Offshore wind turbine generator yaw prediction control method, system and device and medium

Abstract

The invention discloses an offshore wind turbine generator yaw prediction control method, system and device and a medium, and the method comprises the steps: processing historical wind direction data through employing a multi-time scale average value method, obtaining a plurality of groups of average wind direction data, building an Attention-CNN-LSTM short-time wind direction prediction model, and an offshore wind turbine generator yaw prediction control system is established according to the model, when the wind speed exceeds the starting wind speed, the system starts to operate and monitors the current wind direction, the system predicts the average wind direction angle of the next time period according to the current wind direction, and then the system executes the corresponding yaw action in combination with the cabin position at the current moment. By introducing a self-attention mechanism, the accuracy of model prediction is improved, and meanwhile, a yaw prediction control system is designed in combination with the model, so that the system rapidly tracks wind direction changes, yaw errors are reduced, excessive yaw is avoided, and yaw efficiency is improved.

Description

Technical Field

[0001] The invention relates to the field of wind direction prediction models, and in particular to a method, system, device and medium for predicting and controlling the yaw of an offshore wind turbine. Background Art

[0002] Under the background of the dual carbon goals, the installed capacity of renewable energy such as wind power is gradually expanding. As a potential clean energy, offshore wind power has shown unprecedented vitality and is expected to become one of the important directions for the development of new energy in the future. In terms of yaw control of wind turbines, its main purpose is to make the wind turbine always follow the wind direction when the wind direction changes, so as to maximize the acquisition of wind energy and increase the output power of the unit.

[0003] However, the current traditional yaw control method mainly depends on the historical average wind direction data measured by the unit, and its value only reflects the wind direction changes over the past period of time, and is used to determine whether the current wind direction has changed. Due to the time delay between the yaw system and the wind direction, the traditional yaw control system has insufficient yaw accuracy and large yaw error in yaw-to-wind, and the yaw lags behind the wind direction, which causes frequent yaw control and long yaw time. The traditional yaw control method cannot meet the power requirements of wind turbines and reduces the power generation efficiency. With the proposal and improvement of prediction algorithms, current studies have proposed a yaw control method based on wind direction prediction in combination with wind direction prediction. The use of deep learning models such as BP neural networks, convolutional neural networks, and artificial neural networks to predict wind direction has achieved the prediction needs of future wind direction trends, but the model structure used is often relatively simple, the prediction accuracy is insufficient, and it is difficult to apply to actual yaw control systems. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a method, system, device and medium for predicting and controlling the yaw of an offshore wind turbine, which improves the accuracy and speed of yaw prediction and is applied to an actual yaw control system of an offshore wind turbine.

[0005] In a first aspect, an embodiment of the present invention provides a method for predicting and controlling the yaw of an offshore wind turbine, the control method comprising:

[0006] According to the multiple groups of average wind direction data, an Attention-CNN-LSTM short-term wind direction prediction model is established;

[0007] According to the Attention-CNN-LSTM short-term wind direction prediction model, a yaw prediction control system for offshore wind turbines is established;

[0008] When the wind speed exceeds the starting wind speed, the offshore wind turbine yaw prediction control system starts to operate and monitors the current wind direction;

[0009] Predicting the average wind direction angle for the next period of time according to the current wind direction and the Attention-CNN-LSTM short-term wind direction prediction model;

[0010] According to the average wind direction angle in the next period and the nacelle position at the current moment, a corresponding yaw action is performed.

[0011] In some embodiments of the present invention, the step of obtaining historical wind direction data and processing the historical wind direction data using a multi-time scale average method to obtain multiple groups of average wind direction data includes:

[0012] Setting multiple time scales, and averaging the historical wind direction data according to each time scale to obtain multiple groups of average wind direction data corresponding to the multiple time scales;

[0013] A sliding window rolling processing method is adopted to process the multiple groups of average wind direction data respectively to obtain multiple groups of training data;

[0014] Normalization is performed on the multiple groups of training data respectively to obtain multiple groups of standardized training data.

[0015] In some embodiments of the present invention, the plurality of groups of average wind direction data include ten-second average wind direction data, twenty-second average wind direction data, and thirty-second average wind direction data;

[0016] The calculation formula of the average wind direction data is:

[0017]

[0018] in, It is expressed as the N-second average wind direction data at the i-th moment, Expressed as the average wind direction j seconds before the i-th moment; i and j are positive integers; N is 10, 20 or 30.

