtain the evaluation result value of each preset torque gain coefficient.

[0094] Then the evaluation result B of each preset torque gain coefficient can be expressed as:

[0095]

[0096] Among them, A is the fuzzy weight vector, a1, a2, ..., a m are the preset fuzzy weights of the 1st to mth preset torque gain coefficients respectively; Q' is the transformed fuzzy relationship matrix, in which the element q ij '=e qij , i is 1 to m, j is 1 or 2; b1, b2, ..., b m They are the evaluation result values ​​of the 1st to mth preset torque gain coefficients respectively.

[0097] The above-mentioned exponential transformation of the elements in the fuzzy relationship matrix is ​​because the membership degree in the fuzzy comprehensive evaluation is usually in the range of 0 to 1. Therefore, when applying the operator to synthesize the fuzzy relationship matrix, the membership degree of the evaluation index must be transformed first, and the value of the membership degree must be converted into a number greater than 1.

[0098] S135. Determine the preset torque gain coefficient corresponding to the maximum evaluation result value as the target torque gain coefficient.

[0099] S140 . Determine a target torque coefficient based on a product of the target torque gain coefficient and a preset torque coefficient of the target wind turbine generator set.

[0100] The technical solution of the embodiment of the present invention constructs a wind turbine torque coefficient calculation model to determine the probability distribution and maximum value of the wind energy utilization coefficient of the target wind turbine under each preset torque gain coefficient. Based on the probability distribution and maximum value of the wind energy utilization coefficient under each preset torque gain coefficient, the entropy-weighted fuzzy comprehensive evaluation method is used to evaluate each preset torque gain coefficient to determine the evaluation result value of each preset torque gain coefficient. The preset torque gain coefficient corresponding to the maximum evaluation result value is determined as the target torque gain coefficient. This solution uses the wind turbine torque coefficient calculation model to select a target torque gain coefficient that is suitable for the current operating conditions of the target wind turbine from multiple preset torque gain coefficients, so that the determined target torque gain coefficient better matches the actual operating conditions and improves the accuracy of torque gain coefficient selection.

[0101] Example 3

[0102] Figure 3 This is a flow chart of a method for determining the torque coefficient of a wind turbine set provided according to the third embodiment of the present invention. This embodiment is based on the above-mentioned embodiment one, and corrects the wind speed data in the data to be corrected based on the factory power curve of the target wind turbine set to obtain further refinement of the target processed data; and pre-processes the data to be processed to obtain further refinement of the data to be corrected.

[0103] like Figure 3 As shown, the method includes:

[0104] S111 . Obtain data to be processed of a target wind turbine generator set, where the data to be processed includes at least wind speed data, rotation speed data, and power data.

[0105] S112. Abnormal data in the data to be processed is removed based on a cloud segmented optimal entropy algorithm, and missing data in the data to be processed is supplemented based on a four-point interpolation algorithm to obtain data to be corrected.

[0106] Abnormal data in the data to be processed is removed based on the cloud segmented optimal entropy algorithm. Specifically: for any type of data in the data to be processed, it is divided into multiple data intervals to be processed; for any data interval to be processed, the interval entropy value of each data in the interval is calculated based on the cloud segmented optimal entropy algorithm; based on the entropy set curve composed of the entropy values ​​of each interval, the lower threshold and upper threshold of each interval are determined; data outside the range formed by the lower threshold and the upper threshold are removed as abnormal data.

[0107] For example, for power data, the entropy value of the cloud model is used to identify data sets with output power greater than or less than the theoretical value. That is, data sets with higher or lower power generation performance of the unit are removed as upper abnormal data and lower abnormal data, respectively, and the remaining data are normal data.

[0108] The missing data in the data to be processed are supplemented using a four-point interpolation algorithm. Specifically, for any type of data in the data to be processed, the four data interpolation points corresponding to the data to be supplemented are determined using the four-point interpolation algorithm. The data to be supplemented is then supplemented based on the determined data interpolation points. Optionally, during the missing data supplementation process, the mean relative error and reconstruction accuracy can be used as evaluation metrics for the supplemented data, although these are not limited here.

[0109] S121 . Remove wind speed data with turbulence exceeding a set turbulence threshold from the wind speed data to be corrected, to obtain first corrected wind speed data.

