Pitch control system and method for wind turbine generator set

Abstract

The present invention relates to a variable pitch control system and method for a wind turbine generator set, which is intended to optimize the angle adjustment of wind blades in a highly automated and intelligent manner. The system includes a plurality of wind blades with independent angle adjustment functions, and a central control unit, which is equipped with a receiver to receive real-time operation data from each blade, and adjusts the angle of each blade based on these data through a blade angle control algorithm. The operation data includes blade angle, wind speed, blade stress and power generation. In addition, the system also includes a safety monitoring unit, which is physically isolated from the central control unit and can intervene independently when abnormal blade operation is detected, and perform emergency angle adjustment on the blade through a backup control path, thereby ensuring the safe operation of the wind turbine generator set under various conditions. This system and method not only enhance the efficiency and safety of wind power generation, but also improve the ability to respond quickly to abnormal operating conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of variable pitch control of wind turbine generator sets, and in particular to a variable pitch control system and method for wind turbine generator sets. Background Art

[0002] In existing wind power generation technology, the efficiency and reliability of wind turbines largely rely on the angle adjustment system of their blades. These systems usually include multiple blades, each of which can be adjusted in angle by a central control unit to maximize the utilization of wind energy and optimize power generation efficiency. The central control unit usually receives real-time data such as wind speed, blade angle, blade stress, and power generation, and adjusts the blade angle based on these data through algorithms to adapt to changing wind conditions.

[0003] However, the main problem faced by existing technologies is the limitation of effective real-time adjustment of wind blades in the event of system failure or abnormal operating conditions. In particular, when the central control unit fails, the existing system often lacks sufficient independence to ensure the continued safe operation of the generator set. In addition, when real-time operating data indicates that the blades are in a non-ideal state, the traditional system may not respond quickly or effectively, increasing the risk of system failure, thereby affecting the stability and output efficiency of the entire generator set.

[0004] Therefore, there is an urgent need to develop a new type of variable pitch control system and method for wind turbines. Summary of the invention

[0005] The present application provides a variable pitch control system for a wind turbine generator set to improve the efficiency and safety of wind power generation.

[0006] The present application provides a variable pitch control system for a wind turbine generator set, comprising:

[0007] A set of wind blades, each wind blade has independent angle adjustment function;

[0008] A central control unit is configured with a receiver for receiving real-time operation data from each wind blade, and adjusting the angle of each wind blade based on the operation data using a blade angle control algorithm; wherein the real-time operation data includes blade angle, wind speed, blade stress and power generation;

[0009] The safety monitoring unit is physically isolated from the central control unit and can intervene independently of the central control unit when any abnormal operation of the wind blade is detected, and perform emergency angle adjustment on all wind blades to ensure the safe operation of the wind turbine generator set; wherein the safety monitoring unit includes a safety chain feedback mechanism for starting a backup control path when the central control unit fails, and directly controlling the blade angle adjustment through an independent communication link.

[0010] Furthermore, the blade angle control algorithm used by the central control unit is implemented using the following formula 1:

[0011] θ new =θ current ·W (1)

[0012] Among them, θ new is the new blade angle; θ current is the current blade angle; W is the weight adjustment coefficient, which is implemented using the following formula 2:

[0013]

[0014] Where V is the real-time wind speed; V norm is the normal operating wind speed; ΔS is the blade stress change; A max is the maximum stress that the blade can withstand; T is the ambient temperature; θ current is the current blade angle; α, β, γ and δ are adjustment coefficients determined by experimental data.

[0015] Furthermore, the control algorithm includes an adaptive learning mechanism, which can automatically adjust the weight adjustment coefficients α, β, γ and δ based on long-term collected operation data to adapt to changing environmental conditions and improve the overall efficiency of the system.

[0016] Furthermore, the safety chain feedback mechanism is specifically used for:

[0017] Monitor the real-time operation data from the wind blades, and trigger the safety chain signal when it is detected that the real-time operation data exceeds the preset safety parameter range;

[0018] The normal control channel to the central control unit is isolated and the backup control path is activated;

[0019] Sending instructions to the angle adjuster of each wind blade through the backup control path; wherein the instructions include instructions to adjust the blade to a safe position;

[0020] When it is detected that the real-time operating data returns to the preset safety parameter range, the normal control path of the central control unit is restarted and the backup path is closed.

[0021] Furthermore, the safety chain feedback mechanism uses the following formula 3 to calculate the safe position:

[0022] Safe position calculation formula:

[0023] θ safe =θ current +f×sgn(θoptimal ―θ current )(3)

[0024] Among them, θ safe is the calculated safe blade angle; θ current is the current blade angle; θ optimal is the ideal blade angle calculated through historical data and environmental feedback; f is an adjustment function based on environmental factors and the current state of the blade, which is implemented using the following formula 4:

[0025]

[0026] Among them, V crit is the critical wind speed determined by experiment; V is the real-time wind speed; T norm is the normal ambient temperature determined by experiment; ΔS is the blade stress change; T is the ambient temperature; S max It is the maximum stress that the blade can withstand.

[0027] Furthermore, the calculation of the safe position also takes into account the correlation with wind direction. The pitch control system monitors the wind direction changes in real time through a wind direction sensor and adjusts the blade angle to minimize the direct impact of wind on the blades, thereby protecting the blades from damage under extreme weather conditions.

[0028] Furthermore, the safety chain feedback mechanism includes a data reset mechanism for automatically resetting the control settings of all blades to an initial state after the blade operating data returns to a preset safety parameter range, thereby ensuring that the system can operate with an optimal configuration after restarting.