[0019] In some embodiments of the present invention, the Attention-CNN-LSTM short-term wind direction prediction model includes a convolutional neural network, a long short-term memory network, and a self-attention mechanism layer;

[0020] The step of establishing a yaw prediction control system according to the Attention-CNN-LSTM short-term wind direction prediction model includes:

[0021] Input each group of the standardized training data into the convolutional neural network respectively;

[0022] The convolutional neural network extracts feature data of each group of the standardized training data, expands the feature data into a one-dimensional sequence, and sends it to the long short-term memory network;

[0023] After the long short-term memory network performs prediction processing on the one-dimensional sequence, the prediction data is output to the self-attention mechanism layer;

[0024] The self-attention mechanism layer obtains an output result based on the predicted data.

[0025] In some embodiments of the present invention, the step of performing a corresponding yaw action according to the average wind direction angle in the next period and the nacelle position at the current moment includes:

[0026] Determine the absolute value of the error angle between the average wind direction angle in the next period and the current position of the nacelle;

[0027] When the absolute value of the error angle is greater than a preset angle value within a preset time period, a yaw action is performed.

[0028] In some embodiments of the present invention, the average wind direction angle includes a ten-second average wind direction angle, a twenty-second average wind direction angle, and a thirty-second average wind direction angle; the preset time period includes a first preset time period, a second preset time period, and a third preset time period; the step of performing a corresponding yaw action according to the average wind direction angle of the next time period and the cabin position at the current moment includes:

[0029] When the absolute value of the ten-second average wind direction angle in the next period and the error angle of the cabin position at the current moment are both greater than the preset angle value within the first preset time period, a yaw action is performed; or,

[0030] When the absolute value of the twenty-second average wind direction angle in the next period and the error angle of the cabin position at the current moment are both greater than the preset angle value within the second preset time period, a yaw action is performed; or,

[0031] When the thirty-second average wind direction angle in the next period and the absolute value of the error angle of the cabin position at the current moment are both greater than the preset angle value within the third preset time period, a yaw action is performed.

[0032] In some embodiments of the present invention, the yaw action includes forward yaw and reverse yaw; when the error angle is greater than or equal to 1 degree, forward yaw is performed; when the error angle is less than or equal to -1 degree, reverse yaw is performed.

[0033] In a second aspect, an embodiment of the present invention provides an offshore wind turbine yaw prediction control system, including the offshore wind turbine yaw prediction control method as described in the first aspect above.

[0034] In a third aspect, an embodiment of the present invention provides a control device, comprising at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions executable by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can operate the offshore wind turbine yaw prediction control system as described in the second aspect above.

[0035] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the offshore wind turbine yaw prediction control method as described in the first aspect above.

[0036] The yaw prediction control method, system, device and medium according to the embodiments of the present invention have at least the following beneficial effects:

[0037] By introducing the self-attention mechanism, the advantages of CNN (convolutional neural network) and LSTM (long short-term memory network) in time series prediction are fully utilized. The performance of model prediction and the efficiency of learning complex features are improved, meeting the dual needs of short-term rapid prediction and prediction accuracy. Further, the yaw prediction control system of offshore wind turbines is designed in combination with the Attetion-CNN-LSTM (CNN-LSTM network based on attention mechanism) short-term wind direction prediction model. The main goal is to enable the yaw system to quickly track wind direction changes, minimize yaw errors, avoid excessive use of yaw actuators, and improve the yaw efficiency of wind turbines. Solve the problems of insufficient wind accuracy and frequent yaw actions of traditional offshore wind turbine yaw prediction control systems, and achieve high-efficiency power generation of wind turbines. The yaw prediction control system of offshore wind turbines predicts multiple average wind direction angles, and sets multiple corresponding yaw conditions for multiple average wind direction angles to determine whether to perform yaw actions, increase the number of judgments, reduce yaw errors, effectively avoid frequent yaws caused by short-term fluctuations in wind direction, and enhance the stability and reliability of the yaw prediction control system.

