Wind driven generator remote control system and method suitable for complex environment

Abstract

The invention relates to the technical field of wind power generation, and discloses a wind driven generator remote control system suitable for a complex environment, which comprises a data acquisition module, a data transmission module, a remote monitoring module, a dynamics modeling module and an optimal control calculation module, and also discloses a wind driven generator remote control method suitable for the complex environment. Comprising the following steps that state information of a wind driven generator is collected, and the state information comprises the wind speed, the fan rotating speed, the pitch angle, the generated power and the power grid power requirement; based on the state information of the wind driven generator, establishing a kinetic model of the wind driven generator; and based on the kinetic model of the wind driven generator, solving an optimal variable pitch angle control strategy. By introducing the optimal control algorithm and the real-time feedback mechanism, the accurate adjustment of the pitch angle of the fan is realized, and the fan is ensured to quickly adapt to and always keep the optimal operation state in a complex environment, so that the power generation efficiency is improved, and the equipment loss is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a remote control system and method for a wind power generator suitable for complex environments. Background Art

[0002] With the rapid development of renewable energy technology, wind power generation has become an important part of the global energy transformation. As a wind energy conversion device, wind turbines are widely used in various wind farms. However, due to the fluctuation and change of natural wind speed, the stability and efficiency of traditional wind turbine control systems are often challenged. In order to improve the operating efficiency of wind turbines and ensure their stability under complex environmental conditions, the optimization and adjustment of wind turbine pitch angles has become a crucial technology.

[0003] In the prior art, wind turbines generally use a simple preset pitch angle control method, which usually relies on wind speed measurement to adjust the working state of the wind turbine. This type of control method can achieve basic wind turbine operation, but is limited to situations where the wind speed changes are small or relatively stable. In some conventional wind power generation systems, the pitch angle is adjusted mainly by simple adjustment through wind speed or load changes. This method can ensure the stable operation of the wind turbine under most normal operating conditions and has certain reliability and efficiency.

[0004] However, there are significant deficiencies in the existing technology. First, traditional wind turbine control strategies are often unable to cope with sudden changes in wind speed or complex climate changes, causing the wind turbine to be in a non-optimal operating state for a long time, affecting power generation efficiency. Secondly, the existing control algorithm has a slow response speed and fails to adapt to the dynamic changes of wind speed, rotation speed and other variables in real time. This makes the wind turbine unable to adjust the pitch angle in time in some environments, resulting in increased wear of mechanical components. Furthermore, many traditional wind turbine control systems rely only on wind speed data, ignoring other influencing factors such as wind turbine load and mechanical status, and are unable to achieve global optimization, resulting in excessive burden on equipment and energy waste. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides a remote control system and method for a wind turbine suitable for complex environments, which solves the problems in the prior art of insufficient flexibility in adjusting the pitch angle of the wind turbine, slow response, and inability to adapt to changes in complex environments in real time.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A remote control system for a wind turbine generator suitable for complex environments, comprising: A data acquisition module is used to collect status information of the wind turbine, wherein the status information includes wind speed, wind turbine speed, pitch angle, generated power and grid power demand; A data transmission module, used for receiving the status information sent by the data acquisition module, and transmitting the status information to the remote monitoring module in the remote monitoring system through a remote communication network; A remote monitoring module, used to receive the wind turbine status information transmitted by the data transmission module, monitor the operating status of the wind turbine, and optimize the operating efficiency of the wind turbine by adjusting the weight parameters of the control strategy according to environmental changes and the actual working conditions of the wind turbine; A dynamic modeling module, which establishes a dynamic model of the wind turbine based on the state information of the wind turbine and the adjustment parameters fed back by the remote monitoring module, wherein the dynamic model is used to characterize the working state and system performance of the wind turbine; An optimal control calculation module calculates the optimal variable pitch control strategy of the wind turbine according to the dynamic model and the control parameters provided by the remote monitoring module, and transmits the calculation result to the execution control module; The execution control module is used to receive the optimal variable pitch control strategy transmitted by the optimal control calculation module, and adjust the pitch angle of the wind turbine based on the control strategy.

[0007] Preferably, the data acquisition module includes: Wind speed sensor, used to collect wind speed data in real time; Fan speed sensor, used to collect fan speed data; A pitch angle sensor is used to collect pitch angle data; Power generation sensor, used to collect power generation data of the wind turbine; The power grid power demand sensor is used to collect the power grid's power demand data for wind turbines.

[0008] Preferably, the data transmission module includes: A wireless communication unit, used to transmit the collected wind turbine status information to a remote monitoring system by wireless means; A remote communication network interface is used to transmit the status information to a remote monitoring module via 5G and satellite communications.

[0009] Preferably, the remote monitoring module includes: A status monitoring unit is used to receive status information of wind turbines in real time and generate a visual interface of wind turbine operation data; The control strategy adjustment unit is used to adjust the weight parameters in the control strategy according to real-time monitoring data and changes in the external environment.

[0010] Preferably, the kinetic modeling module comprises: A wind turbine dynamics model generation unit generates a mathematical model describing the dynamic performance and control characteristics of the wind turbine based on the wind turbine status information; The parameter adjustment unit is used to adjust the model parameters according to the feedback information provided by the remote monitoring module to adapt to the changes in the environment and the operating status of the fan.

[0011] Preferably, the optimal control calculation module includes: An optimal control algorithm unit, which uses an optimization algorithm to calculate an optimal pitch angle control strategy according to a wind turbine dynamics model, the wind turbine status information and adjustment parameters provided by a remote monitoring module; The deep learning unit is used to approximate the optimal control strategy using a deep approximate dynamic programming method and adjust the pitch angle control strategy in real time.