[0029] Furthermore, the central control unit includes a fault detection module for analyzing the stress data and wind speed data of the blades in real time. When any abnormal data trend is detected, it automatically issues an alarm and adjusts the blade angle to a preset safety threshold to prevent potential mechanical damage.

[0030] Furthermore, the central control unit includes an energy optimization module for dynamically adjusting the blade angle according to the received wind speed and power generation data to maximize the energy capture and conversion efficiency while reducing the energy loss caused by excessive wind speed.

[0031] The present application provides a pitch control method for a wind turbine generator set, comprising:

[0032] Receiving real-time operation data of each wind blade, wherein the real-time operation data includes blade angle, wind speed, blade stress and power generation;

[0033] Based on the real-time operational data received, the angle of each wind blade is adjusted using a predefined control algorithm to optimize performance;

[0034] Continuously monitor the operating data of all wind blades and immediately implement emergency intervention measures if the data indicates abnormal operation of any wind blade;

[0035] When an operational anomaly is detected, a backup control path is activated to perform an emergency angle adjustment of the wind blades independently of the normal control process; the emergency angle adjustment includes adjusting all blades to a safe angle, which is calculated based on the current environmental and operating conditions and is intended to minimize potential risks;

[0036] When the detected operation data indicates that the wind blade has returned to a safe operation range, the backup control path is terminated and the normal control path is restored.

[0037] The beneficial effects of the present application mainly include: (1) Through the independent angle adjustment function of each wind blade, the system can dynamically adjust the angle of each blade according to real-time wind speed, blade stress and other data. Such a design enables the generator set to maximize energy capture and conversion efficiency under various wind conditions, thereby optimizing the overall power generation performance. (2) The introduction of the safety monitoring unit increases the redundancy of the system, especially when the central control unit fails. The safety monitoring unit can operate independently and intervene quickly when an abnormality is detected to adjust the blade to a safe position, thereby effectively preventing potential damage or accidents caused by delays due to failures. (3) The safety monitoring unit includes a safety chain feedback mechanism, which can start a backup control path when the main control unit fails or the blade operation data is abnormal. This independent communication link directly controls the blade angle adjustment, ensuring that the blade can be quickly adjusted to a safe state at a critical moment, greatly improving the system's ability to respond to emergencies. (4) Even under extreme or adverse conditions, such as when the central control unit fails, the system design ensures that the wind turbine generator set can still maintain operation. The existence of the backup control path ensures that there will be no complete shutdown due to a single failure point, thereby improving the overall stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of a variable pitch control system of a wind turbine generator set provided in the first embodiment of the present application.

[0039] Figure 2 This is a flow chart of a pitch control method for a wind turbine generator set provided in the second embodiment of the present application. DETAILED DESCRIPTION

[0040] Many specific details are described in the following description to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present application, so the present application is not limited by the specific implementation disclosed below.

[0041] The first embodiment of the present application provides a variable pitch control system for a wind turbine generator set. Figure 1 , which is a schematic diagram of the first embodiment of the present application. Figure 1 A pitch control system for a wind turbine generator set is described in detail in the first embodiment of the present application.

[0042] The pitch control system of the wind turbine generator set includes a group of wind blades 101 , a central control unit 102 and a safety monitoring unit 103 .

[0043] A group of wind blades 101, each wind blade has an independent angle adjustment function.

[0044] In this embodiment, a group of wind blades 101 is a key component of a wind turbine pitch control system, and is designed to efficiently capture wind energy and convert it into electrical energy. The group of wind blades 101 includes multiple blades, each of which is equipped with an independent angle adjustment mechanism, so that each blade can independently adjust its angle according to real-time environmental data to maximize energy output.

[0045] Each wind blade is equipped with an angle adjustment device, which continuously monitors its angle position through its built-in sensors and sends this data to the central control unit 102 in real time. These blades are usually made of high-strength, corrosion-resistant materials, such as glass fiber reinforced polymer or carbon fiber composites, to withstand high-speed winds and various weather conditions. The design and material selection of the blades are to optimize their aerodynamic properties and structural stability, thereby maintaining long-term durability without sacrificing efficiency.

[0046] The angle adjustment function of each blade is controlled by a servo motor located at the root of the blade. These servo motors receive instructions from the central control unit and dynamically adjust the blade angle based on the real-time operating data received (including wind speed, current blade angle, blade stress and power generation, etc.). This data is transmitted in real time through a high-speed communication network to ensure that the adjustment of the blade angle can respond to environmental changes in real time.

[0047] In addition, each wind blade is equipped with stress sensors that can measure and report the force the blade is subjected to during operation, using this data to help prevent structural damage that may be caused by exceeding the design stress. This independent angle adjustment and stress monitoring mechanism not only enhances the adaptability of the system, but also significantly improves the stability and safety of the entire generator set when facing complex environmental variables.

[0048] By comprehensively applying these technologies, this embodiment can ensure that the wind turbine generator set can maintain optimal operating efficiency under different wind conditions, while also greatly reducing the risks caused by environmental or technical failures.

[0049] The central control unit 102 is configured with a receiver for receiving real-time operation data from each wind blade, and based on the operation data, uses a blade angle control algorithm to adjust the angle of each wind blade; wherein the real-time operation data includes blade angle, wind speed, blade stress and power generation.

[0050] The central control unit 102 is the core component of the wind turbine pitch control system, responsible for coordinating and managing the angle adjustment of the wind blades to optimize power generation efficiency and respond to environmental changes. The control unit is equipped with an advanced receiver specifically designed to receive real-time operating data from each wind blade. These data include but are not limited to the current angle of the blade, wind speed, stress on the blade, and power generation, which are key parameters for adjusting the blade angle to maximize energy efficiency and safety.

