Yaw positioning system for automatically controlling wind generating set

Abstract

The invention discloses a yaw positioning system for automatically controlling a wind generating set, and relates to the technical field of wind power generation, the yaw positioning system comprises a wind generating set control system which is in communication connection with a wind generating set data acquisition module, a wind generating set data processing module and a wind generating set data analysis module; the wind generating set data acquisition module monitors wind speed, wind direction and component temperature in real time through an anemometer, a wind indicator and a temperature sensor, and the wind generating set data processing module predicts the wind direction and automatically adjusts the yaw angle by training a BP neural network model to ensure that the component temperature is within a normal range. The wind generating set data analysis module adjusts yaw angles according to different wind speeds and wind directions based on a BP neural network model prediction result and sets temperature threshold values of different parts, the wind generating set control system is responsible for receiving original data, executing yaw operation and monitoring the temperature of the parts, and the whole system works in a cooperative mode. And efficient operation of the wind generating set is ensured.

Description

Technical Field

[0001] The invention relates to the technical field of wind power generation, and in particular to a yaw positioning system for automatically controlling a wind power generator set. Background Art

[0002] The yaw positioning system of the automatic control wind turbine is standing at a development outlet full of infinite possibilities, and its development prospects are undoubtedly extremely broad. Against the background of the growing global demand for renewable energy, wind power generation, as a clean and renewable energy form, is receiving unprecedented attention. With the continuous breakthroughs in technology and the strong support of government policies, the wind power industry is ushering in an unprecedented period of rapid growth. The yaw positioning system, as a vital link in the wind turbine, has a decisive influence on the operating efficiency of the entire wind power generation system through the optimization and improvement of its performance. With the continuous advancement of intelligent and automated technology, the yaw positioning system is gradually getting rid of the traditional manual control mode and developing in a more intelligent and efficient direction. This not only greatly improves the efficiency of wind energy utilization, enabling wind turbines to maintain optimal working conditions in various complex environments, but also significantly reduces operation and maintenance costs, laying a solid foundation for the sustainable development of the wind power industry.

[0003] At present, electronic yaw systems have achieved remarkable results in the field of wind power generation, achieving more precise control of wind direction. This progress not only improves the operating efficiency of wind turbines, but also further optimizes the capture and utilization of wind energy. However, this is only a starting point for the development of yaw systems, and future development trends will focus more on intelligence and automation. With the rapid development of science and technology, advanced sensing technology and neural network algorithms are gradually being integrated into the design of yaw systems. The application of these technologies will enable the system to accurately predict wind direction changes in real time and automatically adjust the yaw angle according to the prediction results to ensure that the wind turbines are always in the best working condition. At the same time, these technologies will also enhance the system's adaptability in extreme weather conditions, such as cold or hot weather, to ensure that the system can operate continuously and stably. Looking to the future, the yaw positioning system of automatically controlled wind turbines will achieve more efficient, reliable and intelligent development driven by continuous technological innovation and continuous market expansion. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a yaw positioning system for automatically controlling a wind turbine generator set, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a yaw positioning system for automatically controlling a wind turbine generator set, comprising a wind turbine generator set control system, wherein the wind turbine generator set control system is communicatively connected with a wind turbine generator set data acquisition module, a wind turbine generator set data processing module and a wind turbine generator set data analysis module; The wind turbine data acquisition module includes: an anemometer, a wind vane and a temperature sensor on the wind turbine to monitor the changes in wind speed and wind direction and detect the temperature of each component of the wind turbine when it is running; The wind turbine data processing module realizes accurate prediction of wind direction and automatic adjustment of yaw angle by training BP neural network model; The wind turbine data analysis module adjusts the yaw angle of the wind turbine at different wind speeds and wind directions and sets the thresholds of various system components at different temperatures based on the prediction results of the BP neural network model; The wind turbine generator control system is responsible for receiving and processing the collected raw data, executing yaw, and detecting the temperature of each component of the system.

