Fan operation real-time dynamic regulation control method and system based on environmental data

Abstract

The invention discloses a fan operation real-time dynamic adjustment control method and system based on environmental data. The method comprises the steps that multi-dimensional dynamic parameters of the environment where a fan is located are collected in real time based on a distributed sensor network; constructing a dynamic coupling prediction model, inputting the dynamic coupling prediction model into a bidirectional verification module based on multi-dimensional dynamic parameters, and obtaining an environment state prediction matrix in a future time window; based on the environment state prediction matrix, a combined adjustment instruction set of the fan pitch angle, the yaw angle and the generator torque is obtained, and the instruction set comprises main strategy parameters and an emergency tolerance interval; based on an incremental learning mechanism, model parameters are dynamically updated according to the deviation between actual environment parameters and predicted values, and the updating period is adaptively adjusted along with the change of turbulence intensity. The method has the advantages that accurate prediction and optimal control of the fan in a complex environment are achieved, the limitation that a traditional data driving model lacks physical interpretability is broken through, and the running stability and efficiency of the fan are improved.

Description

Technical Field

[0001] The present invention relates to a dynamic regulation technology, and in particular to a real-time dynamic regulation control method and system for fan operation based on environmental data. Background Art

[0002] With the rapid development of renewable energy, wind power generation has been widely used as a clean energy source around the world. In wind power generation systems, the operating efficiency and stability of wind turbines are often affected by environmental factors, such as changes in meteorological conditions such as wind speed, wind direction, temperature, and air pressure. Therefore, how to dynamically adjust wind turbines based on real-time environmental data to improve the operating efficiency of wind turbines, extend equipment life, and ensure the stability and safety of power generation has become an important research direction.

[0003] The current real-time dynamic adjustment and control methods for fan operation on the market mainly focus on real-time monitoring and adjustment of the fan's operating status through intelligent control systems to achieve efficient and stable operation. Common adjustment and control methods include sensor-based data acquisition, real-time monitoring of the fan's operating conditions through key parameters such as temperature, pressure, vibration, and flow, so as to accurately adjust the fan's speed, load, air volume, etc. However, focusing only on the fan itself still has limitations. It lacks dynamic response to real-time environmental data and cannot adapt to environmental changes such as wind speed, temperature, and humidity in real time. Under complex environmental conditions, traditional methods may lead to low fan efficiency, energy waste, or premature equipment loss. Summary of the invention

[0004] In order to improve the existing real-time dynamic adjustment control method and system for fan operation, a real-time dynamic adjustment control method and system for fan operation based on environmental data is provided. This method combines a distributed sensor network with a dynamic coupling prediction model to achieve accurate prediction and optimization control of fans in complex environments. The model parameters are dynamically adjusted through an incremental learning mechanism to improve the stability and efficiency of fan operation, especially for changing environmental conditions.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] The real-time dynamic adjustment and control method of fan operation based on environmental data includes:

[0007] Based on the distributed sensor network, multi-dimensional dynamic parameters of the fan environment are collected in real time;

[0008] Build a dynamic coupling prediction model, input it into the two-way verification module based on multi-dimensional dynamic parameters, and obtain the environmental state prediction matrix in the future time window;

[0009] Based on the environmental state prediction matrix, a joint adjustment instruction set of the wind turbine pitch angle, yaw angle and generator torque is obtained, wherein the instruction set includes main strategy parameters and emergency tolerance intervals;

[0010] Based on the incremental learning mechanism, the model parameters are dynamically updated according to the deviation between the actual environmental parameters and the predicted values, and the update period is adaptively adjusted as the turbulence intensity changes.

[0011] Preferably, the real-time collection of multi-dimensional dynamic parameters of the environment in which the wind turbine is located based on the distributed sensor network specifically includes:

[0012] The multi-dimensional dynamic parameters specifically include wind speed gradient distribution, turbulence intensity, temperature vertical gradient, humidity change rate and air pressure fluctuation data;

[0013] The wind speed gradient distribution is obtained by synchronous scanning of three groups of lidars with a vertical height difference exceeding 20% ​​of the hub diameter. The second group of radars captures turbulent vortex characteristics at an elevation angle of 45 degrees. The sampling frequency is dynamically adjusted with the instantaneous wind speed standard deviation. When the standard deviation exceeds 1.5m / s, the high-frequency scanning mode is started.