[0012] Preferably, the execution control module includes: A pitch angle adjustment unit, used to receive the optimal pitch angle control strategy transmitted by the optimal control calculation module, and adjust the pitch angle of the wind turbine according to the strategy; The real-time execution unit is used to execute the received optimal pitch angle control strategy locally in real time and report the execution result to the remote monitoring module.

[0013] Preferably, the parameter adjustment unit: A feedback receiving unit receives the wind turbine status information feedback from the remote monitoring module, and adjusts the relevant parameters of the wind turbine dynamics model according to the changes in the real-time parameters of wind speed, wind turbine speed and pitch angle; A model adjustment unit dynamically adjusts the parameters of the wind turbine dynamics model according to the received real-time feedback information; The adaptive adjustment unit adjusts the control strategy parameters in the dynamic model according to real-time monitoring data and feedback information, and optimizes the response capability and efficiency of the fan in various complex environments.

[0014] Preferably, the optimal control algorithm unit: The optimization calculation unit uses a mathematical optimization algorithm to calculate the optimal pitch angle control strategy based on the dynamic model of the wind turbine, the state information and the adjustment parameters provided by the remote monitoring module; A deep learning module is used to apply a deep approximate dynamic programming method, combining historical data and environmental changes to approximate the optimal pitch angle control strategy through a deep neural network; The strategy adjustment unit adjusts the parameters of the optimal control algorithm according to the real-time collected wind turbine status and external environment information.

[0015] The present invention also provides a remote control method for a wind turbine generator applicable to a complex environment, comprising the following steps: Collecting status information of wind turbines, including wind speed, wind turbine speed, pitch angle, generated power and grid power demand; Based on the state information of the wind turbine, a dynamic model of the wind turbine is established; Based on the dynamic model of the wind turbine, an optimal variable pitch angle control strategy is solved, wherein the solution adopts the HJB equation; The HJB equation is numerically solved by deep approximate dynamic programming method, and the optimal pitch angle control strategy is approximated by deep neural network. The optimal variable pitch angle control strategy is transmitted to the execution control module to adjust the pitch angle of the wind turbine; According to the real-time status of the wind turbine and environmental changes, the weight parameters of the optimal control strategy are dynamically adjusted through the remote control system.

[0016] The present invention provides a remote control system and method for wind turbines suitable for complex environments, which has the following beneficial effects: 1. The present invention adopts an optimal variable pitch control strategy based on a dynamic model and real-time feedback control, achieving the technical effect of accurately controlling the pitch angle of the wind turbine under complex environmental conditions. Compared with the traditional fixed pitch angle adjustment method in the prior art, the present invention can dynamically adjust the pitch angle according to multiple factors such as wind speed and wind turbine speed, effectively avoiding the performance loss of the wind turbine caused by improper pitch angle, thereby improving power generation efficiency and equipment life.

[0017] 2. The present invention adopts the technical solution of the optimal control calculation module and the execution control module working together to achieve the technical effect of real-time response and precise execution. Compared with the relatively cumbersome and long-delay control method in the prior art, the present invention can quickly receive the optimal control strategy and adjust the pitch angle in real time, so that the wind turbine can quickly adapt to the changing environment and always maintain an efficient operating state, reducing the energy waste caused by response delay.

[0018] 3. The present invention adopts a multi-level feedback mechanism to adjust the pitch angle of the wind turbine, achieving the technical effect of high-precision regulation. Different from the single feedback control in traditional technology, the present invention combines the dynamic feedback of multiple factors such as real-time wind speed and wind turbine load, ensuring that the adjustment of the pitch angle is more accurate and flexible, avoiding overload or inefficient operation of the wind turbine at different wind speeds, and improving the stability and overall performance of the wind turbine system.

[0019] 4. The present invention combines model predictive control (MPC) with optimal control calculation to achieve the technical effect of optimizing the operating efficiency of the wind turbine. Compared with the simpler control strategy in the prior art, the present invention can make optimal adjustments in advance when the wind speed changes through complex mathematical modeling and real-time prediction, ensuring the efficient operation of the wind turbine in extreme weather, reducing unnecessary mechanical wear and extending the service life of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of the system construction of the present invention; Figure 2 It is a data acquisition module framework diagram of the present invention; Figure 3 It is a framework diagram of the data transmission module of the present invention; Figure 4 This is a framework diagram of the remote monitoring module of the present invention; Figure 5 It is a framework diagram of the kinetic modeling module of the present invention; Figure 6 It is the framework diagram of the optimal control calculation module of the present invention; Figure 7 It is a framework diagram of the execution control module of the present invention; Figure 8 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the specification 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.

[0022] Please refer to the attached Figure 1 -Attached Figure 7 The embodiment of the present invention provides a remote control system for a wind turbine generator suitable for a complex environment, including: A data acquisition module is used to collect status information of wind turbines, including wind speed, wind turbine speed, pitch angle, generated power and grid power demand; The data acquisition module plays a vital role in obtaining real-time working status information from the wind turbine. This information covers key parameters such as wind speed, wind turbine speed, pitch angle, power generation and grid power demand. All data needs to be transmitted to the remote monitoring system through the data transmission module for processing and analysis.

[0023] Specifically, the main task of the data acquisition module is to monitor the operating status of the wind turbine in real time through various sensors and generate real-time data during the operation of the wind turbine. These data not only provide basic information for the status evaluation of the wind turbine, but also provide crucial data support for subsequent control optimization, fault diagnosis, energy efficiency evaluation, etc.

[0024] In this embodiment, the data acquisition module includes multiple sensors, which are used to collect information such as wind speed, wind turbine speed, pitch angle, power generation and grid power demand, etc. These sensors can collect data with high precision and high frequency to ensure the timeliness and accuracy of the data.

[0025] The wind speed sensor is one of the core parts of the data acquisition module. It is used to monitor the wind speed changes in the environment in real time. In the operation of wind turbines, wind speed is an important factor affecting the power generation and wind turbine load. The wind speed sensor uses high-precision wind speed sensing technology, which can measure the wind speed in real time and transmit the data to the data acquisition module.