[0006] Furthermore, the process of the wind turbine data acquisition module monitoring the changes of wind speed and wind direction in real time through the anemometer and wind vane installed on the wind turbine includes: The anemometer senses the wind speed through its internal propeller and converts it into an electrical signal. The electrical signal is proportional to the wind speed. When the wind speed increases, the electrical signal increases, and when the wind speed decreases, the electrical signal weakens. The wind vane is used to sense the direction of the wind. It is a freely rotating device that contains a potentiometer. When the wind direction changes, the wind vane rotates accordingly, thereby generating a corresponding electrical signal. This signal represents the current wind direction. The collected wind speed and direction signals are converted into digital signals through analog-to-digital conversion.

[0007] Furthermore, the process in which the wind turbine generator set data acquisition module detects the temperature of each component of the wind turbine generator set during operation through a temperature sensor includes: The temperature sensor senses the temperature of the yaw positioning system gearbox, generator and bearing through its internal thermistor. When the temperature changes, the resistance value of the thermistor will change accordingly. The change is converted into an electrical signal, amplified, filtered and converted into a digital signal, and transmitted to the control system of the wind turbine through a communication protocol.

[0008] Furthermore, the process of the wind turbine generator data processing module inputting the collected data into the BP neural network model input layer includes: The collected data is cleaned to remove noise and outliers, the data is normalized to make them in the same dimension and range, and the data is smoothed to reduce the volatility and uncertainty of the data. A BP neural network model is constructed, which includes an input layer, a hidden layer and an output layer. The historical data of wind speed, wind direction and temperature are used as input of the BP neural network for training the model so that it can learn the relationship between wind speed, wind direction and wind direction prediction and the correlation between the temperature of each component and yaw adjustment. The number of nodes in the input layer corresponds to the number of input features.

[0009] Furthermore, the process of training the hidden layer of the BP neural network model by the wind turbine data processing module includes: The neurons in the hidden layer process the input data through weighted summation and activation function to extract features related to wind direction prediction and yaw angle adjustment. The features include nonlinear transformation of wind speed, wind direction and temperature data. Since the wind direction prediction and yaw angle adjustment problems are nonlinear, the neurons in the hidden layer introduce nonlinear factors through the Sigmoid activation function, so that the model can handle complex nonlinear relationships. The neurons in the hidden layer integrate the information from the input layer through mutual connection and collaboration to form a comprehensive understanding of wind direction and yaw angle adjustment.

[0010] Furthermore, the process of the wind turbine generator data processing module training the output layer of the BP neural network model includes: In the task of predicting wind direction, a regression model is used to output continuous wind direction angle values. A neuron in the output layer is used to output the predicted wind direction angle value. The output value of the neuron is transformed by the sigmoid activation function to be the predicted wind direction angle. In the task of yaw angle adjustment, a classification model is used to output discrete adjustment instructions. The three neurons in the output layer correspond to three different adjustment instructions, including left turn, right turn and hold. The output value of the neuron is transformed by the softmax function to obtain a probability distribution, which represents the probability of each adjustment instruction. The system selects the adjustment instruction with the highest probability as the final output and sends it to the control system to perform yaw adjustment. The real-time collected data is input into the trained BP neural network model. The model calculates and outputs the predicted wind direction change and the working health status of each component of the system according to the input data. The system calculates the yaw angle that needs to be adjusted according to the prediction results and the current operating status of the unit, and generates a control instruction. The yaw system controller adjusts the yaw angle according to the control instruction so that the fan blades always face the direction of the wind and keep the yaw system temperature within the normal working range.

[0011] Furthermore, the wind turbine data analysis module implements the process of adjusting the yaw angle of the wind turbine under different wind speeds and wind directions based on the prediction results of the BP neural network model, including: When the cut-in wind speed is below 3 m / s, the wind turbine cannot generate electricity effectively. At this time, the yaw system is in standby mode and no yaw adjustment is performed. When the wind speed gradually increases to above the cut-in wind speed but below the rated wind speed of 12 m / s, the yaw system adjusts the direction of the wind rotor in real time according to the data of the wind vane. The BP neural network model predicts wind direction changes based on historical data and real-time data. When the wind speed reaches above the rated wind speed, the yaw system dynamically adjusts the yaw angle according to changes in wind speed and wind direction and the operating status of the unit. When the cut-out wind speed is above 25 m / s, the wind turbine automatically disconnects from the grid and shuts down, and the yaw system adjusts the wind rotor to the sidewind position. In addition, when the wind direction changes significantly, the BP neural network model quickly predicts the new wind direction based on real-time data, and controls the yaw system to continuously and smoothly adjust the direction of the wind rotor.