[0038] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0040] Figure 1 A flowchart of a yaw prediction control method provided by one embodiment of the present invention;

[0041] Figure 2 A structural diagram of a control device provided in another embodiment of the present invention;

[0042] Figure 3 This is the 10s average wind direction forecast result chart;

[0043] Figure 4 This is the 20s average wind direction forecast result diagram;

[0044] Figure 5 This is the 30s average wind direction forecast result chart;

[0045] Figure 6 This is a comparison chart of the nacelle position for traditional yaw control and yaw control combined with wind direction prediction;

[0046] Figure 7 It is a comparison diagram of the nacelle position without wind direction prediction yaw control and with wind direction prediction yaw control;

[0047] Figure 8 This is the total yaw error result diagram of the three control systems;

[0048] Fig. 9 This is the result diagram of total yaw action times of three control systems;

[0049] Fig.10 It is the total yaw time result diagram of the three control systems. DETAILED DESCRIPTION

[0050] This section will describe in detail the specific embodiments of the present invention. The preferred embodiments of the present invention are shown in the accompanying drawings. The purpose of the accompanying drawings is to supplement the description of the text part of the specification with graphics, so that people can intuitively and vividly understand each technical feature and the overall technical solution of the present invention, but it cannot be understood as a limitation on the scope of protection of the present invention.

[0051] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0052] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0053] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0054] The present invention provides a yaw prediction control method, which fully utilizes the advantages of CNN and LSTM in time series prediction by introducing a self-attention mechanism. The performance of model prediction and the efficiency of learning complex features are improved, and the dual requirements of short-term rapid prediction and prediction accuracy are met. Further, a yaw prediction control system is designed in combination with the Attetion-CNN-LSTM short-term wind direction prediction model. The main goal is to enable the yaw system to quickly track wind direction changes, minimize yaw errors, avoid excessive use of yaw actuators, and improve the yaw efficiency of wind turbines. The problem of insufficient wind accuracy and frequent yaw actions of traditional yaw systems is solved, and high-efficiency power generation of wind turbines is achieved.

[0055] The method of the embodiment of the present invention is further described below based on the accompanying drawings.

[0056] like Figure 1 As shown, the yaw prediction control method of the present invention includes:

[0057] Step S100: acquiring historical wind direction data, and processing the historical wind direction data using a multi-time scale average method to obtain multiple groups of average wind direction data;

[0058] Step S200: establishing an Attention-CNN-LSTM short-term wind direction prediction model based on multiple groups of average wind direction data;

[0059] Step S300: establishing a yaw prediction control system according to the Attention-CNN-LSTM short-term wind direction prediction model;

[0060] Step S400: When the wind speed exceeds the start wind speed, the yaw prediction control system starts to run and monitor the current wind direction;

[0061] Step S500: predicting the average wind direction angle for the next period based on the current wind direction and the Attention-CNN-LSTM short-term wind direction prediction model;

[0062] Step S600: Execute corresponding yaw action according to the average wind direction angle of the next period and the current cabin position.

[0063] The execution of the yaw action ensures that the nacelle is always in the appropriate direction to meet the power requirements of the wind turbine.

[0064] It should be noted that the multiple time scales include ten seconds, twenty seconds and thirty seconds. The time scale is selected to calculate the average wind direction data according to the accuracy of the prediction model. The multi-time scale average method is used to process the historical wind direction data. The processed wind direction data is smoother and more continuous, which can improve the accuracy of the model prediction. Different yaw conditions can be set for multiple time scales to increase the number of judgments and reduce yaw errors.

[0065] It should be noted that the Attention-CNN-LSTM short-term wind direction prediction model includes a convolutional neural network, a long short-term memory network and a self-attention mechanism layer. Multiple groups of average wind direction data are input into the Attention-CNN-LSTM short-term wind direction prediction model for training.

[0066] It should be noted that the system introduces a self-attention mechanism to leverage the advantages of CNN and LSTM in time series prediction, improve the model prediction performance and the efficiency of learning complex features, meet the dual needs of short-term rapid prediction and prediction accuracy, improve yaw prediction accuracy, enable the yaw system to quickly track wind direction changes, and minimize yaw errors.

[0067] It should be noted that the wind speed is generally measured by a wind speed sensor. When the wind speed exceeds the starting wind speed, the yaw prediction control system starts to run and monitor the current wind direction. The starting wind speed of the yaw prediction control system is set. When the wind speed reaches the starting wind speed, the yaw prediction control system can quickly monitor the change of wind direction and guide the equipment to perform precise wind operation to avoid energy waste and equipment wear caused by excessive yaw.