[0014] Preferably, the construction of the dynamic coupling prediction model, inputting the multi-dimensional dynamic parameters into the bidirectional verification module, and obtaining the environmental state prediction matrix in the future time window specifically includes:

[0015] Based on the acquired multi-dimensional dynamic parameters, a dynamic coupling prediction model is constructed through a neural network, and the multi-dimensional dynamic parameters are input into a bidirectional verification module;

[0016] The bidirectional verification module specifically includes an LSTM neural network and a physical constraint equation;

[0017] The physical constraint equation includes the blade aeroelastic coupling equation and the tower resonance frequency constraint condition, and the formula is:

[0018]

[0019] in, is the aerodynamic load, is the pitch angle, is the structural damping coefficient, is the lateral displacement of the tower, is the time-varying material fatigue threshold, and this constraint is embedded in the LSTM loss function through the Lagrange multiplier method;

[0020] Future time window given by dynamic coupling prediction model Environmental status prediction data within The actual environmental status data , to obtain the accurate environmental state prediction matrix in the future time window, the formula is:

[0021]

[0022] Among them, C is the calibration coefficient, which is used to balance the impact of predicted values ​​and actual data.

[0023] Preferably, the joint adjustment instruction set of wind turbine pitch angle, yaw angle and generator torque is obtained based on the environmental state prediction matrix, and the instruction set includes main strategy parameters and emergency tolerance intervals, specifically including:

[0024] Get the environment state prediction matrix The wind turbine pitch angle , yaw angle , generator torque data;

[0025] The wind turbine pitch angle Control the angle of the wind turbine blades to maximize the capture of wind energy or reduce excessive loads. The regulation function is:

[0026]

[0027] The yaw angle Control the direction of the fan relative to the wind so that the fan is aligned with the wind direction as much as possible. The adjustment formula is:

[0028]

[0029] The generator torque It mainly controls the power output of the fan, and its adjustment formula is:

[0030]

[0031] in, For future time window Wind speed data with inner dimension w, For future time window Temperature data within For future time window Internal turbulence intensity data, For future time window Wind direction data with inner dimension w;

[0032] Generate control instructions based on each adjustment function, combine the main strategy parameters, and build a joint adjustment instruction set ,

[0033]

[0034] in, , , is the weight coefficient, which is adjusted based on actual conditions;

[0035] Based on the wind turbine pitch angle , yaw angle , generator torque Set the emergency tolerance interval based on the upper and lower limits of the system.

[0036] Preferably, the wind turbine pitch angle , yaw angle , generator torque The upper and lower limits of the emergency tolerance interval include:

[0037] Generated by Monte Carlo simulation, simulated environmental parameters are at predicted values Extreme working conditions within the range;

[0038] When the real-time monitoring detects that the strain rate at the root of the blade exceeds 0.15με / s, it automatically switches to the load reduction priority mode and triggers the emergency pre-compression action of the hydraulic pitch system.

[0039] Preferably, the incremental learning mechanism is based on which the model parameters are dynamically updated according to the deviation between the actual environmental parameters and the predicted values, and the update period is adaptively adjusted as the turbulence intensity changes, specifically including:

[0040] Get real-time environmental parameter values (t), predicted value (t), calculate its error value:

[0041]

[0042] The model parameters are adjusted based on the gradient descent method. To update, the formula is:

[0043]

[0044] in, is the learning rate, is the gradient of the error;

[0045] Based on the value of turbulence intensity, obtain its impact on the update cycle and adjust the next update cycle. The formula is:

[0046]

[0047] in, To adjust the coefficient, control the impact of turbulence intensity on the update period.

[0048] Furthermore, a real-time dynamic adjustment control system for wind turbine operation based on environmental data is proposed, including:

[0049] Distributed sensors: Real-time distributed sensors are mainly used to obtain multi-dimensional dynamic parameters, including wind speed sensors, temperature sensors, humidity sensors, air pressure sensors, lidar, etc.

