Method, device and equipment for determining optimal pitch angle of wind turbine generator and medium

Abstract

The invention discloses a method, device and equipment for determining the optimal pitch angle of a wind turbine generator and a medium, and relates to the technical field of wind power generation. The method comprises the following steps: acquiring an operation data set of a wind turbine generator in a target wind speed interval; performing curved surface fitting operation based on the operation data set to obtain a target relation model between the pitch angle and the output power of the wind turbine generator; and solving the target relation model to obtain the optimal pitch angle of the wind turbine generator in the target wind speed interval, so that the output power of the wind turbine generator is maximum. According to the technical scheme provided by the invention, the complexity of the tuning calculation process can be reduced, and the determination efficiency of the optimal pitch angle of the wind turbine generator can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of wind power generation, and in particular to a method, device, equipment and medium for determining an optimal pitch angle of a wind turbine. Background Art

[0002] As a clean renewable energy technology, wind power generation is based on the capture of wind energy by wind turbines. Wind turbines convert wind energy into mechanical energy through wind rotors, and then convert mechanical energy into electrical energy through generators. The efficiency of wind energy conversion into mechanical energy is mainly determined by the aerodynamic design of the wind rotor blades, the wind direction of the wind rotor, the tip speed ratio, and the pitch angle. Among them, the pitch angle of the blade refers to the angle between the blade chord and the relative wind speed, which is a key factor affecting the wind energy capture efficiency and output power.

[0003] In the prior art, when optimizing the control parameters of a wind turbine and determining the optimal pitch angle during the operation of the wind turbine, experimental calculation methods based on the design information of the turbine and numerical simulation methods are usually used. However, this method of determining the optimal pitch angle has the disadvantages of complex calculation process and low efficiency. Summary of the invention

[0004] The present application provides a method, device, equipment and medium for determining the optimal pitch angle of a wind turbine generator set, which can reduce the complexity of the tuning calculation process and improve the efficiency of determining the optimal pitch angle of the wind turbine generator set.

[0005] In a first aspect, the present application provides a method for determining an optimal pitch angle of a wind turbine generator set, the method comprising:

[0006] Obtaining the operating data set of the wind turbine in the target wind speed range;

[0007] Performing a surface fitting operation based on the operating data set to obtain a target relationship model between the pitch angle and output power of the wind turbine generator set;

[0008] The target relationship model is solved to obtain the optimal pitch angle of the wind turbine generator set within the target wind speed range, so as to maximize the output power of the wind turbine generator set.

[0009] Furthermore, solving the target relationship model to obtain the optimal pitch angle of the wind turbine within the target wind speed range includes: determining an initial pitch angle, an iteration step, and an initial gradient of the wind turbine; constructing a pitch angle iteration function for the wind turbine based on the target relationship model; updating the initial pitch angle based on the pitch angle iteration function through the initial gradient and the iteration step until a preset iteration stop condition is met, thereby obtaining the optimal pitch angle of the wind turbine within the target wind speed range.

[0010] Furthermore, when the surface fitting operation is performed based on a layer perceptron neural network, a surface fitting operation is performed based on the operating data set to obtain a target relationship model between the pitch angle and the output power of the wind turbine, including: converting the target power prediction model into a target relationship model between the pitch angle and the output power based on preset conversion software, wherein the target power prediction model is obtained by performing model training on the operating data set based on a multi-layer perceptron neural network.

[0011] Further, the target power prediction model is obtained by model training the operating data set based on a multilayer perceptron neural network in the following manner: randomly dividing the operating data set into a training data set and a test data set; constructing an initial power prediction model based on a multilayer perceptron neural network; based on the initial power prediction model, predicting a first predicted output power according to the wind speed data and the pitch angle data in the training data set; calculating a first loss function between the first predicted output power and the actual output power in the training data set, and updating the model parameters of the initial power prediction model based on the first loss function based on the back propagation algorithm until a preset update stop condition is met to obtain a power prediction model to be evaluated; based on the power prediction model to be evaluated, predicting a second predicted output power according to the wind speed data and the pitch angle data in the test data set; calculating a second loss function between the second predicted output power and the actual output power in the test data set, and determining whether the second loss function is less than a preset loss function threshold; if less than, determining the output power prediction model to be evaluated as the target power prediction model; if not less than, adjusting the model parameters of the power prediction model to be evaluated based on the back propagation algorithm according to the second loss function to obtain a target power prediction model.