[0068] It should be noted that this process extracts wind direction features through the convolutional neural network inside the model, then models the time series through the long short-term memory network, and combines the attention mechanism to focus on important features, and finally outputs the prediction result, that is, the average wind direction angle for the next time period.

[0069] It should be noted that the average wind direction angle is a multi-time scale, and corresponds to the average wind direction data of multiple time scales, that is, the ten-second average wind direction data corresponds to the predicted corresponding ten-second average wind direction angle. At the same time, different yaw conditions are set for each different time scale to increase the number of judgments and reduce the yaw error.

[0070] Through the technical solution of this embodiment, multiple groups of average wind direction data at different time scales are used for model training to improve the prediction accuracy of the model. At the same time, multiple time scales can be used to set multiple yaw conditions, increase the number of judgments, and reduce yaw errors. In addition, by introducing the Attention-CNN-LSTM short-term wind direction prediction model, the prediction accuracy and speed are improved, so that the yaw system can quickly track wind direction changes and minimize yaw errors.

[0071] Furthermore, in some embodiments of the present invention, Figure 1 Step S100 of the illustrated embodiment also includes but is not limited to the following steps:

[0072] (1) Setting multiple time scales, averaging the historical wind direction data according to each time scale, and obtaining multiple groups of average wind direction data corresponding to the multiple time scales;

[0073] Multiple time scales can correspond to setting multiple yaw conditions, increase the number of judgments, and reduce yaw errors; multiple sets of average wind direction data increase the number of model training times and improve the accuracy of the model;

[0074] (2) Using a sliding window rolling processing method, multiple groups of average wind direction data are processed separately to obtain multiple groups of training data;

[0075] Among them, the sliding window step length is 1, and multiple groups of average wind direction data are processed into multiple groups of training data. Each group of training data is a one-dimensional matrix, including 40 sampling points of input data and 20 sampling points of output data, so that the data format is conducive to model training. It can be understood that the number of sampling points of input data and output data can also be other numbers.

[0076] (3) performing normalization processing on multiple sets of training data respectively to obtain multiple sets of standardized training data;

[0077] Normalization processing makes the distribution of data in the model more even, improves the training accuracy and speed of the training model, and improves the accuracy of model prediction.

[0078] It should be noted that the multiple time scales include ten seconds, twenty seconds and thirty seconds, and the average wind direction data include ten-second average wind direction data, twenty-second average wind direction data and thirty-second average wind direction data; the calculation formula for the average wind direction data is:

[0079]

[0080] in, It is expressed as the N-second average wind direction data at the i-th moment, Expressed as the average wind direction j seconds before the i-th moment; i and j are positive integers; N is 10, 20 or 30.

[0081] The calculation formula for normalization is:

[0082]

[0083] train norm =(train-train mean ) / train std

[0084] Among them, A ij is the data of the pth row and qth column of the training data matrix, R is the total number of rows in the training data matrix, train j-mean is the average value of the average wind direction data in the qth column, train q-std is the standard value of the average wind direction data in the qth column; train norm is the normalized training data.

[0085] Furthermore, in some embodiments of the present invention, Figure 1 Step S200 in the illustrated embodiment also includes but is not limited to the following steps:

[0086] (1) Input each set of standardized training data into the convolutional neural network respectively;

[0087] (2) The convolutional neural network extracts the feature data of each set of standardized training data, expands the feature data into a one-dimensional sequence, and sends it to the long short-term memory network;

[0088] (3) After the long short-term memory network predicts the one-dimensional sequence, the output prediction data enters the self-attention mechanism layer;

[0089] (4) The self-attention mechanism layer obtains the output result based on the predicted data.

[0090] Among them, the convolutional neural network includes a convolution layer and a pooling layer. The convolution layer uses a 2×1 convolution kernel to extract the feature data of the standardized training data and generates 10 39x1 output features; the pooling layer uses a 2×1 pooling window with a step size of 1 to pool the output features and output the pooled feature data. The pooled feature data is expanded into a 390x1 one-dimensional sequence through the flattening layer and input into the long short-term memory network; this helps to capture local features and temporal information in the data. The pooling layer further reduces the dimension and pools the feature map, reducing the redundancy of the data while retaining key information. This feature extraction method can improve the model's ability to understand and analyze wind direction data. It is understandable that the size of the convolution kernel, the size of the pooling window, and the step length can be set according to the needs of the prediction model accuracy.