[0050] Prediction model module: The prediction model module is mainly used to obtain the environmental state prediction matrix within the future time window through the two-way verification module;

[0051] Bidirectional verification module: The bidirectional verification module is mainly used for the operation and processing of LSTM neural network and physical constraint equations;

[0052] Adjustment function module: The adjustment function module is mainly used to adjust the pitch angle of the wind turbine under different environmental conditions. , yaw angle , generator torque Adjustment of data;

[0053] Joint regulation instruction set module: The joint regulation instruction set is mainly used to coordinate and optimize multiple control variables to improve the performance and stability of the system, and to set an emergency tolerance interval to ensure the operation of the wind turbine;

[0054] Parameter optimization module: The parameter optimization module is mainly used to dynamically update model parameters according to the deviation between actual environmental parameters and predicted values;

[0055] Processor: The processor is mainly used for the calculation process of each formula and the construction calculation process of each model.

[0056] Compared with the prior art, the advantages of the present invention are:

[0057] Based on the real-time collection of environmental data from a distributed sensor network and the prediction of wind turbine status through a dynamic coupling prediction model, the real-time impact of environmental changes on wind turbine operation can be effectively captured, and a more accurate control strategy can be achieved. The future environmental state is predicted through a two-way verification module, and the wind turbine pitch angle, yaw angle and generator torque are adjusted based on the prediction matrix, which can optimize the operating efficiency of the wind turbine under different environmental conditions while ensuring its safety. The incremental learning mechanism dynamically adjusts the model parameters according to the deviation between the actual environmental parameters and the predicted values, so that the control system can adapt to environmental changes, especially when dealing with sudden changes such as turbulence, it is more flexible and accurate. This adjustment and control method based on real-time dynamic data greatly improves the adaptability and operating stability of the wind turbine, reduces the loss caused by environmental fluctuations, improves the efficiency of wind energy utilization, and reduces the risk of failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a schematic diagram of the real-time dynamic adjustment control of the fan operation based on environmental data proposed by the present invention;

[0059] Figure 2 This is a schematic diagram of obtaining multi-dimensional dynamic parameters proposed by the present invention;

[0060] Figure 3 This is a schematic diagram of the environmental state prediction matrix proposed by the present invention;

[0061] Figure 4 A schematic diagram of the joint adjustment instruction set proposed by the present invention;

[0062] Figure 5 This is a schematic diagram of the emergency tolerance interval proposed by the present invention;

[0063] Figure 6 This is a schematic diagram of the dynamic update model parameters proposed by the present invention;

[0064] Figure 7 This is a schematic diagram of the electronic device in this solution;

[0065] Figure 8 This is a schematic diagram of the computer-readable storage medium structure in this solution. DETAILED DESCRIPTION

[0066] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0067] The real-time dynamic adjustment and control system of the fan operation based on environmental data includes:

[0068] Distributed sensors: Real-time distributed sensors are mainly used to obtain multi-dimensional dynamic parameters, including wind speed sensors, temperature sensors, humidity sensors, air pressure sensors, lidar, etc.

[0069] Prediction model module: The prediction model module is mainly used to obtain the environmental state prediction matrix within the future time window through the two-way verification module;

[0070] Bidirectional verification module: The bidirectional verification module is mainly used for the operation and processing of LSTM neural network and physical constraint equations;

[0071] Adjustment function module: The adjustment function module is mainly used to adjust the pitch angle of the wind turbine under different environmental conditions. , yaw angle , generator torque Adjustment of data;

[0072] Joint regulation instruction set module: The joint regulation instruction set is mainly used to coordinate and optimize multiple control variables to improve the performance and stability of the system, and to set an emergency tolerance interval to ensure the operation of the wind turbine;

[0073] Parameter optimization module: The parameter optimization module is mainly used to dynamically update model parameters according to the deviation between actual environmental parameters and predicted values;

[0074] Processor: The processor is mainly used for the calculation process of each formula and the construction calculation process of each model.