[0012] Furthermore, when the surface fitting operation is performed based on the least squares method, the surface fitting operation is performed based on the operating data set to obtain a target relationship model between the pitch angle and the output power of the wind turbine set, including: constructing an initial relationship model between the pitch angle and the output power of the wind turbine set based on a quadratic function; based on the initial relationship model, calculating a third predicted output power according to the pitch angle data in the operating data set; calculating a third loss function between the third predicted output power and the output power in the operating data set; solving the parameters of the initial relationship model based on the least squares method, and obtaining the target relationship model between the pitch angle and the output power of the wind turbine set when the third loss function meets the minimum value.

[0013] Furthermore, before obtaining the operating data set of the wind turbine set within the target wind speed range, it also includes: obtaining the operating data of the wind turbine set's operating parameters in which the wind speed is below a preset wind speed and the pitch angle is within a preset angle range; segmenting the operating data based on multiple preset wind speed ranges to obtain operating data sets within multiple wind speed ranges.

[0014] Furthermore, before the operating data is segmented and processed based on multiple preset wind speed intervals to obtain operating data sets within multiple wind speed intervals, it also includes: dividing the operating data into multiple data points based on a preset pitch angle step; performing linear fitting on the multiple data points to obtain a linear model between the output power and the wind speed data and the pitch angle data; based on the linear model, calculating the fourth predicted output power of each data point in the multiple data points; determining whether there is a first data point in the multiple data points whose difference between the actual output power and the fourth predicted output power is greater than a preset difference threshold; if so, removing the first data point from the multiple data points to obtain cleaned operating data.

[0015] In a second aspect, the present application provides a device for determining an optimal pitch angle of a wind turbine generator set, the device comprising:

[0016] A data acquisition module is used to acquire an operating data set of a wind turbine within a target wind speed range;

[0017] A model determination module, configured to perform a surface fitting operation based on the operating data set to obtain a target relationship model between the pitch angle and output power of the wind turbine generator set;

[0018] The optimal value determination module is used to solve the target relationship model to obtain the optimal pitch angle of the wind turbine generator set within the target wind speed range, so as to maximize the output power of the wind turbine generator set.

[0019] In a third aspect, the present application provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining the optimal pitch angle of a wind turbine set described in any embodiment of the present application.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining the optimal pitch angle of a wind turbine set described in any embodiment of the present application when executed.

[0021] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for determining the optimal pitch angle of a wind turbine set described in any embodiment of the present application.

[0022] In order to solve the defects of the prior art in the background technology, an embodiment of the present application provides a method for determining the optimal pitch angle of a wind turbine. Execution of this method can bring the following beneficial effects: the present application first obtains an operating data set of the wind turbine in a target wind speed range, and then performs surface fitting based on the data set to obtain a relationship model between the pitch angle and the output power, and then solves the relationship target relationship model based on the gradient ascent method to obtain the optimal pitch angle that maximizes the output power of the wind turbine in the target wind speed range, which can reduce the complexity of the calculation process for tuning the control parameters of the wind turbine and improve the efficiency of determining the optimal pitch angle of the wind turbine.

[0023] It should be noted that the above-mentioned computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer-readable storage medium may be packaged together with the processor of the device for determining the optimal pitch angle of a wind turbine generator set, or may be packaged separately from the processor of the device for determining the optimal pitch angle of a wind turbine generator set, and this application does not limit this.