[0110] The turbulence threshold may be set to 10%, that is, wind speed data with turbulence exceeding 10% is removed from the wind speed data to be corrected to obtain the first corrected wind speed data.

[0111] S122 . For an available operating point in the factory power curve, identify second corrected wind speed data from the first corrected wind speed data, where the second corrected wind speed data is located in a neighborhood of the available operating point.

[0112] That is, the second corrected wind speed data is identified from the first corrected wind speed data, so that the second corrected wind speed data is located at the available working point P in the factory power curve. k The neighborhood of P k (1-ε) to P k The interval between (1+ε), ε can be 0.01.

[0113] S123: Determine a cumulative distribution function curve corresponding to the second corrected wind speed data, and take a preset percentage of data from the cumulative distribution function curve as third corrected wind speed data.

[0114] Calculate the empirical cumulative distribution function of the second corrected wind speed data, obtain a cumulative distribution function curve based on the empirical cumulative distribution function, and take a pre-set percentage (such as 5%) of data from the cumulative distribution function curve as the third corrected wind speed data, that is, select a probability value not exceeding 5% from the cumulative distribution function curve.

[0115] S124: Perform linear regression fitting using the third corrected wind speed data and the factory set wind speed in the factory power curve to obtain corrected wind speed data.

[0116] It should be noted that for each available operating point in the factory power curve, data correction can be performed in the above manner.

[0117] S125 , determining the data except the wind speed data in the data to be corrected and the corrected wind speed data as target processing data.

[0118] S130 : Determine a target torque gain coefficient from a plurality of preset torque gain coefficients of the target wind turbine generator set based on the target processed data and a wind turbine generator set torque coefficient calculation model.

[0119] S140 . Determine a target torque coefficient based on a product of the target torque gain coefficient and a preset torque coefficient of the target wind turbine generator set.

[0120] The technical solution of the embodiment of the present invention removes abnormal data in the data to be processed based on the cloud segmented optimal entropy algorithm, and supplements the missing data in the data to be processed based on the four-point interpolation algorithm to obtain the data to be corrected, thereby improving the accuracy of the input data when determining the torque coefficient of the target wind turbine generator set and effectively improving the data quality; the wind speed data in the data to be corrected is corrected through the factory power curve, without relying on additional meteorological equipment, and has high practicality.

[0121] Example 4

[0122] Figure 4 is a schematic diagram of the structure of a device for determining the torque coefficient of a wind turbine according to a fourth embodiment of the present invention. This embodiment is applicable to situations where the torque coefficient of a wind turbine is determined, such as Figure 4 As shown, the specific structure of the device includes:

[0123] An acquisition module 41 is configured to acquire data to be processed of a target wind turbine and pre-process the data to be processed to obtain data to be corrected, wherein the data to be processed includes at least wind speed data, rotation speed data, and power data;

[0124] A correction module 42 is configured to correct the wind speed data in the data to be corrected based on the factory power curve of the target wind turbine generator system to obtain target processed data;

[0125] A selection module 43 is configured to determine a target torque gain coefficient from a plurality of preset torque gain coefficients of the target wind turbine generator set based on the target processed data and a wind turbine generator set torque coefficient calculation model;

[0126] The determination module 44 is configured to determine a target torque coefficient based on a product of the target torque gain coefficient and a preset torque coefficient of the target wind turbine generator system.

[0127] The torque coefficient determination device of a wind turbine provided in this embodiment obtains the to-be-processed data of a target wind turbine through an acquisition module, and pre-processes the to-be-processed data to obtain the to-be-corrected data, wherein the to-be-processed data includes at least wind speed data, rotation speed data, and power data; the wind speed data in the to-be-corrected data is corrected by a correction module based on the factory power curve of the target wind turbine to obtain target processed data; the target torque gain coefficient is determined from multiple preset torque gain coefficients of the target wind turbine based on the target processed data and a wind turbine torque coefficient calculation model through a selection module; and the target torque coefficient is determined by a determination module based on the product of the target torque gain coefficient and the preset torque coefficient of the target wind turbine. This solution can select a target torque gain coefficient adapted to the operating condition from multiple preset torque gain coefficients according to the operating condition, namely wind speed, rotation speed, and power, and then determine the target torque coefficient adapted to the operating condition, thereby improving the setting accuracy of the torque coefficient and thereby improving the overall operating effect of the wind turbine.