[0026] Changes in wind speed will directly affect the operating efficiency of the wind turbine. In general, the higher the wind speed, the greater the output power of the wind turbine. However, too high a wind speed will cause excessive mechanical load on the wind turbine, so it is necessary to collect wind speed data in a timely manner in order to adjust the pitch angle of the wind turbine or stop operation to avoid equipment damage.

[0027] As an option, wind speed sensors can measure wind speed using different working principles (such as hot wire method, ultrasonic method, etc.). Different types of sensors have their own advantages and limitations, and the specific method to be used can be determined based on actual needs.

[0028] The wind turbine speed sensor is responsible for real-time monitoring of the speed of the wind turbine. The speed is an important parameter that reflects the operating status of the wind turbine. If the wind turbine speed is too low, it means that the wind turbine has not reached its rated operating speed, which may result in insufficient power generation; if the speed is too high, it may cause excessive wear of the equipment or safety risks.

[0029] In this embodiment, the fan speed sensor adopts high-precision speed sensing technology, which can obtain the fan speed data in real time and transmit it to the data acquisition module. The speed information can help the system determine whether the fan is in the best operating state. According to the relationship between the fan speed and the wind speed, the fan's working efficiency and power generation can be calculated, thereby providing a basis for subsequent control strategy adjustments.

[0030] The pitch angle sensor is used to collect the pitch angle of the wind turbine blades. The pitch angle is a key parameter for adjusting the wind turbine power and reducing the blade load. By monitoring the pitch angle in real time, the system can determine the power output of the wind turbine and the load of the blades, thereby optimizing the operation of the wind turbine.

[0031] During the operation of a wind turbine, the adjustment of the pitch angle is the key to achieving efficient power generation. When the wind speed changes, the adjustment of the pitch angle can keep the wind turbine in the optimal power output range while reducing the load caused by excessive wind speed. Generally, the pitch angle sensor will make corresponding adjustments based on the instructions of the wind turbine control system, thereby optimizing the power output of the wind turbine and extending the life of the equipment.

[0032] The power sensor is used to monitor the power generated by the wind turbine in real time. By obtaining the real-time power generated by the wind turbine, the system can determine whether the wind turbine is operating within its rated power range. When the power generation is lower than the preset value, it may mean that the wind turbine is faulty or the environmental conditions are not good; when the power generation is too high, it may cause the wind turbine to be overloaded, increasing the risk of equipment wear.

[0033] In this embodiment, the power sensor uses a current-voltage conversion method or a power measurement algorithm to collect and calculate the power output of the wind turbine in real time. The data acquisition module transmits the power data to the remote monitoring module for subsequent optimization and adjustment of the control algorithm.

[0034] The grid power demand sensor is used to obtain the power demand of the grid for wind turbines in real time. In modern wind power generation systems, the power output of wind turbines must not only be adjusted according to wind speed, but also needs to be adjusted in real time according to the load demand of the grid. Changes in grid power demand will affect the power generation strategy of wind turbines, thereby affecting the operating efficiency of wind turbines.

[0035] The grid power demand sensor can obtain the grid load data in real time through the interface with the grid dispatching system. According to the grid demand, the wind turbine can maintain the power balance between the wind turbine and the grid by adjusting the pitch angle, wind speed control, etc., to avoid the risk of grid instability caused by excessive or insufficient wind turbine output power.

[0036] Through the cooperation of the above sensors, the data acquisition module can collect various status information of the wind turbine in real time. This information includes wind speed, wind turbine speed, pitch angle, power generation and grid power demand, etc., which form a complete wind turbine status data stream on the time axis. The data acquisition module is responsible for collecting and transmitting this data to the data transmission module for subsequent remote monitoring and control optimization.

[0037] Specifically, the wind speed data collected by the data acquisition module through the wind speed sensor can be used to calculate the load of the fan using the following formula: ; in: is the wind energy (power) available to the wind turbine; is the air density; is the fan swept area; is the wind speed; is the power factor, which depends on the fan speed ratio and pitch angle .

[0038] The data acquisition module can track wind speed changes in real time, and provide data support for subsequent control modules through real-time feedback of wind speed, rotation speed, pitch angle and other data, thereby optimizing the operating efficiency of the wind turbine and the adaptability to the grid load.

[0039] A data transmission module, used for receiving the status information sent by the data acquisition module, and transmitting the status information to the remote monitoring module in the remote monitoring system through the remote communication network; The main function of the data transmission module is to receive the status information sent by the data acquisition module and transmit this information to the remote monitoring module in the remote monitoring system through the remote communication network. The data transmission module ensures that the operation data of the wind turbine can be transmitted to the remote monitoring system in a timely, efficient and reliable manner for subsequent data processing, analysis and decision optimization.

[0040] In this embodiment, the data transmission module transmits the status information of the wind turbine from the on-site wind turbine to the remote monitoring system by using a wireless communication unit and a remote communication network interface. The data involved in this process include key parameters such as wind speed, wind turbine speed, pitch angle, power generation, and power demand of the power grid. The transmission process must ensure the integrity and real-time nature of the data so that the remote monitoring system can perform necessary scheduling and control based on the current status information.

[0041] In this embodiment, the data transmission module includes a wireless communication unit, which is responsible for transmitting the collected wind turbine status information to the remote monitoring system by wireless means. The wireless communication unit usually adopts wireless communication technologies such as Wi-Fi, ZigBee or low power wide area network (LPWAN), which has the advantages of long transmission distance, low power consumption and easy installation.