[0024] The description of the second, third, ..., and fifth aspects in this application can refer to the detailed description of the first aspect; and the beneficial effects of the description of the second, third, ..., and fifth aspects can refer to the beneficial effect analysis of the first aspect, which will not be repeated here.

[0025] 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 application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description.

[0026] It is understandable that before using the technical solutions disclosed in the embodiments of this application, the type, scope of use, and usage scenarios of the personal information involved in this application should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1A schematic flow chart of a method for determining an optimal pitch angle of a wind turbine provided in an embodiment of the present application;

[0029] Figure 2 A schematic diagram of the structure of a device for determining an optimal pitch angle of a wind turbine provided in an embodiment of the present application;

[0030] Figure 3 It is a block diagram of an electronic device used to implement a method for determining an optimal pitch angle of a wind turbine generator set according to an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0032] It should be noted that the terms "first", "second", "target", and "original" in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including", "having", and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0033] Figure 1 A flow chart of a method for determining an optimal pitch angle of a wind turbine provided in an embodiment of the present application, the present embodiment can be applied to the case where the pitch angle in the control parameters of the wind turbine is optimized during wind power generation. The method for determining an optimal pitch angle of a wind turbine provided in the present embodiment can be executed by a device for determining an optimal pitch angle of a wind turbine provided in an embodiment of the present application, which can be implemented by software and / or hardware and integrated in an electronic device that executes the method. Preferably, the electronic device in the embodiment of the present application can be a microprocessor in a wind turbine, or can be an edge processing device independent of the main control system of the wind turbine.

[0034] See also Figure 1 The method of this embodiment includes but is not limited to the following steps:

[0035] S110, obtaining an operating data set of the wind turbine generator system within a target wind speed range.

[0036] Among them, a wind turbine, i.e. a wind power generator, is a device that converts wind energy into mechanical energy through a wind rotor, and then converts the mechanical energy into electrical energy through a generator. The operating data set is the relevant operating parameters of the wind turbine in a certain wind speed range, which may include wind speed, output power, pitch angle, temperature, etc. The target wind speed range is a user-defined wind speed range; it can also be multiple wind speed ranges, so as to achieve the purpose of determining the optimal pitch angle of the wind turbine in different wind speed ranges; specifically, the wind speed can be divided into multiple different wind speed ranges, and then the output power and pitch angle in different wind speed ranges are recorded separately. Each operating data in the operating data set may include wind speed data, pitch angle data, and output power data.

[0037] In an embodiment of the present application, the operating data set of the wind turbine can be obtained from the wind turbine manufacturer or operator, can be collected using a wind farm monitoring system, can be obtained from a public database for academic research, or can be obtained by collecting data from the wind turbine using the above-mentioned edge processing equipment.

[0038] Exemplary, the specific methods of using the wind farm monitoring system to collect the operating data set of wind turbines can be: 1. Obtain through the data acquisition platform: Many wind farms are equipped with advanced data acquisition and monitoring systems (SCADA for short), which will record various operating parameters of wind turbines in real time, including wind speed, output power, temperature, etc. By accessing these data acquisition platforms, the operating data within the target wind speed range is extracted. 2. Obtain through edge processing devices: Install additional sensors on the wind turbines, which can be connected to edge processing devices. The edge processing devices monitor the changes of specific parameters within the target wind speed range through sensors, and record and store data in real time.

[0039] S120: Perform a surface fitting operation based on the operating data set to obtain a target relationship model between the pitch angle and output power of the wind turbine generator set.

[0040] Among them, the pitch angle refers to the angle between the blade chord and the relative wind speed. The target relationship model is a mathematical relationship model used to represent the pitch angle and output power of the wind turbine in the target wind speed range. The mathematical relationship model can be used to determine the optimal pitch angle when the wind turbine output power is maximum in the target wind speed range. The surface fitting operation can be completed based on the least squares method or the multi-layer perceptron neural network algorithm.