[0091] The number of hidden layer units in the long short-term memory network is set to 32. The long short-term memory network predicts the one-dimensional sequence and outputs multiple 32x1 matrices X, i.e., predicted data, and inputs the predicted data into the self-attention mechanism layer; the introduction of the long short-term memory network enables the model to handle the long-term dependencies of time series data. The LSTM network predicts the one-dimensional sequence extracted by the convolutional neural network, captures the time series change characteristics in the wind direction data, and improves the time series prediction ability of the model. It is understandable that the number of hidden layer units can be set according to the accuracy of the prediction model.

[0092] The value of the layer head of the self-attention mechanism layer is set to 1, and the value of the key is set to 2; batchsize is 50, that is, each time training, 50 samples are selected in the final state for training, that is, the matrix X with a batch size of 50 is selected for training; epoch is 100, that is, all samples are trained 100 times; the initial learning rate is 0.001; the historical average wind direction data input is 40 data points, and the prediction time step is 20 data points. It is understandable that the parameters of batchsize, epoch, initial learning rate, historical average wind direction data input, and prediction time step can be set to other appropriate parameters.

[0093] In this embodiment, each matrix X will be passed through three different weight matrices W Q , W K and W V Converted into vectors Q, K and V respectively; through dot product and scaling operations, the attention score matrix a is obtained n , use the Softmax function to calculate the above attention score a n Normalize and get the weight coefficient Finally, the weight coefficient The weighted sum is taken with the value vector V to obtain the final output I;

[0094] Q=XW Q +bias Q

[0095] K=XW K +bias K

[0096] V=XW v +bias V

[0097]

[0098] Among them, n and N represent the attention score matrix a n Dimension of W Q , W K and W V is the corresponding randomly initialized weight matrix, which is updated during the training process and has a size of 2×32; X is the 32x1 input matrix; I is the 32×1 output matrix; K T is the transposed matrix of K, d k is a scaling factor whose size is the dimension of the key of the layer head of the self-attention mechanism; bias Q 、bias K and bias V is the bias matrix, both of size 2×1.

[0099] Furthermore, in some embodiments of the present invention, Figure 1 Step S600 of the illustrated embodiment also includes but is not limited to the following steps:

[0100] (1) Determine the average wind direction angle for the next period and the absolute value of the error angle of the nacelle position at the current moment;

[0101] (2) When the absolute value of the error angle is greater than the preset angle value within a preset time period, the yaw action is performed.

[0102] It should be noted that the average wind direction angle includes a ten-second average wind direction angle, a twenty-second average wind direction angle, and a thirty-second average wind direction angle; the preset time period includes a first preset time period, a second preset time period, and a third preset time period;

[0103] Furthermore, in some embodiments of the present invention, step S620 further includes:

[0104] Set the preset time period to T and the counter to T1=0;

[0105] When the absolute value of the average wind direction angle at the last moment of the next time period and the error angle of the cabin position at the current moment is greater than the preset angle, T1=T1+1, that is, T1 increases by 1; continue to predict the average wind direction angle at the next moment, when the absolute value of the predicted average wind direction angle and the error angle of the cabin position at the current moment is less than or equal to the preset angle, T1=0; when T1 and T are equal, it means that the absolute value of the average wind direction angle of the next time period and the error angle of the cabin position at the current moment are greater than the preset angle within the preset time period, and the yaw action is executed at this time.

[0106] Among them, the average wind direction angle is second-level data, that is, when the preset time T=N seconds is set (N is a positive integer), the average wind direction angle of the next time period needs to be predicted once a second within N seconds, and the N consecutive average wind direction angles predicted within N seconds need to be greater than the preset angle in order to execute the yaw action.

[0107] Further, in some embodiments of the present invention, the first preset time period is 30 seconds, the second preset time period is 20 seconds, the third preset time period is 10 seconds, and the preset angle value is 10 degrees;

[0108] When the absolute value of the error angle between the ten-second average wind direction angle at the 5th second of the next period and the current cabin position is greater than 10 degrees within 30 seconds, the yaw action is performed; or,

[0109] When the absolute value of the error angle between the twenty-second average wind direction angle at the 10th second of the next period and the current cabin position is greater than 10 degrees within 20 seconds, the yaw action is performed; or,

[0110] When the absolute value of the error angle between the thirty-second average wind direction angle at the 15th second of the next period and the current cabin position is greater than 10 degrees within 10 seconds, the yaw action is performed.