[0042] In general, the choice of wireless communication unit depends on the specific site conditions, such as the geographical location of the wind turbine, the transmission range, the network load, and the required real-time performance. For example, in a relatively open and barrier-free environment, Wi-Fi or ZigBee technology can provide a relatively stable communication link; while in a remote and complex environment, LPWAN technology can effectively provide a long-distance, low-power data transmission solution.

[0043] As an option, if the wind turbine is located in an area that is difficult to cover, or there are strong interference signals on site, you can consider using an enhanced wireless communication module to ensure communication stability and accurate data transmission.

[0044] In addition to the wireless communication unit, the data transmission module also includes a remote communication network interface, which uses technologies such as 5G or satellite communication to achieve long-distance data transmission. The 5G network interface can provide high bandwidth, low latency and large-scale connection, which is suitable for real-time data transmission of wind turbines in large areas or complex environments.

[0045] In one possible implementation, the remote communication network interface can integrate the dual functions of 5G communication and satellite communication technology. In this way, even in wind farms far away from cities or remote areas, data can be transmitted via satellite links, ensuring that the system can operate stably in any geographical environment.

[0046] Specifically, the 5G network interface can provide millisecond-level low latency, ensuring that the status information of the wind turbine can be quickly transmitted to the remote monitoring module. Satellite communication provides a backup communication method in remote areas where 5G network signals cannot cover, ensuring that no matter where the wind turbine is located, it can maintain a continuous connection with the remote monitoring system.

[0047] In order to ensure the security and integrity of data during transmission, the data transmission module in this embodiment also includes data encryption and identity authentication mechanisms. These mechanisms can ensure that only authorized systems can receive the status information of the wind turbine, avoiding the risk of data leakage or tampering.

[0048] Data encryption uses high-standard encryption algorithms, such as AES-256, to ensure that data cannot be eavesdropped or tampered with during transmission. Authentication is achieved through key pairs to ensure that only authorized recipients can decrypt and read data during data transmission.

[0049] The design of the data transmission module pays special attention to real-time and reliability. In general, the data in the transmission process must be able to reach the remote monitoring system in a very short time so that it can be processed and responded to in time. The data transmission module uses distributed data stream transmission technology to ensure that multiple data channels work simultaneously to avoid data loss or delay due to network congestion or signal interference.

[0050] In one possible implementation, the data transmission module uses a message queue and data cache mechanism to ensure that in the case of network fluctuations, data can be temporarily stored and automatically sent after the network is restored. This mechanism can effectively prevent data loss and improve the robustness of the system.

[0051] In order to ensure the interoperability and compatibility of the system, in this embodiment, the data transmission module adopts a standardized transmission protocol, such as MQTT (Message Queuing Telemetry Transport) or HTTP / HTTPS protocol. These protocols are widely used in data transmission of IoT devices and are lightweight, efficient and scalable.

[0052] Specifically, the MQTT protocol can achieve rapid message transmission through publish / subscribe mode, which is suitable for data transmission of large-scale devices. The HTTP / HTTPS protocol can provide higher data transmission security and is suitable for application scenarios that require a higher level of data protection. The data transmission module selects the appropriate protocol according to different scenarios to ensure the stability and security of data transmission.

[0053] In the process of wind turbine status information transmission, especially in low bandwidth or high latency environment, transmission efficiency becomes the key to system operation. Therefore, the data transmission module adopts data compression technology to reduce the amount of transmitted data and improve transmission efficiency.

[0054] For example, for real-time data such as wind speed and fan speed, compression algorithms such as LZ77 or LZW can significantly reduce the size of the data packet, thereby reducing bandwidth usage. After compression, the data can still maintain a high degree of accuracy, ensuring that the remote monitoring module can accurately analyze and process the data.

[0055] The remote monitoring module is used to receive the wind turbine status information transmitted by the data transmission module, monitor the operating status of the wind turbine, and optimize the operating efficiency of the wind turbine by adjusting the weight parameters of the control strategy according to environmental changes and the actual working conditions of the wind turbine; The remote monitoring module plays a key role. It is responsible for receiving the status information of the wind turbine from the data transmission module and monitoring the operating status of the wind turbine in real time. By analyzing the real-time data, the remote monitoring module can judge the working condition of the wind turbine and optimize the operating efficiency of the wind turbine by adjusting the weight parameters in the control strategy according to the environmental changes and the actual working conditions of the wind turbine. The core goal of this process is to achieve intelligent scheduling and efficient operation of wind turbines in complex environments.

[0056] In this embodiment, the remote monitoring module provides a solution for adjusting the control strategy based on environmental feedback by collecting, analyzing and processing the status data of the fan. The realization of its function depends on the close cooperation of the data acquisition module, the data transmission module and the control system, so as to ensure that the system can respond quickly and optimize the operation of the fan when facing different environmental changes.

[0057] In this embodiment, the remote monitoring module includes a status monitoring unit, which is responsible for receiving and processing the wind turbine status information transmitted from the data transmission module in real time. Through this unit, the system can grasp the operating status of the wind turbine in real time, including key parameters such as wind speed, wind turbine speed, pitch angle, power generation, and power demand of the power grid.

[0058] In general, the status monitoring unit will display the working status of the fan in a graphical way, so that the operator can intuitively understand the current working condition of the fan. The core of status monitoring lies in the comprehensive integration and dynamic display of various data to ensure that the operator can find abnormalities in the first place and take corresponding treatment measures.

[0059] Specifically, the status monitoring unit will first analyze the data collected by each sensor and display it on the interface of the monitoring platform. Through real-time monitoring of the data, operators can adjust strategies in time according to the actual operating conditions of the wind turbine to avoid equipment failures caused by overload or unstable working conditions.

[0060] Another key component in the remote monitoring module is the control strategy adjustment unit. The main task of this unit is to dynamically adjust the weight parameters in the control strategy according to real-time monitoring data and changes in the external environment, thereby optimizing the operating efficiency of the wind turbine.