[0051] The central control unit processes this data in real time through built-in software and hardware components, using specially developed control algorithms to precisely adjust the angle of each blade. The algorithm dynamically calculates the optimal blade angle based on real-time data to cope with changing wind speeds and other environmental factors, ensuring maximum power generation efficiency while reducing structural stress. The control algorithm is designed with multiple operating modes and environmental scenarios in mind to ensure optimal performance under various wind conditions.

[0052] To achieve these functions, the central control unit is equipped with a high-performance microprocessor and a multi-channel data receiving module, which are standard electronic components available on the market. These components are integrated in a closed control box, usually installed at the tower base of the generator set or close to the nacelle to facilitate signal transmission and equipment maintenance. The central control unit also includes the necessary communication hardware to support communication links with wind blades and independent communication paths with the safety monitoring unit.

[0053] In addition, the central control unit is designed with system reliability and safety in mind, using multiple redundancy designs such as dual power supply systems and fail-safe operation modes to ensure that the system can maintain basic operations or shut down safely when key components fail. Both the unit's software and hardware designs follow industry standards and best practices to ensure long-term stable operation and easy maintenance.

[0054] Furthermore, the blade angle control algorithm used by the central control unit is implemented using the following formula 1:

[0055] θ new =θ current ·W (1)

[0056] Among them, θ new is the new blade angle; θ current is the current blade angle; W is the weight adjustment coefficient, which is implemented using the following formula 2:

[0057]

[0058] Where V is the real-time wind speed; V norm is the normal operating wind speed; ΔS is the blade stress change; S max is the maximum stress that the blade can withstand; T is the ambient temperature; θ current is the current blade angle; α, β, γ and δ are adjustment coefficients determined by experimental data.

[0059] The variable pitch control system of the wind turbine generator set provided in this embodiment uses a specific control algorithm to adjust the angle of the wind blades to optimize energy capture and improve system efficiency. The key to the control algorithm implemented in the central control unit is to adjust the angle of the blades to cope with environmental changes and operating conditions. A mathematical model is used to calculate the new blade angle θ new .

[0060] Specifically, the new blade angle θ new The current blade angle θ current It is obtained by multiplying a weight adjustment coefficient \(W\). This weight adjustment coefficient W is calculated by the following formula:

[0061]

[0062] The specific meanings of each term in the formula are as follows:

[0063] 1.V: Real-time wind speed, which is the wind speed value measured in real time by the anemometer on the wind turbine.

[0064] 2.V norm: Normal operating wind speed, which is the standard wind speed determined based on the design specifications of the wind turbine and historical operating data, and usually represents the wind speed for the best efficiency of the wind turbine.

[0065] 3.ΔS: Blade stress change, which indicates the change in blade stress from the last measurement to the current one, measured by the stress sensor installed on the blade.

[0066] 4.S max : The maximum stress that the blade can withstand, which is the maximum safety stress value determined based on the physical properties of the blade material and the strength design specifications.

[0067] 5.T: Ambient temperature, the current ambient temperature obtained by a thermometer installed near the wind turbine.

[0068] 6. θ current : The current blade angle, which is the current blade angle measured in real time by the blade angle sensor.

[0069] 7.α, β, γ and δ: adjustment coefficients, which are derived based on a large amount of experimental data and simulation tests and are used to adjust the sensitivity and response speed of the blade angle response model.

[0070] Through the above formula, the control algorithm can comprehensively consider various factors that affect the performance of wind blades, such as changes in wind speed, physical stress of blades, ambient temperature and current angle of blades, and achieve precise adjustment of blade angles. This precise adjustment ensures that wind turbines can maintain optimal operating conditions under various environmental conditions, maximize energy output, and reduce the risk of damage caused by environmental changes.

[0071] By implementing this algorithm, the reliability and efficiency of wind power generation systems can be improved and their operating performance can be optimized.

[0072] Furthermore, the control algorithm includes an adaptive learning mechanism, which can automatically adjust the weight adjustment coefficients α, β, γ and δ based on long-term collected operation data to adapt to changing environmental conditions and improve the overall efficiency of the system.

[0073] In the wind turbine pitch control system, an advanced adaptive learning mechanism is introduced, which is designed to enhance the system's responsiveness and optimize its operating efficiency. Through this mechanism, the system can not only achieve instant blade angle adjustment, but also gradually optimize its control parameters based on the accumulated operating data, so as to better adapt to long-term environmental changes and operational requirements.

[0074] In the variable pitch control system of a wind turbine, the core function of the adaptive learning mechanism is to optimize system operation through a carefully constructed data analysis module. This module continuously collects and analyzes the performance data of the system under different environmental and operating conditions, such as wind speed, blade stress, ambient temperature, and the corresponding power generation efficiency and system stability. Through this comprehensive data monitoring, the mechanism can accurately understand how various operating variables affect the overall performance of the system.

[0075] The collected data is then used to train a statistical model, which is usually based on machine learning, such as regression models, decision trees or more complex neural network models. These models have the ability to learn and predict optimal parameter settings through historical data, thereby automatically identifying how control parameters should be adjusted to achieve optimal efficiency and stability under specific environmental and operating conditions.

[0076] For example, the model might analyze the optimal pitch angle of the blades in high wind conditions to reduce stress and prevent damage while maintaining high power generation efficiency. The model might also identify how system performance is affected in low temperature conditions and recommend appropriate parameter adjustments to optimize output and reduce mechanical problems caused by low temperatures.