[0075] See also Figure 1 As shown, the real-time dynamic adjustment and control method of the fan operation based on environmental data includes:

[0076] Step 1: Collect multi-dimensional dynamic parameters of the fan environment in real time based on a distributed sensor network;

[0077] Step 2: Build a dynamic coupling prediction model, input it into the bidirectional verification module based on multi-dimensional dynamic parameters, and obtain the environmental state prediction matrix in the future time window;

[0078] Step 3: Based on the environmental state prediction matrix, a joint adjustment instruction set of the wind turbine pitch angle, yaw angle and generator torque is obtained, wherein the instruction set includes main strategy parameters and emergency tolerance intervals;

[0079] Step 4: Based on the incremental learning mechanism, the model parameters are dynamically updated according to the deviation between the actual environmental parameters and the predicted values, and the update period is adaptively adjusted as the turbulence intensity changes.

[0080] See also Figure 2 As shown in the figure, the multi-dimensional dynamic parameters of the environment in which the fan is located are collected in real time based on the distributed sensor network, including:

[0081] The multi-dimensional dynamic parameters specifically include wind speed gradient distribution, turbulence intensity, temperature vertical gradient, humidity change rate and air pressure fluctuation data;

[0082] The wind speed gradient distribution is obtained by synchronous scanning of three groups of lidars with a vertical height difference exceeding 20% ​​of the hub diameter. The second group of radars captures turbulent vortex characteristics at an elevation angle of 45 degrees. The sampling frequency is dynamically adjusted with the instantaneous wind speed standard deviation. When the standard deviation exceeds 1.5m / s, the high-frequency scanning mode is started.

[0083] See also Figure 3 As shown, a dynamic coupling prediction model is constructed, and it is input into a two-way verification module based on multi-dimensional dynamic parameters to obtain the environmental state prediction matrix in the future time window, which specifically includes:

[0084] Based on the acquired multi-dimensional dynamic parameters, a dynamic coupling prediction model is constructed through a neural network, and the multi-dimensional dynamic parameters are input into a bidirectional verification module;

[0085] The bidirectional verification module specifically includes an LSTM neural network and a physical constraint equation;

[0086] The physical constraint equation includes the blade aeroelastic coupling equation and the tower resonance frequency constraint condition, and the formula is:

[0087]

[0088] in, is the aerodynamic load, is the pitch angle, is the structural damping coefficient, is the lateral displacement of the tower, is the time-varying material fatigue threshold, and this constraint is embedded in the LSTM loss function through the Lagrange multiplier method;

[0089] Future time window given by dynamic coupling prediction model Environmental status prediction data within The actual environmental status data , to obtain the accurate environmental state prediction matrix in the future time window, the formula is:

[0090]

[0091] Among them, C is the calibration coefficient, which is used to balance the impact of predicted values ​​and actual data.

[0092] Specifically, the physical constraint equation converts the dynamic response characteristics of the flexible body described by the blade aeroelastic coupling equation and the natural vibration modal law expressed by the tower resonance frequency constraint into a regularization term in the form of a differential equation and embeds it into the training process. This two-way verification mechanism compares the predicted data with the actual monitoring data in real time, and uses error back propagation to simultaneously optimize the network weights and physical parameters, which not only ensures that the prediction results conform to the basic principles of fluid mechanics and structural dynamics, but also can adaptively correct model deviations. The final output environmental state prediction matrix not only contains meteorological elements such as wind speed, wind direction, and turbulence in the future time window, but also integrates key parameters such as the blade dynamic load spectrum and the tower vibration spectrum to form a multi-dimensional spatiotemporal-correlated feature tensor, providing high-precision prediction support for the active pitch control and resonance avoidance strategy of wind turbines.