[0041] In an achievable embodiment, when performing a surface fitting operation based on a layer perceptron neural network, performing a surface fitting operation based on an operating data set to obtain a target relationship model between a pitch angle and output power of a wind turbine generator system includes: converting a target power prediction model into a target relationship model between a pitch angle and output power based on a preset conversion software, wherein the target power prediction model is obtained by model training the operating data set based on a multi-layer perceptron neural network. Optionally, the preset conversion software may use MATLAB software.

[0042] Further, the target power prediction model is obtained by model training the operating data set based on the multilayer perceptron neural network in the following manner: first, the operating data set is randomly divided into a training data set and a test data set. Second, an initial power prediction model is constructed based on the multilayer perceptron neural network, wherein the initial output power prediction model includes two neurons in the input layer (corresponding to wind speed and pitch angle), three neurons in the hidden layer and one neuron in the output layer (corresponding to power), an activation function, etc. Third, based on the initial power prediction model, the output power is predicted according to the wind speed data and the pitch angle data in the training data set, which is recorded as the first predicted output power. Fourth, the first loss function between the first predicted output power and the actual output power in the training data set is calculated, and the model parameters of the initial power prediction model are updated based on the back propagation algorithm according to the first loss function until the preset update stop condition is met, and the power prediction model to be evaluated is obtained. Fifth, in order to avoid overfitting, the power prediction model to be evaluated is verified based on the test data set, specifically: based on the power prediction model to be evaluated, the output power is predicted according to the wind speed data and the pitch angle data in the test data set, which is recorded as the second predicted output power. Sixth, calculate the second loss function between the second predicted output power and the actual output power in the test data set, and determine whether the second loss function is less than a preset loss function threshold; if it is less than, determine the output power prediction model to be evaluated as the target power prediction model; if it is not less than, adjust the model parameters of the power prediction model to be evaluated based on the back propagation algorithm according to the second loss function to obtain the target power prediction model.

[0043] Optionally, mean square error can be used as the loss function. The update stop condition can be that the number of iterations meets a preset iteration threshold, or that the degree of fit meets a preset requirement, such as greater than 95%.

[0044] In another achievable embodiment, when performing a surface fitting operation based on the least squares method, the surface fitting operation is performed based on the running data set to obtain a target relationship model between the pitch angle and the output power of the wind turbine generator set, including: first, constructing an initial relationship model between the pitch angle and the output power of the wind turbine generator set based on a quadratic function, and the initial relationship model can be expressed as the following formula (1):

[0045] p=f(β,λ)=λ 1 β 2 +λ 2 β+λ 3 (1)

[0046] In the formula, p represents power data, β represents pitch angle data, and λ 1 , 2 , 3 Represents the parameters of the relationship function. Second, based on the initial relationship model, the output power is calculated according to the pitch angle data in the operating data set, which is recorded as the third predicted output power. Third, a third loss function is calculated between the third predicted output power and the output power in the operating data set; optionally, the residual sum of squares can be used as the loss function, which is expressed as the following formula (2):

[0047]

[0048] In the formula, p i represents the output power data in the operating data set within the target wind speed range, i is the index number of the data in the operating data set, the number of operating data is n, f(β,λ) represents the third predicted output power data; L represents the third loss function. Fourth, the parameters of the initial relationship model are solved based on the least squares method, and when the third loss function meets the minimum value, the target relationship model between the pitch angle and the output power of the wind turbine is obtained.

[0049] S130, solving the target relationship model to obtain the optimal pitch angle of the wind turbine generator set within the target wind speed range, so as to maximize the output power of the wind turbine generator set.

[0050] Specifically, solving the target relationship model to obtain the optimal pitch angle of the wind turbine in the target wind speed range includes: determining the initial pitch angle, iteration step and initial gradient of the wind turbine; constructing a pitch angle iteration function for the wind turbine based on the target relationship model; using the gradient ascent method to update the initial pitch angle based on the pitch angle iteration function through the initial gradient and iteration step until the preset iteration stop condition is met, thereby obtaining the optimal pitch angle of the wind turbine in the target wind speed range.