[0077] To achieve this function, the model used by the learning mechanism may include a multi-layer neural network that can handle nonlinear relationships and simulate complex system dynamics. The input layer of the network receives real-time data (such as wind speed, blade angle, blade stress and temperature), the hidden layer processes these inputs and learns the complex relationships between the data, and the output layer generates specific parameter adjustment suggestions (such as adjustment coefficients α, β, γ and δ).

[0078] The model training process involves supervised learning using historical operating data, during which the model iteratively adjusts its internal weights to reduce the error between the predicted output and the actual result. This training enables the model to not only respond based on current data, but also predict the optimal response strategy under possible future operating conditions.

[0079] Through this detailed modeling and adaptive learning process, the wind turbine’s pitch control system is able to continuously optimize itself and adapt to changing environmental conditions, thereby ensuring the long-term efficiency and stability of the system.

[0080] Furthermore, the central control unit includes a fault detection module for analyzing the stress data and wind speed data of the blades in real time. When any abnormal data trend is detected, it automatically issues an alarm and adjusts the blade angle to a preset safety threshold to prevent potential mechanical damage.

[0081] In the wind turbine pitch control system provided in this embodiment, the central control unit includes a fault detection module, which is a key part of system safety and efficiency management. The main function of this module is to monitor and analyze the stress data and wind speed data transmitted from the wind blades in real time, so as to identify in advance any abnormal data trends that may cause system failure or blade damage. This early warning system ensures that the wind turbine can take prompt action when facing potential risks and adjust the blades to a safer angle, thereby protecting the equipment from damage.

[0082] The working principle of the fault detection module is as follows:

[0083] The fault detection module continuously receives real-time data about the stress and wind speed of the wind blades through high-precision sensors. These sensors are distributed on each blade and can measure and report the physical stress and real-time wind speed of the blade under different wind conditions.

[0084] 1. Data collection and analysis:

[0085] The fault detection module first performs a preliminary analysis of the collected data, including real-time monitoring of the data and long-term trend analysis.

[0086] Real-time monitoring focuses on immediate data, such as sudden increases in wind speed or stresses exceeding normal operating ranges.

[0087] Long-term trend analysis helps identify potential risks due to gradually changing environmental conditions or blade wear.

[0088] 2. Abnormal trend identification:

[0089] When the module identifies data anomalies, such as stress data showing a safety range beyond the design allowable range, or wind speed continuously exceeding the design limit of the blade, the system will automatically mark it as a potential fault.

[0090] This automated monitoring ensures that the system can identify risks before problems become serious failures.

[0091] 3. Early warning and automatic adjustment:

[0092] Once an abnormality is detected, the fault detection module will immediately send a warning signal to the system and trigger the automatic adjustment mechanism.

[0093] According to the preset safety protocol, the system automatically adjusts the angle of the affected blade to a safety threshold. This safety threshold is pre-set based on the design parameters of the wind turbine and historical operating data to minimize the impact or stress of wind on the blade.

[0094] To achieve this function, the fault detection module is equipped with advanced computing hardware and complex software algorithms, which are designed based on the latest machine learning and data processing technologies. The software algorithms continuously learn from operational experience and can gradually optimize their anomaly detection and response strategies over time. In addition, all hardware and software are designed to meet industry safety standards to ensure reliability and stability in various operating environments.

[0095] Furthermore, the central control unit includes an energy optimization module for dynamically adjusting the blade angle according to the received wind speed and power generation data to maximize the energy capture and conversion efficiency while reducing the energy loss caused by excessive wind speed.

[0096] In the wind turbine pitch control system provided in this embodiment, a particularly important component is added to the central control unit: the energy optimization module. The main function of this module is to maximize the energy capture and conversion efficiency by dynamically adjusting the angle of the wind blades, while also taking into account the need to reduce energy loss under high wind speed conditions. This function ensures that the wind turbine can operate more efficiently and safely under various wind speed conditions.

[0097] The specific ways to implement the function include:

[0098] 1. Data Collection and Analysis

[0099] The energy optimization module first receives real-time data from the wind blade sensor system, mainly including wind speed and power generation data. These data are the basis for the energy optimization module to make decisions and provide direct feedback on the current wind power generation status.

[0100] 2. Dynamic angle adjustment

[0101] Based on the received wind speed data, the energy optimization module calculates an optimal blade angle that maximizes power generation efficiency at the current wind speed. This calculation process involves a complex algorithm that takes into account the impact of wind speed changes on power generation efficiency and the physical limitations of blade angle adjustment.

[0102] For example, when the wind speed increases, the traditional approach may be to adjust the blades to a flatter angle to avoid excessive wind damage to the equipment. However, the energy optimization module will calculate an optimal angle that allows the blades to withstand higher wind speeds without damage based on the actual wind speed and the current state of the blades, which not only protects the equipment but also optimizes energy capture.

[0103] 3. Minimize energy loss

[0104] When the wind speed is too high, the energy optimization module also has the function of adjusting the blade angle to reduce energy loss. When the wind speed exceeds a certain threshold, the traditional control system may choose to shut down to prevent equipment damage. The energy optimization module finely adjusts the blade angle so that the blades can continue to operate under safe conditions, thereby reducing the downtime that may be caused by excessive wind speed and optimizing the overall energy output of the system.

[0105] Energy optimization modules are implemented through software algorithms that are embedded in the firmware or software of the central control unit. When implemented, it is necessary to ensure that these algorithms can receive accurate sensor data and can send adjustment commands to the blade drive system in real time. In addition, the algorithm needs to be able to self-calibrate at system startup to ensure that its decisions are based on the latest system performance data.