[0111] Among them, the 5th second of the next period means that based on the wind direction data at the current moment, a sequence of average wind direction angles for the next 5 seconds is predicted, and the average wind direction angle at the 5th second is taken as the predicted data.

[0112] In this embodiment, the yaw action includes positive yaw and negative yaw. When the error angle is greater than or equal to 1 degree, positive yaw is performed; when the error angle is less than or equal to -1 degree, negative yaw is performed. When the absolute value of the error angle is less than 1 degree, the yaw action is completed.

[0113] The yaw prediction control system predicts multiple average wind direction angles, and sets multiple corresponding yaw conditions for the multiple average wind direction angles to determine whether to perform the yaw action. In addition, by introducing the concept of a preset time period and a preset angle value, it is ensured that the yaw action will only be performed when the absolute value of the error angle is greater than the preset angle value for a continuous period of time. This can effectively avoid frequent yaws caused by short-term fluctuations in wind direction, and enhance the stability and reliability of the yaw prediction control system. At the same time, according to the positive and negative values ​​of the error angle, the embodiment defines two actions, positive yaw and reverse yaw, respectively. This yaw strategy based on the direction of the error angle is more accurate.

[0114] In order to further evaluate the performance of the yaw prediction control system, three aspects will be evaluated: the accuracy of wind direction tracking, the use of yaw actuator, and power generation.

[0115] The accuracy of wind direction tracking is indicated by two performance indicators: mean absolute error and root mean square error. The calculation formula is as follows:

[0116] θ ye =θ wd -θ np

[0117]

[0118] Among them, θ ye is the yaw error, θ wd and θ np are wind direction angle and cabin position angle respectively, MAE(θ ye ) is the mean absolute error of the yaw error, RMSE(θ ye ) is the root mean square error of the yaw error;

[0119] The usage of the yaw actuator is indicated by two performance indicators: yaw action time and yaw action times. The calculation formula is as follows:

[0120]

[0121] Among them, t yaw is the activation time of the yaw actuator, is the nacelle yaw speed. This formula indicates that when the yaw speed is not 0, the yaw action time begins to accumulate. yaw is the activation times of the yaw actuator. This formula means that when the current yaw speed is different from the previous moment, the activation times of the yaw actuator is increased by 1.

[0122] The power increase is indicated by the power reduction factor indicator, and the calculation formula is as follows:

[0123]

[0124] Among them, P a is the output power of the wind turbine when there is a yaw error. ξ is the power reduction factor of the wind turbine. ideal and P red They represent the output power of wind turbines under ideal conditions and actual conditions respectively. is the yaw error at the jth moment, f j Its probability distribution.

[0125] The performance evaluation was conducted using 5 hours of second-level wind direction data from Guishan Island to verify the superiority of the present invention in yaw prediction control.

[0126] In Table 1, the CNN and LSTM wind direction prediction models show similar prediction performance, but their structures are simple and their prediction performance is insufficient compared to the CNN-LSTM hybrid neural network. Based on the CNN-LSTM model, the self-attention mechanism is introduced to show that the determination coefficient R 2 The average absolute error (MAE) increased by 0.05589, the root mean square error (RMSE) decreased by 0.2103, and the root mean square error (RMSE) decreased by 0.3469, which further improved the accuracy of wind direction prediction.

[0127] Table 2 is the statistical results of the evaluation indicators of three yaw control systems. C1 is the traditional yaw control (referred to as control system 1), C2 is the yaw control without wind direction prediction (referred to as control system 2), and C3 is the yaw control combined with Attention-CNN-LSTM wind direction prediction (referred to as control system 3). System 3 in Table 2 has the best performance indicators and the smallest power reduction (3.267%). Considering that the installed capacity of the offshore wind farm is 500MW and the effective power generation time is 3500 hours per year, the application of system 3 will increase the power generation by 2848.3MWh. The power generation capacity of the wind farm can be increased by 0.15%.

[0128]

[0129] Table 1 Performance statistics of four deep learning models for 30-second average wind direction prediction

[0130]

[0131] Table 2 Statistical results of evaluation indicators of three yaw control systems

[0132] It is understandable that when comparing the performance of the four deep learning models, CNN, LSTM, CNN-LSTM, and Attention-CNN-LSTM, the same type of training parameter settings are involved in each deep learning model.