[0051] This embodiment uses the gradient ascent method to solve the optimal pitch angle, which can improve the solution efficiency. The pitch angle iteration function is expressed by the following formula (3):

[0052] β k+1 =β k +α k ▽f(β k ),f(β k+1 )>f(β k ) (3)

[0053] In the formula, β k represents the pitch angle at the kth iteration, β k+1 represents the pitch angle at the k+1th iteration, α k represents the iteration step length, ▽f(β k ) represents the gradient at the k-th iteration. Specifically, the process of the gradient ascent method to solve the optimal solution of the relationship function is as follows: 1 , β 1 , ...β k →β, where β k Indicates the k-th iteration, the expected update β k+1 , and satisfies f(β k+1 )>f(β k ).

[0054] The initial gradient can be calculated based on the numerical differentiation method. The iteration stop condition can be that the gradient is less than a preset gradient threshold, or the number of iterations reaches a preset iteration threshold, or the change value of the pitch angle is less than a preset change threshold, etc., which is not specifically limited in the embodiments of the present application.

[0055] Optionally, the optimal pitch angles of the wind turbine generator set in different wind speed ranges may be determined based on the methods of steps S110 to S130 of the embodiment.

[0056] The technical solution provided in this embodiment obtains an operating data set of the wind turbine in the target wind speed range; performs a surface fitting operation based on the operating data set to obtain a target relationship model between the pitch angle and the output power of the wind turbine; and solves the target relationship model to obtain the optimal pitch angle of the wind turbine in the target wind speed range, so as to maximize the output power of the wind turbine. Compared with the prior art, the present application first obtains an operating data set of the wind turbine in the target wind speed range, then performs a surface fitting operation based on the data set to obtain a relationship model between the pitch angle and the output power, and then solves the relationship target relationship model based on the gradient ascent method to obtain the optimal pitch angle that maximizes the output power of the wind turbine in the target wind speed range, thereby reducing the complexity of the calculation process for tuning the control parameters of the wind turbine and improving the efficiency of determining the optimal pitch angle of the wind turbine.

[0057] In an optional embodiment, when the wind speed is low, a smaller pitch angle helps capture more wind energy; and when the wind speed is high, a larger pitch angle can reduce the capture of wind energy and avoid overloading the unit. Therefore, the optimal pitch angle will be different at different wind speeds. In order to improve the accuracy of determining the optimal pitch angle, before obtaining the operating data set of the wind turbine in the target wind speed range, it also includes: selecting appropriate standard operating data, and then processing it to obtain the operating data set.

[0058] The specific process is: obtaining the operating data of the wind turbine operating parameters, including wind speed data, pitch angle data, output power data, etc., when the wind speed is below the preset wind speed (such as 25m / s) and the pitch angle is within the preset angle range (such as 0-5°); dividing the selected wind speed into multiple preset wind speed intervals, and based on the multiple preset wind speed intervals, segmenting the operating data according to the wind speed data in the operating data to obtain operating data sets within multiple wind speed intervals.

[0059] Furthermore, before the operating data is segmented according to the wind speed data in the operating data, it also includes: cleaning the operating data, and the specific process is: first, dividing the operating data into multiple data points based on the preset pitch angle step. For example, according to the pitch angle step of 0.5°, the data points of wind speed data and output power data under different pitch angle conditions are obtained. Second, linear fitting is performed on multiple data points to obtain a linear model between the output power and the wind speed data and the pitch angle data. The linear model can be expressed as P=f(vi)+f(β 0 ), where P represents power, v i represents the wind speed data under different pitch angle conditions, β 0 Represents the pitch angle condition obtained according to the set step size of 0.5°. Third, the wind speed data and pitch angle data in the data point are input into the above-mentioned linear model, and based on the linear model, the output power of each data point in the multiple data points is calculated, which is recorded as the fourth predicted output power. Fourth, the difference between the actual output power of each data point and the fourth predicted output power is calculated to determine whether there is a first data point in the multiple data points where the difference between the actual output power and the fourth predicted output power is greater than the preset difference threshold; if so, it means that the first data point deviates from the overall level, then the first data point is removed from the multiple data points to obtain the cleaned operating data. That is, among the multiple data points, the data points where the difference between the actual output power and the fourth predicted output power is not greater than the preset difference threshold are retained.