[0106] The safety monitoring unit 103 is physically isolated from the central control unit and can intervene independently of the central control unit when any abnormal operation of the wind blade is detected, and perform emergency angle adjustment on all wind blades to ensure the safe operation of the wind turbine generator set; wherein the safety monitoring unit includes a safety chain feedback mechanism for starting a backup control path when the central control unit fails, and directly controlling the blade angle adjustment through an independent communication link.

[0107] The safety monitoring unit 103 plays a vital role in the wind turbine pitch control system. Its main task is to monitor and ensure the safety of the system, especially when potential operating problems or system failures occur. The unit is physically isolated from the central control unit 102 to ensure that even if the main control system fails, the safety monitoring unit can operate independently to maintain the safety and functionality of the system.

[0108] The safety monitoring unit has independent receiving capabilities and can directly receive real-time operating data from each wind blade, including but not limited to blade angle, wind speed, blade stress and power generation. This information is critical for assessing the current status and possible risks of the blades. The software system built into the safety monitoring unit continuously analyzes this data to monitor whether any data indicators are beyond the normal operating range. Once abnormal data is detected, such as excessive blade stress or a sudden increase in wind speed, the unit will take prompt action.

[0109] One of the core functions of the unit is the safety chain feedback mechanism, which is designed to automatically activate the backup control path in the event of a failure of the central control unit or when a serious operating anomaly is detected. This backup control path operates via an independent communication link, ensuring complete independence of communication with the central control unit. In backup mode, the safety monitoring unit can send instructions directly to the control interface of each blade to adjust the blade to a predefined safe position, which usually means adjusting the blade to an angle that reduces the impact of wind loads to prevent damage to the blade when the wind speed is too high.

[0110] To achieve these functions, the safety monitoring unit is equipped with high-reliability electronic components and enhanced software programs. These components and programs are designed with redundancy and fail-safe functions to ensure reliable mission execution even under extreme or adverse environmental conditions. In addition, the unit is also equipped with a power backup system and environmental monitoring sensors to adapt to various external influences and ensure continuous and stable operation.

[0111] In addition, all designs and component selections comply with industry standards and safety specifications, ensuring that the system can not only effectively respond to various operational anomalies, but also protect the safe operation of wind turbines at critical moments.

[0112] Furthermore, the safety chain feedback mechanism is specifically used for:

[0113] Monitor the real-time operation data from the wind blades, and trigger the safety chain signal when it is detected that the real-time operation data exceeds the preset safety parameter range;

[0114] The normal control channel to the central control unit is isolated and the backup control path is activated;

[0115] Sending instructions to the angle adjuster of each wind blade through the backup control path; wherein the instructions include instructions to adjust the blade to a safe position;

[0116] When it is detected that the real-time operating data returns to the preset safety parameter range, the normal control path of the central control unit is restarted and the backup path is closed.

[0117] In the wind turbine pitch control system provided in this embodiment, the safety chain feedback mechanism is a crucial safety feature, which ensures that the system can respond quickly and effectively when potential dangers or system failures occur, thereby protecting the safe operation of the entire device. The safety chain feedback mechanism is described in detail below.

[0118] First, the safety chain feedback mechanism is embedded in the system, which continuously monitors the real-time operation data from each wind blade, including but not limited to the blade angle, wind speed, blade stress and other key performance indicators. These data are regularly analyzed to determine whether they are within the preset safety parameters. Once these real-time data exceed the safety range, such as excessive blade stress or abnormal increase in wind speed, the safety chain feedback mechanism is immediately activated, triggering the safety chain signal.

[0119] After the safety chain signal is triggered, the safety chain feedback mechanism will automatically be isolated from the conventional control channel of the central control unit to prevent further erroneous operations caused by problems with the central control unit. At the same time, the mechanism will start a backup control path. This backup control path is pre-set and completely independent of the conventional control system. It is usually operated through an independent communication network to ensure that it can still operate independently when the main system fails.

[0120] Through the backup control path, the safety chain feedback mechanism sends direct commands to the angle adjusters of each wind blade. These commands include adjusting the blade to a predefined safe position, usually adjusting the blade angle to a state that minimizes the impact of wind pressure to protect the blade from potential damage caused by excessive wind speed.

[0121] Once the system detects that the real-time operating data has returned to the safe preset parameter range, the safety chain feedback mechanism will instruct the system to restart the normal control path of the central control unit and close the backup control path. This transition ensures that the system can smoothly return to normal operation after confirming safety, while maintaining system stability and power generation efficiency.

[0122] Furthermore, the safety chain feedback mechanism uses the following formula 3 to calculate the safe position:

[0123] Safe position calculation formula:

[0124] θ safe =θ current +f×sgn(θ optimal ―θ current )(3)

[0125] Among them, θ safe is the calculated safe blade angle; θ current is the current blade angle; θ optimal is the ideal blade angle calculated through historical data and environmental feedback; the sgn function is a sign function when θ optimal >θ current Returns 1 if true, otherwise returns -1.

[0126] f is an adjustment function based on environmental factors and the current state of the blade, which is implemented using the following formula 4:

[0127]

[0128] Among them, V crit is the critical wind speed determined by experiment; V is the real-time wind speed; T norm is the normal ambient temperature determined by experiment; ΔS is the blade stress change; T is the ambient temperature; S max It is the maximum stress that the blade can withstand.