[0093] See also Figure 4 As shown, based on the environmental state prediction matrix, a joint adjustment instruction set of wind turbine pitch angle, yaw angle and generator torque is obtained, and the instruction set includes main strategy parameters and emergency tolerance intervals, specifically including:

[0094] Get the environment state prediction matrix The wind turbine pitch angle , yaw angle , generator torque data;

[0095] The wind turbine pitch angle Control the angle of the wind turbine blades to maximize the capture of wind energy or reduce excessive loads. The regulation function is:

[0096]

[0097] The yaw angle Control the direction of the fan relative to the wind so that the fan is aligned with the wind direction as much as possible. The adjustment formula is:

[0098]

[0099] The generator torque It mainly controls the power output of the fan, and its adjustment formula is:

[0100]

[0101] in, For future time window Wind speed data with inner dimension w, For future time window Temperature data within For future time window Internal turbulence intensity data, For future time window Wind direction data with inner dimension w;

[0102] Generate control instructions based on each adjustment function, combine the main strategy parameters, and build a joint adjustment instruction set ,

[0103]

[0104] in, , , is the weight coefficient, which is adjusted based on actual conditions;

[0105] Based on the wind turbine pitch angle , yaw angle , generator torque Set the emergency tolerance interval based on the upper and lower limits of the system.

[0106] It is understandable that when constructing a joint regulation instruction set, the main strategy parameters may be inconsistent with the requirements of each regulation function, resulting in conflicts or inability to coordinate between control instruction sets. For example, the regulation of generator torque may conflict with the regulation of wind turbine pitch angle, thus affecting the effect of the overall control strategy. Therefore, when constructing a joint regulation instruction set, an optimization algorithm should be used for parameter matching to ensure the coordination between the various regulation functions. At the same time, constraints can be introduced to ensure that the various control instructions do not interfere with each other and achieve the best overall regulation effect.

[0107] See also Figure 5 As shown, based on the wind turbine pitch angle , yaw angle , generator torque The upper and lower limits of the emergency tolerance interval include:

[0108] Generated by Monte Carlo simulation, simulated environmental parameters are at predicted values Extreme working conditions within the range;

[0109] When the real-time monitoring detects that the strain rate at the root of the blade exceeds 0.15με / s, it automatically switches to the load reduction priority mode and triggers the emergency pre-compression action of the hydraulic pitch system.

[0110] Specifically, a certain range of variables is set in the Monte Carlo simulation generation, such as the upper and lower limits of wind speed, and the fluctuation range of other environmental parameters. Run the simulation model and generate different environmental conditions through random sampling to simulate a variety of possible extreme working conditions. Based on the simulation results, the performance of the wind turbine under these extreme conditions is evaluated to identify potential risk factors. When the system monitors in real time that the strain rate at the root of the blade exceeds 0.15με / s, it indicates that the wind turbine may be in an overloaded or unstable operating state. At this time, the system will automatically switch to the load reduction priority mode to reduce the load on the wind turbine, reduce the pressure on mechanical components, and prevent equipment damage. Immediately afterwards, the hydraulic variable pitch system will start and perform emergency pre-stressing operations to adjust the windward angle of the blades and reduce the pressure of wind on the blades, thereby reducing the stress on the blades and maintaining stable operation of the wind turbine.

[0111] See also Figure 6 As shown in the figure, based on the incremental learning mechanism, the model parameters are dynamically updated according to the deviation between the actual environmental parameters and the predicted values, and the update cycle is adaptively adjusted as the turbulence intensity changes. Specifically, it includes:

[0112] Get real-time environmental parameter values (t), predicted value (t), calculate its error value:

[0113]

[0114] The model parameters are adjusted based on the gradient descent method. To update, the formula is:

[0115]

[0116] in, is the learning rate, is the gradient of the error;

[0117] Based on the value of turbulence intensity, obtain its impact on the update cycle and adjust the next update cycle. The formula is:

[0118]

[0119] in, To adjust the coefficient, control the impact of turbulence intensity on the update period.

[0120] Furthermore, the method according to the embodiment of the present application can also be performed by Figure 7 The electronic device architecture shown in FIG. Figure 7 As shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to a network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, may store the real-time dynamic adjustment control method and system of the wind turbine operation based on environmental data provided in the present application. The electronic device 500 may also include a terminal interface 508. Of course, Figure 7 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 7 One or more components of an electronic device are shown.