[0012] Furthermore, the process of the wind turbine generator data analysis module setting thresholds under different temperature conditions for various components of the system based on the prediction results of the BP neural network model includes: When the gearbox temperature of the wind turbine is between 30℃ and 70℃, the system works normally. When the gearbox temperature reaches 80℃, the system issues a high temperature alarm and starts the heat sink installed on the outside of the gearbox. When the gearbox temperature reaches 90℃, the wind turbine automatically shuts down. When the generator temperature of the wind turbine is between 60℃ and 120℃, the system works normally. When the generator temperature reaches 130℃, the system issues a high temperature alarm and starts the cooling water circulation system inside the generator. When the generator temperature reaches 150℃, the wind turbine automatically shuts down. When the bearing temperature of the wind turbine is between 30℃ and 70℃, the system works normally. When the bearing temperature reaches 80℃, the system issues a high temperature alarm to ensure lubrication. The grease is sufficient and clean, and the load and speed of the wind turbine are adjusted. When the bearing temperature reaches 95℃, the wind turbine will automatically shut down. When the gear box temperature drops to -20℃ or below, the generator temperature drops to -10℃ or below, and the bearing temperature drops to -15℃ or below, any of the three situations occurs, the system will issue a low temperature alarm and start the heater to heat the gear box, generator and bearing to restore them to the normal temperature range. The blade angle is adjusted to reduce the vibration and noise of the wind turbine caused by low temperature. For low temperature environments, low-temperature lubricants with low freezing point and good fluidity are selected to ensure that all system components can maintain good lubrication effects at low temperatures.

[0013] Furthermore, the wind turbine control system is responsible for receiving and processing the collected raw data, and the process of executing yaw and detecting the temperature of each component of the system includes: The control system receives the real-time monitoring data of wind speed, wind direction and temperature transmitted by the data acquisition module, and uses the BP neural network model in the data processing module to process and analyze the received data, predict wind direction changes and calculate the required yaw angle. According to the processing results, the yaw angle of the wind turbine is adjusted through the actuator so that the blades are always aligned with the direction of the wind, monitor the operating temperature of each component of the wind turbine, and take corresponding protective measures when the temperature exceeds the normal range.

[0014] The present invention provides a yaw positioning system for automatically controlling a wind turbine generator set. It has the following beneficial effects: The yaw positioning system of the automatic control wind turbine has achieved a major breakthrough in the field of wind power generation by deeply integrating advanced sensing technology and BP neural network algorithm. The system can capture subtle changes in wind speed, wind direction and temperature of key components in real time, providing detailed data support for precise control. With the high-precision prediction ability of the BP neural network model, the system can foresee subtle changes in wind direction and autonomously adjust the yaw angle to ensure that the wind turbine is always facing the wind accurately, thereby maximizing the capture of wind energy and significantly improving power generation efficiency and energy utilization. More advanced is that the system has a powerful built-in adaptive mechanism that can maintain excellent operating stability under extreme climate conditions such as severe cold and heat, effectively avoiding the potential threats to equipment operation caused by environmental factors, and further extending the service life of the equipment. In addition, the application of intelligent control strategies has greatly reduced the need for manual intervention, reduced the workload of operation and maintenance personnel, saved operation and maintenance costs, and improved overall economic benefits. In summary, the yaw positioning system not only greatly improves the reliability and efficiency of wind power generation, but also opens up a new path for the sustainable utilization of wind energy resources, injecting strong impetus into the vigorous development of the green energy industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a block diagram of a system for automatically controlling the yaw positioning of a wind turbine generator set according to an embodiment of the present application. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] like Figure 1As shown, the present invention provides a technical solution: a yaw positioning system for automatically controlling a wind turbine generator set, comprising a wind turbine generator set control system, wherein the wind turbine generator set control system is communicatively connected with a wind turbine generator set data acquisition module, a wind turbine generator set data processing module and a wind turbine generator set data analysis module; The wind turbine data acquisition module includes: an anemometer, a wind vane and a temperature sensor on the wind turbine to monitor the changes in wind speed and wind direction and detect the temperature of each component of the wind turbine when it is running; The wind turbine data processing module realizes accurate prediction of wind direction and automatic adjustment of yaw angle by training BP neural network model; The wind turbine data analysis module adjusts the yaw angle of the wind turbine at different wind speeds and wind directions and sets the thresholds of various system components at different temperatures based on the prediction results of the BP neural network model; The wind turbine generator control system is responsible for receiving and processing the collected raw data, executing yaw, and detecting the temperature of each component of the system.