[0129] In the wind turbine pitch control system provided in this embodiment, the safety chain feedback mechanism is crucial to ensure that the system maintains safe operation under different environmental conditions. This mechanism determines the safe position of the wind blades through a specific calculation model, which is calculated based on the current environmental state and the real-time data of the wind blades. The following details the source and implementation of this calculation process and each of its components.

[0130] The safe position calculation formula is defined as:

[0131] θ safe =θ current +f×sgn(θ optimal ―θ current )(3)

[0132] Here, θ safe represents the calculated safe blade angle, and θ current is the current angle of the blade. The θ in the formula optimal It is the ideal blade angle calculated based on historical data and environmental feedback. It reflects the blade angle that can achieve the best energy efficiency under given environmental conditions.

[0133] Function f is an adjustment function that dynamically adjusts the amplitude of the angle change according to environmental factors and the current state of the blade. Specifically, the calculation formula of f is:

[0134]

[0135] The following is an explanation of each item in Formula 4:

[0136] V is the wind speed measured in real time, V crit It is the critical wind speed determined through experiments and historical data, which means that wind speeds exceeding this speed may cause damage to the blades.

[0137] ΔS is the change in blade stress, which indicates the change in blade stress from the last measurement to the current measurement, and is measured by the stress sensor installed on the blade. maxis the maximum stress the blade can withstand, and these two values ​​are used to assess the physical condition of the blade and adjust its angle to avoid excessive stress.

[0138] T is the real-time ambient temperature. norm The normal operating ambient temperature. Wind turbine manufacturers will determine an ideal operating temperature T based on the average climatic conditions of the target installation environment during design. norm This temperature is usually based on long-term meteorological data, taking into account the highest and lowest temperatures expected at the installation site. By comparing the difference between these two temperatures, the blades are adjusted to suit the different temperature conditions.

[0139] is a trigonometric function based on the current blade angle and is used to adjust the nonlinear response of blade angle changes.

[0140] Through this calculation formula, the safety chain feedback mechanism can dynamically adjust the blade angle according to the actual operating conditions of the blades and external environmental factors at any given moment to ensure the safety and efficiency of the wind turbine. This calculation method provides an accurate and reliable way to deal with possible environmental risks, thereby ensuring the stable operation of wind turbines under various operating conditions.

[0141] Furthermore, the calculation of the safe position also takes into account the correlation with wind direction. The pitch control system monitors the wind direction changes in real time through a wind direction sensor and adjusts the blade angle to minimize the direct impact of wind on the blades, thereby protecting the blades from damage under extreme weather conditions.

[0142] In the wind turbine pitch control system provided in this embodiment, the importance of considering wind direction changes for blade adjustment strategies is particularly emphasized to ensure the safety and efficiency of blades under extreme weather conditions. The system monitors wind direction in real time through an integrated wind direction sensor, which is achieved by installing one or more wind direction sensors on the top of the wind turbine or other appropriate locations. These sensors can accurately capture the direction of the wind, which is crucial for controlling the angle of the blades.

[0143] In order to achieve the response of the wind turbine pitch control system to the change of wind direction, this embodiment adopts a detailed control algorithm, which can not only dynamically adjust the angle of the wind blades to cope with the change of wind direction, but also optimize the power generation efficiency and reduce the direct impact of wind on the blades. The following is a specific implementation method of the control algorithm.

[0144] Wind direction sensors are key devices installed on the top of wind turbines or other unobstructed locations to capture wind from all directions. These sensors can monitor and record wind direction in real time and transmit wind direction data in the form of electronic signals to the central control unit in real time. These data include wind direction and wind speed, which is one of the input parameters of the control algorithm.

[0145] The core of the control algorithm is a real-time data processing module that analyzes the input data from the wind direction sensor and calculates the optimal angle of the blades according to the wind direction changes. The specific steps of the algorithm are as follows:

[0146] 1. Data reception and preliminary analysis:

[0147] The system first determines the current direction of the wind and compares it to the current angle of the blades.

[0148] Calculate the difference in angle between wind direction and the face-to-face angle of the blades. This difference is the basis for adjusting the blades.

[0149] 2. Calculate the adjustment angle:

[0150] Use the following formula to calculate the blade adjustment angle:

[0151] Δθ=κ×(θ wind ―θ current )

[0152] Among them, θ wind is the wind direction angle, θ current is the current angle of the blade, and κ is a coefficient adjusted according to the actual wind turbine model characteristics and site conditions, which is used to control the adjustment range.

[0153] 3. Dynamic Optimization and Execution:

[0154] The system dynamically adjusts the blade angle according to the calculated Δθ to minimize the direct impact of wind on the blades.

[0155] Adjustments are performed by electric motors on the blades, which adjust the actual physical position of the blades based on instructions from a central control unit.

[0156] 4. Feedback and iteration:

[0157] The system continuously monitors the adjusted blade performance as well as environmental changes, such as further changes in wind speed and direction.

[0158] Based on the blade’s performance and safety data, the system may further fine-tune the κ value to optimize the response strategy and improve efficiency.

[0159] This algorithm not only reflects a rapid response to real-time environmental changes, but also reflects the continuous optimization of the overall system performance. Through real-time wind direction monitoring and intelligent angle adjustment, the algorithm can ensure that wind turbines can operate safely and efficiently in all weather conditions.

[0160] Furthermore, the safety chain feedback mechanism includes a data reset mechanism for automatically resetting the control settings of all blades to an initial state after the blade operating data returns to a preset safety parameter range, thereby ensuring that the system can operate with an optimal configuration after restarting.

[0161] In the wind turbine generator set pitch control system provided in this embodiment, a safety chain feedback mechanism is particularly introduced, including a data reset mechanism, which is to ensure that when the operation data of the wind blades returns to the preset safety parameter range after emergency adjustment, the system can effectively restore to its optimal operating state. The data reset mechanism is described in detail below.