[0061] The control strategy is adjusted based on multiple factors, including real-time wind speed changes, the operating status of the wind turbine, the need to adjust the pitch angle, etc. In some embodiments, the control strategy adjustment unit combines these factors, calculates new parameters, and adjusts the working strategy of the wind turbine to ensure that the wind turbine can maintain the best power output and the lowest equipment loss under different environmental conditions.

[0062] For example, when the ambient wind speed changes dramatically, the control strategy adjustment unit will recalculate the optimal value of the pitch angle based on the real-time data of the wind speed. The formula is as follows: ; in: is the optimal pitch angle; is the real-time wind speed; is the real-time power generation of the wind turbine; is the speed ratio of the fan; is the power factor, which depends on the fan speed ratio and pitch angle , It is a mapping relationship of fan performance.

[0063] The control strategy adjustment unit will adjust the wind turbine's working strategy in a timely manner according to the changes in these parameters to ensure that the system can operate stably under any wind speed changes and maximize wind energy capture under different environmental conditions.

[0064] The control strategy adjustment unit in the remote monitoring module also includes a feedback adjustment mechanism, which dynamically adjusts the working parameters of the fan through real-time feedback data. This adjustment process can ensure that the fan always maintains the best working state in complex and uncertain environments.

[0065] In some embodiments, the feedback regulation mechanism not only relies on real-time data of the environment, but also makes model predictions based on historical operating data. For example, by analyzing the relationship between past wind speed and wind turbine power output, the system can predict the optimal operation mode of the wind turbine under certain wind speed conditions and adjust the control strategy accordingly.

[0066] Specifically, the feedback regulation mechanism can use data such as wind turbine speed, wind speed and power demand to dynamically adjust the weight parameters in the control strategy. For example, when the wind speed is low, the system may strengthen the control of wind turbine speed; while when the wind speed is high, it may focus on adjusting the pitch angle to reduce blade load and prevent excessive power generation.

[0067] An important goal of the remote monitoring module is to optimize the operating efficiency of the wind turbine, especially under complex environmental conditions. To this end, the remote monitoring module uses advanced algorithms to optimize the adjustment process of the control strategy. These algorithms include deep learning, predictive control and other methods, which can perform adaptive optimization based on real-time data and environmental conditions.

[0068] In this embodiment, the control strategy adjustment unit not only adjusts the operating state of the wind turbine through external environmental data (such as wind speed, temperature, etc.), but also dynamically adjusts the weight parameters of the control strategy according to the working conditions of the wind turbine itself (such as power generation, speed, etc.). Specifically, these parameters include but are not limited to pitch angle, wind turbine speed and power demand.

[0069] In general, an increase in wind speed usually requires the wind turbine to adjust the pitch angle to prevent excessive load on the blades, while low wind speeds may require adjusting the pitch angle to increase power output. By acquiring this data in real time, the remote monitoring module can adjust the weight of each control parameter to keep the wind turbine running in the optimal state.

[0070] The dynamic modeling module establishes the dynamic model of the wind turbine based on the status information of the wind turbine and the adjustment parameters fed back by the remote monitoring module. The dynamic model is used to characterize the working status and system performance of the wind turbine. The core task of the dynamic modeling module is to establish the dynamic model of the wind turbine based on the status information of the wind turbine and the adjustment parameters fed back by the remote monitoring module. This model is used to characterize the working status and system performance of the wind turbine, and provide theoretical basis and data support for subsequent control strategy optimization and equipment management. The dynamic modeling module enables the wind turbine to achieve optimal operating efficiency under various environmental conditions through precise modeling and dynamic adjustment.

[0071] In this embodiment, the dynamic modeling module establishes and continuously updates the dynamic model of the wind turbine by integrating the real-time status information of the wind turbine and the adjustment parameters transmitted by the remote monitoring module. This model can not only accurately reflect the dynamic characteristics of the wind turbine, but also predict and adjust the behavior of the wind turbine when the environment changes and the working conditions fluctuate. This modeling process provides crucial input data for the subsequent optimal control calculation module.

[0072] In this embodiment, the dynamic modeling module uses the real-time collected wind speed, wind turbine speed, pitch angle, power generation and grid power demand information to establish a mathematical model of the wind turbine. The model represents the various working parameters of the wind turbine through a series of nonlinear equations, and takes into account multiple factors such as wind speed changes, mechanical losses, and load regulation.

[0073] In general, the dynamic model of a wind turbine can characterize the relationship between the wind turbine speed, pitch angle and other variables over time, and describe the response of the wind turbine under different operating conditions. The core of this model is to accurately model the interaction between the various components of the wind turbine to ensure that the model can accurately reflect the dynamic behavior of the wind turbine during actual operation.

[0074] Specifically, the kinetic model can be expressed as a differential equation of the following form: ; in: is the speed of the fan; is the real-time wind speed; is the torque of the fan; is the pitch angle; is the real-time power generation of the wind turbine; are mechanical losses and system loss coefficients, is the fan speed ( ) is the rate of change over time, It is a mapping relationship of fan performance.

[0075] This equation reflects the impact of factors such as wind speed, pitch angle, and power generation on the wind turbine speed, and takes into account the impact of the wind turbine's mechanical losses on the dynamic system. By modeling the relationship between these variables, the performance of the wind turbine under different working conditions can be predicted and the control strategy can be adjusted accordingly.

[0076] In order to ensure that the dynamic model of the fan can maintain accuracy in a complex environment, in this embodiment, the dynamic modeling module also combines the adjustment parameters fed back by the remote monitoring module. These adjustment parameters reflect the various external environmental changes and internal working conditions encountered by the fan during actual operation. The feedback mechanism of the adjustment parameters can ensure that the dynamic model is continuously optimized in a real-time changing environment to maintain accurate prediction of the fan behavior.