[0128] Furthermore, the selection module 43 is specifically configured to:

[0129] Determining the probability distribution of the wind energy utilization coefficient and the maximum value of the wind energy utilization coefficient of the target wind turbine under each preset torque gain coefficient based on the target processing data and the wind turbine torque coefficient calculation model;

[0130] Based on the determined probability distribution of the wind energy utilization coefficient and the determined maximum value of the wind energy utilization coefficient, a factor domain corresponding to each preset torque gain coefficient is constructed;

[0131] Based on the factor domain corresponding to each preset torque gain coefficient, a fuzzy relationship matrix is ​​constructed;

[0132] Determining evaluation result values ​​of each preset torque gain coefficient based on the fuzzy relationship matrix and the fuzzy weight vector;

[0133] The preset torque gain coefficient corresponding to the maximum evaluation result value is determined as the target torque gain coefficient.

[0134] Furthermore, the wind turbine torque coefficient calculation model indicates a preset torque gain coefficient, a relationship between the preset torque coefficient and the rotational speed and the generator torque, and a relationship between the preset torque coefficient and the maximum value of the wind energy utilization coefficient.

[0135] Furthermore, the selection module 43 is specifically configured to:

[0136] Mapping the probability distribution of the wind energy utilization coefficient in the factor domain corresponding to each preset torque gain coefficient into a first membership degree according to the first membership function;

[0137] Mapping the maximum value of the wind energy utilization coefficient in the factor domain corresponding to each preset torque gain coefficient to a second membership degree according to the second membership function;

[0138] A fuzzy relationship matrix is ​​constructed by using the first membership degree and the second membership degree corresponding to each preset torque gain coefficient.

[0139] Furthermore, the selection module 43 is specifically configured to:

[0140] Performing exponential transformation on the elements in the fuzzy relation matrix to obtain a transformed fuzzy relation matrix;

[0141] The fuzzy weight vector is multiplied by the transformed fuzzy relationship matrix to obtain the evaluation result value of each preset torque gain coefficient.

[0142] Furthermore, the correction module 42 is specifically configured to:

[0143] Remove wind speed data with turbulence exceeding a set turbulence threshold from the wind speed data to be corrected, to obtain first corrected wind speed data;

[0144] For an available operating point in the factory power curve, identifying second corrected wind speed data from the first corrected wind speed data, where the second corrected wind speed data is located in a neighborhood of the available operating point;

[0145] Determine a cumulative distribution function curve corresponding to the second corrected wind speed data, and take a predetermined percentage of data from the cumulative distribution function curve as third corrected wind speed data;

[0146] Performing linear regression fitting using the third corrected wind speed data and the factory set wind speed in the factory power curve to obtain corrected wind speed data;

[0147] The data to be corrected, excluding the wind speed data, and the corrected wind speed data are determined as target processing data.

[0148] Furthermore, the acquisition module 41 is specifically configured to:

[0149] Abnormal data in the data to be processed are removed based on a cloud segmented optimal entropy algorithm, and missing data in the data to be processed are supplemented based on a four-point interpolation algorithm to obtain data to be corrected.

[0150] The torque coefficient determination device for a wind turbine generator provided in an embodiment of the present invention can execute the torque coefficient determination method for a wind turbine generator provided in any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0151] Example 5

[0152] Figure 5 is a schematic diagram of the structure of an electronic device that implements an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0153] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0154] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0155] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for determining the torque coefficient of a wind turbine.

[0156] In some embodiments, the method for determining the torque coefficient of a wind turbine generator set may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for determining the torque coefficient of the wind turbine generator set described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for determining the torque coefficient of the wind turbine generator set in any other appropriate manner (e.g., by means of firmware).

[0157] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0158] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0159] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0160] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0161] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0162] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0163] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0164] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for determining the torque coefficient of a wind turbine generator system, characterized in that: include: Acquire data to be processed of a target wind turbine generator set, and pre-process the data to be processed to obtain data to be corrected, wherein the data to be processed includes at least wind speed data, rotation speed data, and power data; Based on the factory power curve of the target wind turbine generator set, the wind speed data in the data to be corrected is corrected to obtain target processed data; Determining a target torque gain coefficient from a plurality of preset torque gain coefficients of the target wind turbine group based on the target processed data and a wind turbine group torque coefficient calculation model; A target torque coefficient is determined based on a product of the target torque gain coefficient and a preset torque coefficient of the target wind turbine generator set.