[0060] It should be noted that before cleaning the operation data, the operation data may be preliminarily cleaned according to a preset specified range. Exemplarily, the preset specified range is as shown in Table 1.

[0061] Table 1 Preset range of wind turbine operating data

[0062] Parameter name Scope of regulations Wind speed 0<v<25m / s power 0<p<1.5MW Speed 0<omega<20rpm

[0063] Figure 2 A schematic diagram of the structure of a device for determining an optimal pitch angle of a wind turbine provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the device 200 may include:

[0064] The data acquisition module 210 is used to acquire the operating data set of the wind turbine generator set within the target wind speed range;

[0065] A model determination module 220, configured to perform a surface fitting operation based on the operating data set to obtain a target relationship model between the pitch angle and output power of the wind turbine generator set;

[0066] The optimal value determination module 230 is used to solve the target relationship model to obtain the optimal pitch angle of the wind turbine generator set within the target wind speed range, so as to maximize the output power of the wind turbine generator set.

[0067] Furthermore, the above-mentioned optimal value determination module 230 can be specifically used to: determine the initial pitch angle, iteration step and initial gradient of the wind turbine group; construct a pitch angle iteration function for the wind turbine group based on the target relationship model; based on the pitch angle iteration function, update the initial pitch angle through the initial gradient and the iteration step until the preset iteration stop condition is met, so as to obtain the optimal pitch angle of the wind turbine group in the target wind speed range.

[0068] Furthermore, the above-mentioned model determination module 220 can be specifically used for: when the surface fitting operation is performed based on a layer perceptron neural network, the target power prediction model is converted into a target relationship model between the pitch angle and the output power based on a preset conversion software, wherein the target power prediction model is obtained by model training the operating data set based on a multi-layer perceptron neural network.

[0069] In one embodiment, the target power prediction model is obtained by model training the operating data set based on a multilayer perceptron neural network in the following manner: randomly dividing the operating data set into a training data set and a test data set; constructing an initial power prediction model based on a multilayer perceptron neural network; based on the initial power prediction model, predicting a first predicted output power according to the wind speed data and the pitch angle data in the training data set; calculating a first loss function between the first predicted output power and the actual output power in the training data set, and updating the model parameters of the initial power prediction model based on the first loss function based on the back propagation algorithm until a preset update stop condition is met to obtain a power prediction model to be evaluated; based on the power prediction model to be evaluated, predicting a second predicted output power according to the wind speed data and the pitch angle data in the test data set; calculating a second loss function between the second predicted output power and the actual output power in the test data set, and determining whether the second loss function is less than a preset loss function threshold; if less than, determining the output power prediction model to be evaluated as the target power prediction model; if not less than, adjusting the model parameters of the power prediction model to be evaluated based on the back propagation algorithm according to the second loss function to obtain a target power prediction model.

[0070] Furthermore, the above-mentioned model determination module 220 can also be specifically used for: when the surface fitting operation is performed based on the least squares method, constructing an initial relationship model between the pitch angle and the output power of the wind turbine based on a quadratic function; based on the initial relationship model, calculating the third predicted output power according to the pitch angle data in the operating data set; calculating the third loss function between the third predicted output power and the output power in the operating data set; solving the parameters of the initial relationship model based on the least squares method, and when the third loss function meets the minimum value, obtaining the target relationship model between the pitch angle and the output power of the wind turbine.