[0133] Figure 6 The middle red line represents the traditional yaw control system (referred to as control system 1). Figure 7 The middle red line represents the yaw control system without wind direction prediction (referred to as control system 2). Figure 6 and Figure 7 The yellow line in the middle represents the yaw control system combined with Attention-CNN-LSTM wind direction prediction (referred to as control system 3)

[0134] Figure 6 to Figure 7 This is a comparison of the cabin positions of three yaw control systems under the same wind conditions. In the time period from 17000 to 19000, it can be seen that control system 2 makes the first judgment on the wind direction change, followed by control system 3, and control system 1 last. There is an obvious lag between the wind direction and the cabin position in control system 1. After the wind direction changes for a period of time, the yaw action adjusts the cabin to the specified position. Figure 2 , Figure 3 It can be seen that control system 2 has more sensitive parameters than control system 1, but the yaw action is frequent while quickly reflecting the change of wind direction. In contrast, control system 3 has smoother yaw action and more accurate yaw to wind.

[0135] Figure 8-Figure 10 The blue line in the middle represents the traditional yaw control system (referred to as control system 1), the red line represents the yaw control system without wind direction prediction (referred to as control system 2), and the yellow line represents the yaw control system combined with Attention-CNN-LSTM wind direction prediction (referred to as control system 3).

[0136] Figure 8-Figure 10The results of the total error, total yaw action times, and total yaw time of the three control systems are shown in Figure 2. The proposed control system 3 has the smallest total error, total yaw action times, and total yaw time among the three control systems, which are 159525°, 44 times, and 390 seconds, respectively, and its performance is much better than that of control system 1. Control system 1 has the largest yaw error of 166457°, the lowest yaw action times of 44 times, and the time of 452 seconds. It uses larger control parameters to sacrifice a certain yaw error in exchange for a lower yaw action times. Control system 2 reduces the yaw error to 163721, but the yaw action times and yaw time increase to 100 times and 824 seconds, and the yaw efficiency is low. In contrast, the proposed control system 3 not only meets the yaw accuracy requirements for wind, but also reduces the yaw error by 2.6% compared with control system 1, and improves the yaw efficiency. The yaw action times and time are reduced by 56% and 53.8% compared with control system 2, avoiding frequent yaw.

[0137] From the above, it can be seen that under the yaw control using historical wind direction, system 2 has smaller control parameters than system 1, and frequent yaw control only slightly improves the wind accuracy, and the yaw error is reduced by 1.64%, which is unacceptable. Under the same strategy, control system 3 combined with wind direction prediction not only has a smaller yaw error than control system 2, but also meets the dual requirements of less yaw times and time. The yaw error is reduced by 2.6% compared with control system 1. Therefore, the proposed control system 3 combined with wind direction prediction effectively improves the wind accuracy of the traditional yaw control system and improves the power generation efficiency of the wind turbine.

[0138] In a second aspect, an embodiment of the present invention further provides an offshore wind turbine yaw prediction control system, including the offshore wind turbine yaw prediction control method as described above.

[0139] In a third aspect, the present invention also provides a yaw prediction control device for an offshore wind turbine, such as Figure 2 As shown, including:

[0140] The processor 701 may be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0141] The memory 702 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 702 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 702, and the processor 701 is used to call and run the offshore wind turbine yaw prediction control system of the embodiment of this application;

[0142] Input / output interface 703, used to implement information input and output;

[0143] Communication interface 704, used to realize communication interaction between the present apparatus and other devices, which can be realized by wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.);

[0144] Bus 705 , which transmits information between various components of the device (e.g., processor 801 , memory 802 , input / output interface 703 , and communication interface 704 );

[0145] The processor 701 , the memory 702 , the input / output interface 703 and the communication interface 704 are connected to each other in communication within the device via a bus 705 .

[0146] In a fourth aspect, an embodiment of the present invention further provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the above-mentioned offshore wind turbine yaw prediction control method is implemented.