[0162] The data reset mechanism works as follows:

[0163] When the blades of a wind turbine deviate from normal operating parameters due to extreme conditions or other reasons, the safety chain feedback mechanism intervenes and adjusts the blades to a safe position to prevent damage. Once the environmental conditions stabilize and the blade operating data falls into the safe range again, the data reset mechanism is activated, the purpose of which is to restore all relevant control settings to the initial or optimal state.

[0164] The data reset mechanism first verifies that the blade operating data is fully back within the designed safety parameters. This includes checking whether key indicators such as wind speed, blade stress, blade angle, etc. meet the system's safe operating standards. Once it is confirmed that these parameters have returned to normal, the mechanism will instruct the central control unit to automatically reset the blade control settings.

[0165] The implementation steps include:

[0166] 1. Monitoring and evaluation: Continuously monitor the current operating data of the blades, including wind speed, blade angle, blade stress, etc. Evaluate whether these data have stabilized and returned to the preset safety range.

[0167] 2. Activate data reset: Once the data is confirmed to be safely restored, the system automatically triggers the data reset process. This process involves sending a reset command to the blade's control unit, instructing them to adjust all settings back to the original or preset optimal state.

[0168] 3. Perform reset operation: After receiving the reset command, each control unit adjusts the blade angle, speed and other adjustable parameters to the initial settings. These settings are configured based on the system design parameters and previous best operating experience.

[0169] 4. Verification and adjustment: After reset, the system performs a short run test to verify that the blades and other system components are working as expected. If any parameter is detected to fail to achieve the expected effect, the system will automatically make fine adjustments.

[0170] 5. System restart: After confirming that all settings have been successfully reset and the system is running stably, the system is ready to restart the normal operation mode. At this time, the system should be in the optimal configuration state to ensure maximum power generation efficiency and minimize operating risks.

[0171] Through the above detailed operation process, this embodiment ensures that the wind turbine generator can quickly recover to the optimal operating state after facing unstable environmental conditions, thereby enhancing the reliability and safety of the system.

[0172] In the above embodiment, a variable pitch control system of a wind turbine generator set is provided. Correspondingly, the present application also provides a variable pitch control method of a wind turbine generator set. Figure 2 , which is a flow chart of an embodiment of a variable pitch control method for a wind turbine generator set of the present application. Since this embodiment, i.e., the second embodiment, is basically similar to the first embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the first embodiment. The method embodiment described below is only illustrative.

[0173] A second embodiment of the present application provides a pitch control method for a wind turbine generator set, comprising:

[0174] Step S201: receiving real-time operation data of each wind blade, including blade angle, wind speed, blade stress and power generation;

[0175] Step S202: Based on the received real-time operation data, use a predefined control algorithm to adjust the angle of each wind blade to optimize performance;

[0176] Step S203: continuously monitoring the operating data of all wind blades, and immediately executing emergency intervention measures when the data indicates that any wind blade is operating abnormally;

[0177] Step S204: when an operational abnormality is detected, activating a backup control path to perform emergency angle adjustment on the wind blades independently of the conventional control process; the emergency angle adjustment includes adjusting all blades to a safe angle, which is calculated based on the current environment and operating conditions to minimize potential risks;

[0178] Step S205: When the detected operating data shows that the wind blade has returned to a safe operating range, the backup control path is terminated and the normal control path is restored.

[0179] In this embodiment, a variable pitch control method for a wind turbine is provided, which aims to ensure that the wind blades can operate in an optimal state under various environments through detailed steps, and can respond quickly to protect the system safety when an abnormality occurs. The following is a description of the control method.

[0180] First, the system starts to collect real-time operating data of each wind blade. This data includes the current angle of the blade, wind speed, stress on the blade, and power generation. This data is collected in real time by sensors installed on the blades and units, and transmitted to the central control unit through the communication network. The accurate collection of real-time data is the basis of the entire control method, because this data will directly affect all subsequent decisions and adjustments.

[0181] Next, the system uses a predefined control algorithm based on the collected data to adjust the angle of each wind blade. This algorithm is developed based on the design parameters of the wind turbine and historical performance data to optimize the blade angle under different wind conditions to maximize power generation efficiency and reduce mechanical stress caused by excessive or unstable wind speeds.

[0182] The system also continuously monitors the operating data of all wind blades to ensure that each blade is working properly. Once any abnormal data is detected, such as a sudden increase in wind speed that exceeds the limit of the blade design or excessive blade stress, the system will immediately perform emergency intervention measures. These measures include activating a backup control path that is independent of the regular control process and can continue to operate in the event that the central control unit fails.

[0183] When the backup control path is activated, the system adjusts all blades to a safe angle. This safe angle is calculated based on the current environmental and operating conditions and is designed to minimize potential risks, such as reducing the direct impact of wind on the blades by lowering the blade angle.

[0184] Finally, when the system detects that the operating data of all blades have returned to a safe preset range, that is, the wind speed has dropped to a safe level and the blade stress has returned to the normal range, the system terminates the backup control path and reactivates the regular control path. This step ensures that the wind turbine can smoothly return to normal operation after safety is confirmed.

[0185] Through such detailed operation steps, this embodiment not only improves the operational safety and efficiency of the wind turbine generator set, but also ensures that the system can respond quickly and effectively when faced with emergencies.