[0077] In a possible implementation, the adjustment parameters of the remote monitoring module include wind speed change, ambient temperature, humidity, fan load, etc. The dynamic modeling module receives these adjustment parameters and dynamically adjusts the coefficients and parameters in the model to adapt to different operating conditions.

[0078] In some embodiments, the dynamic modeling module further includes a parameter adjustment unit, which adjusts the relevant parameters of the fan dynamic model in real time according to the fan status information fed back by the remote monitoring module. Specifically, these parameters include the torque coefficient, aerodynamic parameters, load coefficient, etc. of the fan.

[0079] Generally, as environmental conditions (such as wind speed and temperature) change, the working state of the fan will change, and some parameters in the dynamic model need to be adjusted accordingly. For example, when the wind speed increases, the torque of the fan will increase accordingly, and the system must adjust the torque coefficient in time to avoid excessive load on the fan.

[0080] Specifically, some coefficients in the model, such as aerodynamic effect coefficients and mechanical loss coefficients, can be dynamically adjusted based on historical data and real-time feedback. In this way, the dynamic modeling module can maintain high accuracy and adaptability of the model when factors such as wind speed, temperature and load change.

[0081] The dynamic model in this embodiment is not only used to predict the operating state of the fan, but also can characterize the overall performance of the fan system. By calculating various performance indicators of the fan, the dynamic model can help evaluate the operating efficiency, equipment fatigue status and energy efficiency loss of the fan.

[0082] In general, the dynamic model can be used to calculate the operating efficiency of the fan , the efficiency can be calculated by the following formula: ; in: The operating efficiency of the fan; is the real-time power generation of the wind turbine; The wind energy that can be utilized by the wind turbine.

[0083] The operating efficiency of the fan is an important indicator for judging the performance of the fan. Through real-time calculation of the dynamic modeling module, the system can dynamically evaluate the efficiency of the fan and adjust the control strategy to improve the power generation capacity and energy-saving effect of the fan when inefficient operation occurs.

[0084] The optimal control calculation module calculates the optimal variable pitch control strategy of the wind turbine according to the dynamic model and the control parameters provided by the remote monitoring module, and transmits the calculation results to the execution control module; The core function of the optimal control calculation module is to calculate the optimal variable pitch control strategy of the wind turbine based on the dynamic model and the control parameters provided by the remote monitoring module, and transmit the control strategy to the execution control module. This module calculates the optimal control strategy that can maximize the efficiency of the wind turbine by analyzing the working status of the wind turbine and combining it with real-time environmental change information. The calculation of this strategy takes into account the complex relationship between parameters such as wind speed, rotation speed, pitch angle, and power generation, and can be dynamically adjusted according to different working conditions to ensure that the wind turbine can achieve the best operating effect in various environments.

[0085] In this embodiment, the optimal control calculation module works closely with the dynamic modeling module and the remote monitoring module. The dynamic model provides predictions of the behavior of the wind turbine under various conditions, and the remote monitoring module provides real-time control parameters and environmental information. By integrating these data, the optimal control calculation module can calculate the optimal variable pitch control strategy and transmit the results to the execution control module to ensure that the wind turbine always maintains the best working state under changing environmental conditions.

[0086] The optimal control calculation module calculates the optimal variable pitch control strategy of the wind turbine by solving the control optimization problem. The control optimization problem is usually a dynamic optimization problem, the goal is to maximize the operating efficiency of the wind turbine or reduce the energy loss of the system, while taking into account the dynamic characteristics of the wind turbine and environmental conditions. Common optimization methods include linear or nonlinear programming in optimal control theory, dynamic programming, and model predictive control (MPC).

[0087] In general, the objective function of the optimal control calculation module can be expressed as: ; in: is the objective function, which represents the comprehensive benefits of the fan; is the power generation of the wind turbine; is the pitch angle of the wind turbine; and is the adjustment coefficient, which is used to balance the cost of power generation and pitch angle adjustment; To optimize the calculation time interval, Indicates a small increment of time (in seconds).

[0088] This objective function comprehensively considers the wind turbine's power generation and pitch angle changes. By optimizing this objective function, the optimal control calculation module can select the most suitable pitch angle under different wind speeds and environmental conditions, so that the wind turbine can achieve the optimal operating efficiency.

[0089] Specifically, the change in pitch angle is closely related to multiple factors such as wind speed, wind turbine speed, power generation and grid power demand. In general, when the wind speed is high, the pitch angle of the wind turbine needs to be adjusted appropriately to prevent excessive wind speed from causing excessive load on the blades. On the other hand, when the wind speed is low, the adjustment of the pitch angle can keep the wind turbine in the optimal area of ​​effective power output.

[0090] In some embodiments, the optimal control calculation module may use a model predictive control (MPC) method to solve the optimal pitch control strategy. The MPC method predicts the system behavior in the future and calculates and adjusts the control strategy in real time.

[0091] Specifically, the MPC method predicts future operations based on the dynamic model of the wind turbine, calculates the pitch angle in the future, and continuously updates the control strategy. The MPC optimization problem can be expressed as:

[0092] in: For the future moment The pitch angle of the blade; For the future moment The power generation capacity; is the target power generation; is the adjustment coefficient, which is used to balance the cost of power generation and pitch angle adjustment; is the number of time steps for prediction, Indicates that the target is the pitch angle Perform minimization optimization to find the pitch angle that minimizes the objective function.

[0093] Through this optimization method, the optimal control calculation module can dynamically adjust the pitch angle according to changes in environmental parameters such as wind speed and temperature, and enable the wind turbine to reach the target power generation within a period of time in the future. This method can respond to environmental changes in real time and adjust the control strategy based on the prediction results to ensure that the wind turbine always maintains efficient operation under changing environmental conditions.