[0147] As a non-transient computer-readable storage medium, the memory can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and are implemented to be located in one place, or may also be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0148] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically include computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0149] In the description of this specification, the description with reference to the terms "one embodiment", "further embodiments", "some specific embodiments" or "some examples" etc. means that the specific features, structures or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0150] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A method for predicting and controlling the yaw of an offshore wind turbine, characterized in that: The method comprises: Acquire historical wind direction data, and process the historical wind direction data using a multi-time scale average method to obtain multiple groups of average wind direction data; According to the multiple groups of average wind direction data, an Attention-CNN-LSTM short-term wind direction prediction model is established; According to the Attention-CNN-LSTM short-term wind direction prediction model, a yaw prediction control system is established; When the wind speed exceeds the starting wind speed, the yaw prediction control system starts to operate and monitors the current wind direction; Predicting the average wind direction angle for the next period of time according to the current wind direction and the Attention-CNN-LSTM short-term wind direction prediction model; According to the average wind direction angle in the next period and the nacelle position at the current moment, a corresponding yaw action is performed.

2. The offshore wind turbine yaw prediction control method according to claim 1, characterized in that: The step of obtaining historical wind direction data and processing the historical wind direction data using a multi-time scale average method to obtain multiple groups of average wind direction data includes: Setting multiple time scales, and averaging the historical wind direction data according to each time scale to obtain multiple groups of average wind direction data corresponding to the multiple time scales; A sliding window rolling processing method is adopted to process the multiple groups of average wind direction data respectively to obtain multiple groups of training data; Normalization is performed on the multiple groups of training data respectively to obtain multiple groups of standardized training data.

3. The offshore wind turbine yaw prediction control method according to claim 2, characterized in that: The plurality of groups of average wind direction data include ten-second average wind direction data, twenty-second average wind direction data and thirty-second average wind direction data; The calculation formula of the average wind direction data is: in, It is expressed as the N-second average wind direction data at the i-th moment, Expressed as the average wind direction j seconds before the i-th moment; i and j are positive integers; N is 10, 20 or 30.

4. The offshore wind turbine yaw prediction control method according to claim 2, characterized in that: The Attention-CNN-LSTM short-term wind direction prediction model includes a convolutional neural network, a long short-term memory network and a self-attention mechanism layer; The step of establishing an Attention-CNN-LSTM short-term wind direction prediction model according to the multiple groups of average wind direction data includes: Input each group of the standardized training data into the convolutional neural network respectively; The convolutional neural network extracts feature data of each group of the standardized training data, expands the feature data into a one-dimensional sequence, and sends it to the long short-term memory network; After the long short-term memory network performs prediction processing on the one-dimensional sequence, the prediction data is output to the self-attention mechanism layer; The self-attention mechanism layer obtains an output result based on the predicted data.

5. The offshore wind turbine yaw prediction control method according to claim 1, characterized in that: The step of performing a corresponding yaw action according to the average wind direction angle of the next period and the current position of the nacelle comprises: Determine the absolute value of the error angle between the average wind direction angle in the next period and the current position of the nacelle; When the absolute value of the error angle is greater than a preset angle value within a preset time period, a yaw action is performed.

6. The offshore wind turbine yaw prediction control method according to claim 5, characterized in that: The average wind direction angle includes a ten-second average wind direction angle, a twenty-second average wind direction angle, and a thirty-second average wind direction angle; the preset time period includes a first preset time period, a second preset time period, and a third preset time period; The step of performing a corresponding yaw action according to the average wind direction angle of the next period and the current position of the nacelle comprises: When the absolute value of the ten-second average wind direction angle in the next period and the error angle of the cabin position at the current moment are both greater than the preset angle value within the first preset time period, a yaw action is performed; or, When the absolute value of the twenty-second average wind direction angle in the next period and the error angle of the cabin position at the current moment are both greater than the preset angle value within the second preset time period, a yaw action is performed; or, When the thirty-second average wind direction angle in the next period and the absolute value of the error angle of the cabin position at the current moment are both greater than the preset angle value within the third preset time period, a yaw action is performed.

7. The offshore wind turbine yaw prediction control method according to claim 5, characterized in that: The yaw action includes forward yaw and reverse yaw; when the error angle is greater than or equal to 1 degree, forward yaw is performed; when the error angle is less than or equal to -1 degree, reverse yaw is performed.

8. An offshore wind turbine yaw prediction control system, characterized in that: The invention comprises the offshore wind turbine yaw prediction control method as described in any one of claims 1 to 7.

9. A yaw prediction control device for an offshore wind turbine, characterized in that: It comprises at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions executable by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to operate the offshore wind turbine yaw prediction control system as claimed in claim 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the offshore wind turbine yaw prediction control method according to any one of claims 1 to 7.

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