[0018] The process of the wind turbine data acquisition module monitoring the changes of wind speed and wind direction in real time through the anemometer and wind vane installed on the wind turbine includes: The anemometer senses the wind speed through its internal propeller and converts it into an electrical signal. The electrical signal is proportional to the wind speed. When the wind speed increases, the electrical signal increases, and when the wind speed decreases, the electrical signal weakens. The wind vane is used to sense the direction of the wind. It is a freely rotating device that contains a potentiometer. When the wind direction changes, the wind vane rotates accordingly, thereby generating a corresponding electrical signal. This signal represents the current wind direction. The collected wind speed and direction signals are converted into digital signals through analog-to-digital conversion.

[0019] The process of the wind turbine generator set data acquisition module detecting the temperature of each component of the wind turbine generator set during operation by means of a temperature sensor includes: The temperature sensor senses the temperature of the yaw positioning system gearbox, generator and bearing through its internal thermistor. When the temperature changes, the resistance value of the thermistor will change accordingly. The change is converted into an electrical signal, amplified, filtered and converted into a digital signal, and transmitted to the control system of the wind turbine through a communication protocol.

[0020] The process of the wind turbine generator data processing module inputting the collected data into the BP neural network model input layer includes: The collected data is cleaned to remove noise and outliers, the data is normalized to make them in the same dimension and range, and the data is smoothed to reduce the volatility and uncertainty of the data. A BP neural network model is constructed, which includes an input layer, a hidden layer and an output layer. The historical data of wind speed, wind direction and temperature are used as input of the BP neural network for training the model so that it can learn the relationship between wind speed, wind direction and wind direction prediction and the correlation between the temperature of each component and yaw adjustment. The number of nodes in the input layer corresponds to the number of input features.

[0021] The process of training the hidden layer of the BP neural network model by the wind turbine data processing module includes: The neurons in the hidden layer process the input data through weighted summation and activation function to extract features related to wind direction prediction and yaw angle adjustment. The features include nonlinear transformation of wind speed, wind direction and temperature data. Since the wind direction prediction and yaw angle adjustment problems are nonlinear, the neurons in the hidden layer introduce nonlinear factors through the Sigmoid activation function, so that the model can handle complex nonlinear relationships. The neurons in the hidden layer integrate the information from the input layer through mutual connection and collaboration to form a comprehensive understanding of wind direction and yaw angle adjustment.

[0022] The process of the wind turbine generator data processing module training the output layer of the BP neural network model includes: In the task of predicting wind direction, a regression model is used to output continuous wind direction angle values. A neuron in the output layer is used to output the predicted wind direction angle value. The output value of the neuron is transformed by the sigmoid activation function to be the predicted wind direction angle. In the task of yaw angle adjustment, a classification model is used to output discrete adjustment instructions. The three neurons in the output layer correspond to three different adjustment instructions, including left turn, right turn and hold. The output value of the neuron is transformed by the softmax function to obtain a probability distribution, which represents the probability of each adjustment instruction. The system selects the adjustment instruction with the highest probability as the final output and sends it to the control system to perform yaw adjustment. The real-time collected data is input into the trained BP neural network model. The model calculates and outputs the predicted wind direction change and the working health status of each component of the system according to the input data. The system calculates the yaw angle that needs to be adjusted according to the prediction results and the current operating status of the unit, and generates a control instruction. The yaw system controller adjusts the yaw angle according to the control instruction so that the fan blades always face the direction of the wind and keep the yaw system temperature within the normal working range.