[0094] In this embodiment, the ultimate task of the optimal control calculation module is to transmit the calculated optimal variable pitch control strategy to the execution control module. The execution control module adjusts the pitch angle of the wind turbine according to the optimal control strategy, so that the wind turbine can maximize the power generation and reduce the equipment burden under the current environmental conditions.

[0095] Generally, the optimal control calculation module transmits the optimal control strategy to the execution control module in real time through the communication protocol. After receiving the control strategy, the execution control module accurately adjusts the pitch angle based on the strategy to achieve dynamic control of the wind turbine. The transmission and execution process of the control strategy must have high real-time performance to ensure that the wind turbine can respond quickly to changes in wind speed or sudden environmental conditions.

[0096] An execution control module is used to receive the optimal variable pitch control strategy transmitted by the optimal control calculation module, and adjust the pitch angle of the wind turbine based on the control strategy; The core function of the execution control module is to receive the optimal variable pitch control strategy transmitted by the optimal control calculation module, and accurately adjust the pitch angle of the wind turbine according to the control strategy. This module achieves the optimal operating state of the wind turbine under different wind speeds and working conditions by accurately controlling the change of the pitch angle, so as to improve the power generation efficiency of the wind turbine and reduce equipment losses. The execution control module is closely connected with the optimal control calculation module to ensure that the calculated control strategy can be converted into actual operation in real time to achieve the ideal control effect.

[0097] In this embodiment, the operation process of the execution control module is adjusted based on the optimal variable pitch control strategy calculated by the aforementioned optimal control calculation module to ensure that the wind turbine maintains stable and efficient operation under real-time environmental conditions. The optimal control strategy received by the execution control module will be applied to the pitch angle adjustment of the wind turbine in a very short time, thereby optimizing the power generation performance of the wind turbine and avoiding mechanical loss and system instability caused by improper pitch angle.

[0098] Generally, after the execution control module receives the optimal variable pitch control strategy transmitted by the optimal control calculation module, it first calculates the pitch angle and converts the result into an executable control signal. These control signals will be transmitted to the pitch adjustment device of the wind turbine, thereby changing the angle of the wind turbine blades. The adjustment of the pitch angle is crucial to the operating efficiency of the wind turbine, which directly affects the performance indicators of the wind turbine, such as speed, load and power output.

[0099] Specifically, the executive control module continuously adjusts the pitch angle at certain time intervals according to the control strategy transmitted by the optimal control calculation module. The pitch angle adjustment is not only affected by the current wind speed, but also appropriately corrected according to multiple factors such as wind turbine speed, power generation and grid load. The executive control module will respond promptly to these changes to ensure that the wind turbine always operates within the optimal working range.

[0100] In some embodiments, the output signal of the execution control module can act on the fan pitch adjustment system through electrical or mechanical devices, and the system will drive the fan blades to change angles according to the instructions. In order to improve the control accuracy and response speed, the control signal of the execution control module usually has a high real-time performance to ensure that the fan pitch angle can be adjusted synchronously with the changes in the external environment.

[0101] As an option, the executive control module uses a feedback control-based algorithm to adjust the pitch angle. The control algorithm determines whether the pitch angle needs to be adjusted based on the current operating status of the wind turbine and real-time sensor data. The feedback control algorithm can adopt proportional-integral-derivative (PID) control, fuzzy control or adaptive control technology to optimize the performance of the wind turbine.

[0102] Specifically, in a possible implementation, the execution control module calculates the pitch angle adjustment amount based on the PID control algorithm. The output of the PID controller can be expressed as: ; in: is the adjustment amount of the pitch angle; is the error at the current moment, defined as the difference between the target pitch angle and the current pitch angle; , ,and are the proportional, integral and differential coefficients respectively; For time; is the integral variable, used to represent changes in time; Error About time The derivative of , which represents the rate of change of the error over time.

[0103] The control algorithm determines the adjustment amount of the pitch angle by calculating the current error and adjusting the error according to the proportional, integral and differential coefficients. The execution control module transmits the calculation results to the pitch adjustment system, which in turn changes the angle of the wind turbine blades. Through the PID control algorithm, the pitch angle of the wind turbine can be accurately adjusted to ensure the optimal operating state of the wind turbine under various working conditions.

[0104] In this embodiment, the execution control module not only makes adjustments based on the control strategy transmitted by the optimal control calculation module, but also optimizes the adjustment process according to the real-time feedback signal. Generally, after receiving the pitch angle adjustment instruction, the execution control module will monitor the status of the wind turbine in real time through sensor feedback, including wind speed, rotation speed, pitch angle and other information, and feed these data back to the optimal control calculation module.

[0105] As an option, the executive control module can adjust the coefficients in the PID controller and optimize the control strategy to adapt to changing environmental conditions and workloads by monitoring the changes in the pitch angle and wind turbine operating status in real time. Through this real-time feedback mechanism, the system can continuously adjust and optimize the pitch angle control to ensure that the wind turbine is always in the best working condition.

[0106] The remote control method of a wind turbine generator applicable to a complex environment described below and the remote control system of a wind turbine generator applicable to a complex environment described above may refer to each other.

[0107] Please refer to the attached Figure 8 The present invention also provides a remote control method for a wind turbine generator suitable for a complex environment, comprising the following steps: S1. Collect status information of wind turbines, including wind speed, wind turbine speed, pitch angle, generated power and grid power demand; S2. establishing a dynamic model of the wind turbine based on the state information of the wind turbine; S3. Based on the dynamic model of the wind turbine, the optimal variable pitch angle control strategy is solved by using the HJB equation; S4. Numerically solve the HJB equation by deep approximate dynamic programming method, and use deep neural network to approximate the optimal pitch angle control strategy; S5, transmitting the optimal variable pitch angle control strategy to the execution control module to adjust the pitch angle of the wind turbine; S6. According to the real-time status of the wind turbine and environmental changes, the weight parameters of the optimal control strategy are dynamically adjusted through the remote control system.