[0186] Although the present application is disclosed as above in the form of a preferred embodiment, it is not intended to limit the present application. Any technical personnel in this field may make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

Claims

1. A variable pitch control system for a wind turbine generator set, characterized in that: include: A set of wind blades, each wind blade has independent angle adjustment function; A central control unit is configured with a receiver for receiving real-time operation data from each wind blade, and adjusting the angle of each wind blade based on the operation data using a blade angle control algorithm; wherein the real-time operation data includes blade angle, wind speed, blade stress and power generation; A safety monitoring unit is physically isolated from the central control unit and can intervene independently of the central control unit when any abnormal operation of the wind blade is detected to perform emergency angle adjustment on all wind blades to ensure safe operation of the wind turbine generator set; wherein the safety monitoring unit includes a safety chain feedback mechanism for activating a backup control path when the central control unit fails and directly controlling the blade angle adjustment through an independent communication link; The blade angle control algorithm used by the central control unit is implemented using the following formula 1: i new =θ current ·W (1) Among them, θ new is the new blade angle; θ current is the current blade angle; W is the weight adjustment coefficient, which is implemented using the following formula 2: Where V is the real-time wind speed; V norm is the normal operating wind speed; ΔS is the blade stress change; S max is the maximum stress that the blade can withstand; T is the ambient temperature; θ current is the current blade angle; α, β, γ and δ are adjustment coefficients determined by experimental data; Wherein, the control algorithm includes an adaptive learning mechanism, which can automatically adjust the weight adjustment coefficients α, β, γ and δ according to the operation data collected over a long period of time to adapt to the changing environmental conditions and improve the overall efficiency of the system.

2. The variable pitch control system of a wind turbine generator set according to claim 1, characterized in that: The safety chain feedback mechanism is specifically used for: Monitor the real-time operation data from the wind blades, and trigger the safety chain signal when it is detected that the real-time operation data exceeds the preset safety parameter range; The normal control channel to the central control unit is isolated and the backup control path is activated; Sending instructions to the angle adjuster of each wind blade through the backup control path; wherein the instructions include instructions to adjust the blade to a safe position; When it is detected that the real-time operating data returns to the preset safety parameter range, the normal control path of the central control unit is restarted and the backup path is closed.

3. The variable pitch control system of a wind turbine generator set according to claim 2, characterized in that: The safety chain feedback mechanism uses the following formula 3 to calculate the safe position: Safety position calculation formula: i safe =θ current +f×sgn(θ optimal ―θ current (3) Among them, θ safe is the calculated safe blade angle; θ current is the current blade angle; θ optimal is the ideal blade angle calculated through historical data and environmental feedback; f is an adjustment function based on environmental factors and the current state of the blade, which is implemented using the following formula 4: Among them, V crit is the critical wind speed determined by experiment; V is the real-time wind speed; T norm is the normal ambient temperature determined by experiment; ΔS is the blade stress change; T is the ambient temperature; S max It is the maximum stress that the blade can withstand.

4. The variable pitch control system of a wind turbine generator set according to claim 3, characterized in that: The calculation of the safe position also takes into account the correlation with wind direction. The pitch control system monitors the wind direction changes in real time through a wind direction sensor and adjusts the blade angle to minimize the direct impact of wind on the blade, thereby protecting the blade from damage under extreme weather conditions.

5. The variable pitch control system of a wind turbine generator set according to claim 2, characterized in that: The safety chain feedback mechanism includes a data reset mechanism for automatically resetting the control settings of all blades to an initial state after the blade operation data returns to a preset safety parameter range, thereby ensuring that the system can operate in an optimal configuration after restarting.

6. The variable pitch control system of a wind turbine generator set according to claim 1, characterized in that: The central control unit includes a fault detection module for analyzing the stress data and wind speed data of the blades in real time. When any abnormal data trend is detected, it automatically issues an early warning and adjusts the blade angle to a preset safety threshold to prevent potential mechanical damage.

7. The pitch control system of a wind turbine generator set according to claim 1, characterized in that: The central control unit includes an energy optimization module for dynamically adjusting the blade angle to maximize energy capture and conversion efficiency based on the received wind speed and power generation data, while reducing energy losses caused by excessive wind speed.

8. A variable pitch control method for a wind turbine generator set, characterized in that: include: Receiving real-time operation data of each wind blade, wherein the real-time operation data includes blade angle, wind speed, blade stress and power generation; Based on the real-time operational data received, the angle of each wind blade is adjusted using a predefined control algorithm to optimize performance; Continuously monitor the operating data of all wind blades and immediately implement emergency intervention measures if the data indicates abnormal operation of any wind blade; When an operational anomaly is detected, a backup control path is activated to perform an emergency angle adjustment of the wind blades independently of the normal control process; the emergency angle adjustment includes adjusting all blades to a safe angle, which is calculated based on the current environmental and operating conditions and is intended to minimize potential risks; When the detected operation data shows that the wind blade has returned to a safe operation range, the backup control path is terminated and the normal control path is restored; The predefined control algorithm is implemented using the following formula 1: i new =θ current ·W (1) Among them, θ new is the new blade angle; θ current is the current blade angle; W is the weight adjustment coefficient, which is implemented using the following formula 2: Where V is the real-time wind speed; V norm is the normal operating wind speed; ΔS is the blade stress change; S max is the maximum stress that the blade can withstand; T is the ambient temperature; θ current is the current blade angle; α, β, γ and δ are adjustment coefficients determined by experimental data; Wherein, the control algorithm includes an adaptive learning mechanism, which can automatically adjust the weight adjustment coefficients α, β, γ and δ according to the operation data collected over a long period of time to adapt to the changing environmental conditions and improve the overall efficiency of the system.

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