[0023] The process of adjusting the yaw angle of the wind generator set under different wind speeds and wind directions by the wind generator set data analysis module based on the prediction results of the BP neural network model includes: When the cut-in wind speed is below 3 m / s, the wind turbine cannot generate electricity effectively. At this time, the yaw system is in standby mode and no yaw adjustment is performed. When the wind speed gradually increases to above the cut-in wind speed but below the rated wind speed of 12 m / s, the yaw system adjusts the direction of the wind rotor in real time according to the data of the wind vane. The BP neural network model predicts wind direction changes based on historical data and real-time data. When the wind speed reaches above the rated wind speed, the yaw system dynamically adjusts the yaw angle according to changes in wind speed and wind direction and the operating status of the unit. When the cut-out wind speed is above 25 m / s, the wind turbine automatically disconnects from the grid and shuts down, and the yaw system adjusts the wind rotor to the sidewind position. In addition, when the wind direction changes significantly, the BP neural network model quickly predicts the new wind direction based on real-time data, and controls the yaw system to continuously and smoothly adjust the direction of the wind rotor.

[0024] The process of setting the threshold values ​​of various components of the system under different temperature conditions based on the prediction results of the BP neural network model by the wind turbine data analysis module includes: When the gearbox temperature of the wind turbine is between 30℃ and 70℃, the system works normally. When the gearbox temperature reaches 80℃, the system issues a high temperature alarm and starts the heat sink installed on the outside of the gearbox. When the gearbox temperature reaches 90℃, the wind turbine automatically shuts down. When the generator temperature of the wind turbine is between 60℃ and 120℃, the system works normally. When the generator temperature reaches 130℃, the system issues a high temperature alarm and starts the cooling water circulation system inside the generator. When the generator temperature reaches 150℃, the wind turbine automatically shuts down. When the bearing temperature of the wind turbine is between 30℃ and 70℃, the system works normally. When the bearing temperature reaches 80℃, the system issues a high temperature alarm to ensure lubrication. The grease is sufficient and clean, and the load and speed of the wind turbine are adjusted. When the bearing temperature reaches 95℃, the wind turbine will automatically shut down. When the gear box temperature drops to -20℃ or below, the generator temperature drops to -10℃ or below, and the bearing temperature drops to -15℃ or below, any of the three situations occurs, the system will issue a low temperature alarm and start the heater to heat the gear box, generator and bearing to restore them to the normal temperature range. The blade angle is adjusted to reduce the vibration and noise of the wind turbine caused by low temperature. For low temperature environments, low-temperature lubricants with low freezing point and good fluidity are selected to ensure that all system components can maintain good lubrication effects at low temperatures.

[0025] The wind turbine control system is responsible for receiving and processing the collected raw data, and the process of executing yaw and detecting the temperature of each component of the system includes: The control system receives the real-time monitoring data of wind speed, wind direction and temperature transmitted by the data acquisition module, and uses the BP neural network model in the data processing module to process and analyze the received data, predict wind direction changes and calculate the required yaw angle. According to the processing results, the yaw angle of the wind turbine is adjusted through the actuator so that the blades are always aligned with the direction of the wind, monitor the operating temperature of each component of the wind turbine, and take corresponding protective measures when the temperature exceeds the normal range.

[0026] How to use this system: First, the wind speed, wind direction and operating temperature of each component of the wind turbine are monitored in real time using an anemometer, wind vane and temperature sensor. Subsequently, the data processing module receives these real-time data and uses the trained BP neural network model to accurately predict the wind direction. At the same time, the yaw angle is automatically adjusted according to the prediction results to ensure that the temperature of the components of the wind turbine is within the normal range. Next, the data analysis module further optimizes the yaw angle for different wind speed and wind direction conditions based on the prediction results of the BP neural network, and sets the corresponding protection threshold according to the component temperature. Finally, the control system receives all the raw data, performs yaw operations, and continuously monitors the component temperature. Once an abnormality is found, protective measures are taken immediately to ensure that the wind turbine can operate efficiently and safely.

[0027] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions. The sentence "includes an element defined by ... does not exclude the existence of other identical elements in the process, method, article or device including the element".

[0028] Although 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 appended claims and their equivalents.