[0108] The method of this embodiment can be used to execute the above system embodiment, and its principles and technical effects are similar, which will not be repeated here.

[0109] 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 remote control system for wind turbines suitable for complex environments, characterized in that: include: A data acquisition module is used to collect status information of the wind turbine, wherein the status information includes wind speed, wind turbine speed, pitch angle, generated power and grid power demand; A data transmission module, used for receiving the status information sent by the data acquisition module, and transmitting the status information to the remote monitoring module in the remote monitoring system through a remote communication network; A remote monitoring module, used to receive the wind turbine status information transmitted by the data transmission module, monitor the operating status of the wind turbine, and optimize the operating efficiency of the wind turbine by adjusting the weight parameters of the control strategy according to environmental changes and the actual working conditions of the wind turbine; A dynamic modeling module, which establishes a dynamic model of the wind turbine based on the state information of the wind turbine and the adjustment parameters fed back by the remote monitoring module, wherein the dynamic model is used to characterize the working state and system performance of the wind turbine; An optimal control calculation module calculates the optimal variable pitch control strategy of the wind turbine according to the dynamic model and the control parameters provided by the remote monitoring module, and transmits the calculation result to the execution control module; The execution control module is used to receive the optimal variable pitch control strategy transmitted by the optimal control calculation module, and adjust the pitch angle of the wind turbine based on the control strategy.

2. The remote control system for wind turbines suitable for complex environments according to claim 1, characterized in that: The data acquisition module comprises: Wind speed sensor, used to collect wind speed data in real time; Fan speed sensor, used to collect fan speed data; A pitch angle sensor is used to collect pitch angle data; Power generation sensor, used to collect power generation data of the wind turbine; The power grid power demand sensor is used to collect the power grid's power demand data for wind turbines.

3. The remote control system for wind turbines suitable for complex environments according to claim 1, characterized in that: The data transmission module comprises: A wireless communication unit, used to transmit the collected wind turbine status information to a remote monitoring system by wireless means; A remote communication network interface is used to transmit the status information to a remote monitoring module via 5G and satellite communications.

4. The remote control system for wind turbines suitable for complex environments according to claim 1, characterized in that: The remote monitoring module comprises: A status monitoring unit is used to receive status information of wind turbines in real time and generate a visual interface of wind turbine operation data; The control strategy adjustment unit is used to adjust the weight parameters in the control strategy according to real-time monitoring data and changes in the external environment.

5. The remote control system for wind turbines suitable for complex environments according to claim 1, characterized in that: The kinetic modeling module includes: A wind turbine dynamics model generation unit generates a mathematical model describing the dynamic performance and control characteristics of the wind turbine based on the wind turbine status information; The parameter adjustment unit is used to adjust the model parameters according to the feedback information provided by the remote monitoring module to adapt to the changes in the environment and the operating status of the fan.

6. The remote control system for wind turbines suitable for complex environments according to claim 1, characterized in that: The optimal control calculation module includes: An optimal control algorithm unit, which uses an optimization algorithm to calculate an optimal pitch angle control strategy according to a wind turbine dynamics model, the wind turbine status information and adjustment parameters provided by a remote monitoring module; The deep learning unit is used to approximate the optimal control strategy using a deep approximate dynamic programming method and adjust the pitch angle control strategy in real time.

7. The remote control system for wind turbines suitable for complex environments according to claim 1, characterized in that: The execution control module comprises: A pitch angle adjustment unit, used to receive the optimal pitch angle control strategy transmitted by the optimal control calculation module, and adjust the pitch angle of the wind turbine according to the strategy; The real-time execution unit is used to execute the received optimal pitch angle control strategy locally in real time and report the execution result to the remote monitoring module.

8. The remote control system for wind turbines suitable for complex environments according to claim 5, characterized in that: The parameter adjustment unit: A feedback receiving unit receives the wind turbine status information feedback from the remote monitoring module, and adjusts the relevant parameters of the wind turbine dynamics model according to the changes in the real-time parameters of wind speed, wind turbine speed and pitch angle; A model adjustment unit dynamically adjusts the parameters of the wind turbine dynamics model according to the received real-time feedback information; The adaptive adjustment unit adjusts the control strategy parameters in the dynamic model according to real-time monitoring data and feedback information, and optimizes the response capability and efficiency of the fan in various complex environments.

9. The remote control system for wind turbines suitable for complex environments according to claim 6, characterized in that: Optimal control algorithm unit: The optimization calculation unit uses a mathematical optimization algorithm to calculate the optimal pitch angle control strategy based on the dynamic model of the wind turbine, the state information and the adjustment parameters provided by the remote monitoring module; A deep learning module is used to apply a deep approximate dynamic programming method, combining historical data and environmental changes to approximate the optimal pitch angle control strategy through a deep neural network; The strategy adjustment unit adjusts the parameters of the optimal control algorithm according to the real-time collected wind turbine status and external environment information.

10. A remote control method for a wind turbine generator suitable for a complex environment, according to any one of claims 1 to 9, characterized in that: The following steps are involved: Collecting status information of wind turbines, including wind speed, wind turbine speed, pitch angle, generated power and grid power demand; Based on the state information of the wind turbine, a dynamic model of the wind turbine is established; Based on the dynamic model of the wind turbine, an optimal variable pitch angle control strategy is solved, wherein the solution adopts the HJB equation; The HJB equation is numerically solved by deep approximate dynamic programming method, and the optimal pitch angle control strategy is approximated by deep neural network. The optimal variable pitch angle control strategy is transmitted to the execution control module to adjust the pitch angle of the wind turbine; According to the real-time status of the wind turbine and environmental changes, the weight parameters of the optimal control strategy are dynamically adjusted through the remote control system.

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