2. The method according to claim 1, characterized in that Determining a target torque gain coefficient from a plurality of preset torque gain coefficients of the target wind turbine generator set based on the target processed data and a wind turbine generator set torque coefficient calculation model includes: Determining the probability distribution of the wind energy utilization coefficient and the maximum value of the wind energy utilization coefficient of the target wind turbine generator set under each preset torque gain coefficient based on the target processing data and the wind turbine generator set torque coefficient calculation model; Based on the determined probability distribution of the wind energy utilization coefficient and the determined maximum value of the wind energy utilization coefficient, a factor domain corresponding to each preset torque gain coefficient is constructed; Based on the factor domain corresponding to each preset torque gain coefficient, a fuzzy relationship matrix is ​​constructed; Determining evaluation result values ​​of each preset torque gain coefficient based on the fuzzy relationship matrix and the fuzzy weight vector; The preset torque gain coefficient corresponding to the maximum evaluation result value is determined as the target torque gain coefficient.

3. The method according to claim 2, characterized in that The wind turbine torque coefficient calculation model indicates a preset torque gain coefficient, a relationship between a preset torque coefficient and a rotational speed and a generator torque, and a relationship between a preset torque coefficient and a maximum value of a wind energy utilization coefficient.

4. The method according to claim 2, characterized in that Based on the factor domain corresponding to each preset torque gain coefficient, a fuzzy relationship matrix is ​​constructed, including: Mapping the probability distribution of the wind energy utilization coefficient in the factor domain corresponding to each preset torque gain coefficient into a first membership degree according to the first membership function; Mapping the maximum value of the wind energy utilization coefficient in the factor domain corresponding to each preset torque gain coefficient to a second membership degree according to the second membership function; A fuzzy relationship matrix is ​​constructed by using the first membership degree and the second membership degree corresponding to each preset torque gain coefficient.

5. The method according to claim 2, characterized in that Determining the evaluation result value of each preset torque gain coefficient based on the fuzzy relationship matrix and the fuzzy weight vector includes: Performing exponential transformation on the elements in the fuzzy relation matrix to obtain a transformed fuzzy relation matrix; The fuzzy weight vector is multiplied by the transformed fuzzy relationship matrix to obtain the evaluation result value of each preset torque gain coefficient.

6. The method according to claim 1, characterized in that Based on the factory power curve of the target wind turbine generator set, the wind speed data in the data to be corrected is corrected to obtain target processed data, including: Remove wind speed data with turbulence exceeding a set turbulence threshold from the wind speed data to be corrected, to obtain first corrected wind speed data; For an available operating point in the factory power curve, identifying second corrected wind speed data from the first corrected wind speed data, where the second corrected wind speed data is located in a neighborhood of the available operating point; Determine a cumulative distribution function curve corresponding to the second corrected wind speed data, and take a predetermined percentage of data from the cumulative distribution function curve as third corrected wind speed data; Performing linear regression fitting using the third corrected wind speed data and the factory set wind speed in the factory power curve to obtain corrected wind speed data; The data to be corrected, excluding the wind speed data, and the corrected wind speed data are determined as target processing data.

7. The method according to claim 1, characterized in that Preprocessing the data to be processed to obtain data to be corrected includes: Abnormal data in the data to be processed are removed based on a cloud segmented optimal entropy algorithm, and missing data in the data to be processed are supplemented based on a four-point interpolation algorithm to obtain data to be corrected.

8. A device for determining the torque coefficient of a wind turbine generator set, characterized in that: include: An acquisition module is used to acquire data to be processed of a target wind turbine group and pre-process the data to be processed to obtain data to be corrected, wherein the data to be processed includes at least wind speed data, rotation speed data and power data; a correction module, configured to correct the wind speed data in the data to be corrected based on the factory power curve of the target wind turbine generator set to obtain target processed data; a selection module, configured to determine a target torque gain coefficient from a plurality of preset torque gain coefficients of the target wind turbine generator set based on the target processed data and a wind turbine generator set torque coefficient calculation model; The determination module is configured to determine a target torque coefficient based on a product of the target torque gain coefficient and a preset torque coefficient of the target wind turbine generator set.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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