Claims

1. A yaw positioning system for automatically controlling a wind turbine generator set, characterized in that: It includes a wind turbine control system, wherein the wind turbine control system is communicatively connected with a wind turbine data acquisition module, a wind turbine data processing module and a wind turbine data analysis module; The wind turbine data acquisition module includes: an anemometer, a wind vane and a temperature sensor on the wind turbine to monitor the changes in wind speed and wind direction and detect the temperature of each component of the wind turbine when it is running; The wind turbine data processing module realizes accurate prediction of wind direction and automatic adjustment of yaw angle by training BP neural network model; The wind turbine data analysis module adjusts the yaw angle of the wind turbine at different wind speeds and wind directions and sets the thresholds of various system components at different temperatures based on the prediction results of the BP neural network model; The wind turbine generator control system is responsible for receiving and processing the collected raw data, executing yaw, and detecting the temperature of each component of the system.

2. The yaw positioning system for automatically controlling a wind turbine generator set according to claim 1, characterized in that: The process of the wind turbine data acquisition module monitoring the changes of wind speed and wind direction in real time through the anemometer and wind vane installed on the wind turbine includes: The anemometer senses the wind speed through its internal propeller and converts it into an electrical signal. The electrical signal is proportional to the wind speed. When the wind speed increases, the electrical signal increases, and when the wind speed decreases, the electrical signal weakens. The wind vane is used to sense the direction of the wind. It is a freely rotating device that contains a potentiometer. When the wind direction changes, the wind vane rotates accordingly, thereby generating a corresponding electrical signal. This signal represents the current wind direction. The collected wind speed and direction signals are converted into digital signals through analog-to-digital conversion.

3. The yaw positioning system for automatically controlling a wind turbine generator set according to claim 2, characterized in that: The process of the wind turbine generator set data acquisition module detecting the temperature of each component of the wind turbine generator set during operation by means of a temperature sensor includes: The temperature sensor senses the temperature of the yaw positioning system gearbox, generator and bearing through its internal thermistor. When the temperature changes, the resistance value of the thermistor will change accordingly. The change is converted into an electrical signal, amplified, filtered and converted into a digital signal, and transmitted to the control system of the wind turbine through a communication protocol.

4. The automatic yaw positioning system for controlling a wind turbine generator set according to claim 3, characterized in that: The process of the wind turbine generator data processing module inputting the collected data into the BP neural network model input layer includes: The collected data is cleaned to remove noise and outliers, the data is normalized to make them in the same dimension and range, and the data is smoothed to reduce the volatility and uncertainty of the data. A BP neural network model is constructed, which includes an input layer, a hidden layer and an output layer. The historical data of wind speed, wind direction and temperature are used as input of the BP neural network for training the model so that it can learn the relationship between wind speed, wind direction and wind direction prediction and the correlation between the temperature of each component and yaw adjustment. The number of nodes in the input layer corresponds to the number of input features.

5. The yaw positioning system for automatically controlling a wind turbine generator set according to claim 4, characterized in that: The process of training the hidden layer of the BP neural network model by the wind turbine data processing module includes: The neurons in the hidden layer process the input data through weighted summation and activation function to extract features related to wind direction prediction and yaw angle adjustment. The features include nonlinear transformation of wind speed, wind direction and temperature data. Since the wind direction prediction and yaw angle adjustment problems are nonlinear, the neurons in the hidden layer introduce nonlinear factors through the Sigmoid activation function, so that the model can handle complex nonlinear relationships. The neurons in the hidden layer integrate the information from the input layer through mutual connection and collaboration to form a comprehensive understanding of wind direction and yaw angle adjustment.