[0071] Furthermore, the above-mentioned device for determining the optimal pitch angle of a wind turbine generator set may further include: a data preprocessing module;

[0072] The data preprocessing module is used to obtain the operating data of the wind turbine set in the operating parameters when the wind speed is below the preset wind speed and the pitch angle is within the preset angle range before obtaining the operating data set of the wind turbine set in the target wind speed range; and to segment the operating data based on multiple preset wind speed ranges to obtain the operating data sets within multiple wind speed ranges.

[0073] Furthermore, the data preprocessing module is also used to segment the operating data based on multiple preset wind speed intervals to obtain operating data sets within multiple wind speed intervals, and divide the operating data into multiple data points based on a preset pitch angle step; perform linear fitting on the multiple data points to obtain a linear model between the output power and the wind speed data and the pitch angle data; calculate the fourth predicted output power of each of the multiple data points based on the linear model; determine whether there is a first data point among the multiple data points, the difference between the actual output power and the fourth predicted output power is greater than a preset difference threshold; if so, remove the first data point from the multiple data points to obtain cleaned operating data.

[0074] The device for determining the optimal pitch angle of a wind turbine provided in this embodiment can be applied to the method for determining the optimal pitch angle of a wind turbine provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0075] Figure 3 It is a block diagram of an electronic device for implementing a method for determining the optimal pitch angle of a wind turbine set according to an embodiment of the present application. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can 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 application described and / or required herein.

[0076] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and 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 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0077] A number of 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 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 through a computer network such as the Internet and / or various telecommunication networks.

[0078] The processor 11 may be a variety of general and / or special processing components 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 special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for determining the optimal pitch angle of a wind turbine.

[0079] In some embodiments, the method for determining the optimal pitch angle of a wind turbine can 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 can 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 optimal pitch angle of the wind turbine described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the method for determining the optimal pitch angle of the wind turbine in any other appropriate manner (for example, by means of firmware).

[0080] Various embodiments of the systems and techniques described above 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), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including 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.

[0081] The computer programs for implementing the methods of the present application 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, so that when the computer programs are executed by the processor, the functions / operations specified in the flow charts and / or block diagrams are implemented. The computer programs may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0082] In the context of the present application, a computer readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. Alternatively, a computer readable storage medium may be a machine readable signal medium. A more specific example of a machine readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0083] To provide interaction with a user, the systems and techniques described herein may 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 a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0084] The systems and techniques described herein can be implemented in a computing system that includes backend 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 frontend components (e.g., a user computer with a graphical user interface or a 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 backend components, middleware components, or frontend 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.

[0085] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0086] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. For example, those skilled in the art can use the various forms of processes shown above to reorder, add, or delete steps; the steps recorded in the present application can be performed in parallel, sequentially, or in different orders, as long as the desired results of the technical solution of the present application can be achieved, and this document does not limit them here.

[0087] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for determining an optimal pitch angle of a wind turbine generator system, characterized in that: The method comprises: Obtaining the operating data set of the wind turbine in the target wind speed range; Performing a surface fitting operation based on the operating data set to obtain a target relationship model between the pitch angle and output power of the wind turbine generator set; The target relationship model is solved to obtain the optimal pitch angle of the wind turbine generator set within the target wind speed range, so as to maximize the output power of the wind turbine generator set.

2. The method for determining the optimal pitch angle of a wind turbine according to claim 1, characterized in that: Solving the target relationship model to obtain the optimal pitch angle of the wind turbine generator set within the target wind speed range includes: Determining an initial pitch angle, an iteration step size and an initial gradient of the wind turbine generator set; Constructing a pitch angle iteration function for the wind turbine generator system based on the target relationship model; Based on the pitch angle iteration function, the initial pitch angle is updated through the initial gradient and the iteration step size until a preset iteration stop condition is met, thereby obtaining the optimal pitch angle of the wind turbine in the target wind speed range.

3. The method for determining the optimal pitch angle of a wind turbine according to claim 1, characterized in that: When the surface fitting operation is performed based on the layer perceptron neural network, the surface fitting operation is performed based on the running data set to obtain a target relationship model between the pitch angle and the output power of the wind turbine generator set, including: The target power prediction model is converted into a target relationship model between pitch angle and output power based on the preset conversion software. The target power prediction model is obtained by training the operating data set based on a multi-layer perceptron neural network.

4. The method for determining the optimal pitch angle of a wind turbine according to claim 3, characterized in that: The target power prediction model is obtained by performing model training on the operating data set based on a multi-layer perceptron neural network in the following manner: Randomly divide the running data set into a training data set and a test data set; Constructing an initial power prediction model based on a multi-layer perceptron neural network; Based on the initial power prediction model, predicting a first predicted output power according to the wind speed data and the pitch angle data in the training data set; Calculating a first loss function between the first predicted output power and the actual output power in the training data set, and updating the model parameters of the initial power prediction model based on the back propagation algorithm according to the first loss function until a preset update stop condition is met, thereby obtaining a power prediction model to be evaluated; Based on the power prediction model to be evaluated, predicting a second predicted output power according to the wind speed data and the pitch angle data in the test data set; Calculating a second loss function between the second predicted output power and the actual output power in the test data set, and determining whether the second loss function is less than a preset loss function threshold; If it is less than, determining the output power prediction model to be evaluated as the target power prediction model; If it is not less than, the model parameters of the power prediction model to be evaluated are adjusted based on the back propagation algorithm according to the second loss function to obtain the target power prediction model.

5. The method for determining the optimal pitch angle of a wind turbine according to claim 1, characterized in that: When the surface fitting operation is performed based on the least squares method, the surface fitting operation is performed based on the operating data set to obtain a target relationship model between the pitch angle and the output power of the wind turbine generator set, including: Constructing an initial relationship model between the pitch angle and output power of the wind turbine generator system based on a quadratic function; Based on the initial relationship model, calculating a third predicted output power according to the pitch angle data in the operating data set; calculating a third loss function between the third predicted output power and the output power in the operating data set; The parameters of the initial relationship model are solved based on the least square method, and when the third loss function satisfies the minimum value, the target relationship model between the pitch angle and the output power of the wind turbine generator set is obtained.

6. The method for determining the optimal pitch angle of a wind turbine according to claim 1, characterized in that: Before obtaining the operating data set of the wind turbine in the target wind speed range, it also includes: Acquiring operating data of the wind turbine generator set in which the wind speed is below a preset wind speed and the pitch angle is within a preset angle range; The operation data is segmented based on a plurality of preset wind speed intervals to obtain operation data sets within a plurality of wind speed intervals.

7. The method for determining the optimal pitch angle of a wind turbine according to claim 6, characterized in that: Before segmenting the operating data based on a plurality of preset wind speed intervals to obtain operating data sets within a plurality of wind speed intervals, the method further includes: Dividing the operating data into a plurality of data points based on a preset pitch angle step; Performing linear fitting on the multiple data points to obtain a linear model between the output power and the wind speed data and the pitch angle data; Calculating a fourth predicted output power for each of the plurality of data points based on the linear model; Determine whether there is a first data point among the plurality of data points, where the difference between the actual output power and the fourth predicted output power is greater than a preset difference threshold; If so, the first data point is removed from the multiple data points to obtain cleaned operation data.

8. A device for determining the optimal pitch angle of a wind turbine, characterized in that: The device comprises: A data acquisition module is used to acquire an operating data set of a wind turbine within a target wind speed range; A model determination module, configured to perform a surface fitting operation based on the operating data set to obtain a target relationship model between the pitch angle and output power of the wind turbine generator set; The optimal value determination module is used to solve the target relationship model to obtain the optimal pitch angle of the wind turbine generator set within the target wind speed range, so as to maximize the output power of the wind turbine generator set.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining the optimal pitch angle of a wind turbine set according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining the optimal pitch angle of a wind turbine set according to any one of claims 1 to 7 when executed.

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