6. The yaw positioning system for automatically controlling a wind turbine generator set according to claim 5, characterized in that: The process of the wind turbine generator data processing module training the output layer of the BP neural network model includes: In the task of predicting wind direction, a regression model is used to output continuous wind direction angle values. A neuron in the output layer is used to output the predicted wind direction angle value. The output value of the neuron is transformed by the sigmoid activation function to be the predicted wind direction angle. In the task of yaw angle adjustment, a classification model is used to output discrete adjustment instructions. The three neurons in the output layer correspond to three different adjustment instructions, including left turn, right turn and hold. The output value of the neuron is transformed by the softmax function to obtain a probability distribution, which represents the probability of each adjustment instruction. The system selects the adjustment instruction with the highest probability as the final output and sends it to the control system to perform yaw adjustment. The real-time collected data is input into the trained BP neural network model. The model calculates and outputs the predicted wind direction change and the working health status of each component of the system according to the input data. The system calculates the yaw angle that needs to be adjusted according to the prediction results and the current operating status of the unit, and generates a control instruction. The yaw system controller adjusts the yaw angle according to the control instruction so that the fan blades always face the direction of the wind and keep the yaw system temperature within the normal working range.

7. The automatic yaw positioning system for controlling a wind turbine generator set according to claim 6, characterized in that: The process of adjusting the yaw angle of the wind generator set under different wind speeds and wind directions by the wind generator set data analysis module based on the prediction results of the BP neural network model includes: When the cut-in wind speed is below 3 m / s, the wind turbine cannot generate electricity effectively. At this time, the yaw system is in standby mode and no yaw adjustment is performed. When the wind speed gradually increases to above the cut-in wind speed but below the rated wind speed of 12 m / s, the yaw system adjusts the direction of the wind rotor in real time according to the data of the wind vane. The BP neural network model predicts wind direction changes based on historical data and real-time data. When the wind speed reaches above the rated wind speed, the yaw system dynamically adjusts the yaw angle according to changes in wind speed and wind direction and the operating status of the unit. When the cut-out wind speed is above 25 m / s, the wind turbine automatically disconnects from the grid and shuts down, and the yaw system adjusts the wind rotor to the sidewind position. In addition, when the wind direction changes significantly, the BP neural network model quickly predicts the new wind direction based on real-time data, and controls the yaw system to continuously and smoothly adjust the direction of the wind rotor.

8. The yaw positioning system for automatically controlling a wind turbine generator set according to claim 7, characterized in that: The process of setting the threshold values ​​of various components of the system under different temperature conditions based on the prediction results of the BP neural network model by the wind turbine data analysis module includes: When the gearbox temperature of the wind turbine is between 30℃ and 70℃, the system works normally. When the gearbox temperature reaches 80℃, the system issues a high temperature alarm and starts the heat sink installed on the outside of the gearbox. When the gearbox temperature reaches 90℃, the wind turbine automatically shuts down. When the generator temperature of the wind turbine is between 60℃ and 120℃, the system works normally. When the generator temperature reaches 130℃, the system issues a high temperature alarm and starts the cooling water circulation system inside the generator. When the generator temperature reaches 150℃, the wind turbine automatically shuts down. When the bearing temperature of the wind turbine is between 30℃ and 70℃, the system works normally. When the bearing temperature reaches 80℃, the system issues a high temperature alarm to ensure lubrication. The grease is sufficient and clean, and the load and speed of the wind turbine are adjusted. When the bearing temperature reaches 95℃, the wind turbine will automatically shut down. When the gear box temperature drops to -20℃ or below, the generator temperature drops to -10℃ or below, and the bearing temperature drops to -15℃ or below, any of the three situations occurs, the system will issue a low temperature alarm and start the heater to heat the gear box, generator and bearing to restore them to the normal temperature range. The blade angle is adjusted to reduce the vibration and noise of the wind turbine caused by low temperature. For low temperature environments, low-temperature lubricants with low freezing point and good fluidity are selected to ensure that all system components can maintain good lubrication effects at low temperatures.

9. The yaw positioning system for automatically controlling a wind turbine generator set according to claim 8, characterized in that: The wind turbine control system is responsible for receiving and processing the collected raw data, and the process of executing yaw and detecting the temperature of each component of the system includes: The control system receives the real-time monitoring data of wind speed, wind direction and temperature transmitted by the data acquisition module, and uses the BP neural network model in the data processing module to process and analyze the received data, predict wind direction changes and calculate the required yaw angle. According to the processing results, the yaw angle of the wind turbine is adjusted through the actuator so that the blades are always aligned with the direction of the wind, monitor the operating temperature of each component of the wind turbine, and take corresponding protective measures when the temperature exceeds the normal range.

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