[0121] Figure 8 Schematic diagram of the computer-readable storage medium structure provided by an embodiment of the present application. Figure 8 As shown, a computer-readable storage medium 600 according to an embodiment of the present application is shown. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are executed by the processor, the real-time dynamic adjustment control method and system for wind turbine operation based on environmental data according to the embodiment of the present application described with reference to the above figures can be executed. The storage medium 600 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0122] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0123] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0124] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A real-time dynamic adjustment and control method for fan operation based on environmental data, characterized in that: include: Based on the distributed sensor network, multi-dimensional dynamic parameters of the fan environment are collected in real time; Build a dynamic coupling prediction model, input it into the two-way verification module based on multi-dimensional dynamic parameters, and obtain the environmental state prediction matrix in the future time window; Based on the environmental state prediction matrix, a joint adjustment instruction set of the wind turbine pitch angle, yaw angle and generator torque is obtained, wherein the instruction set includes main strategy parameters and emergency tolerance intervals; Based on the incremental learning mechanism, the model parameters are dynamically updated according to the deviation between the actual environmental parameters and the predicted values, and the update period is adaptively adjusted as the turbulence intensity changes.

2. The real-time dynamic adjustment and control method for fan operation based on environmental data according to claim 1 is characterized in that: The multi-dimensional dynamic parameters of the environment in which the fan is located are collected in real time based on the distributed sensor network, specifically including: The multi-dimensional dynamic parameters specifically include wind speed gradient distribution, turbulence intensity, temperature vertical gradient, humidity change rate and air pressure fluctuation data; The wind speed gradient distribution is obtained by synchronous scanning of three groups of lidars with a vertical height difference exceeding 20% ​​of the hub diameter. The second group of radars captures turbulent vortex characteristics at an elevation angle of 45 degrees. The sampling frequency is dynamically adjusted with the instantaneous wind speed standard deviation. When the standard deviation exceeds 1.5m / s, the high-frequency scanning mode is started.

3. The real-time dynamic adjustment and control method for fan operation based on environmental data according to claim 1 is characterized in that: The construction of the dynamic coupling prediction model, inputting the multi-dimensional dynamic parameters into the bidirectional verification module, and obtaining the environmental state prediction matrix in the future time window specifically includes: Based on the acquired multi-dimensional dynamic parameters, a dynamic coupling prediction model is constructed through a neural network, and the multi-dimensional dynamic parameters are input into a bidirectional verification module; The bidirectional verification module specifically includes an LSTM neural network and a physical constraint equation; The physical constraint equation includes the blade aeroelastic coupling equation and the tower resonance frequency constraint condition, and the formula is: in, is the aerodynamic load, is the pitch angle, is the structural damping coefficient, is the lateral displacement of the tower, is the time-varying material fatigue threshold. This constraint is embedded in the LSTM loss function through the Lagrange multiplier method, and t is the time in the time window; Future time window given by dynamic coupling prediction model Environmental status prediction data within The actual environmental status data , to obtain the accurate environmental state prediction matrix in the future time window, the formula is: Among them, C is the calibration coefficient, which is used to balance the impact of predicted values ​​and actual data.

4. The real-time dynamic adjustment and control method for fan operation based on environmental data according to claim 1 is characterized in that: The joint adjustment instruction set of wind turbine pitch angle, yaw angle and generator torque is obtained based on the environmental state prediction matrix, and the instruction set includes main strategy parameters and emergency tolerance intervals, specifically including: Get the environment state prediction matrix The wind turbine pitch angle , yaw angle , generator torque data; The wind turbine pitch angle Control the angle of the wind turbine blades to maximize the capture of wind energy or reduce excessive loads. The regulation function is: The yaw angle Control the direction of the fan relative to the wind so that the fan is aligned with the wind direction as much as possible. The adjustment formula is: The generator torque It mainly controls the power output of the fan, and its adjustment formula is: in, For future time window Wind speed data with inner dimension w, For future time window Temperature data within For future time window Internal turbulence intensity data, For future time window Wind direction data with inner dimension w; Generate control instructions based on each adjustment function, combine the main strategy parameters, and build a joint adjustment instruction set , in, , , is the weight coefficient, which is adjusted based on actual conditions; Based on the wind turbine pitch angle , yaw angle , generator torque Set the emergency tolerance interval based on the upper and lower limits of the system.

5. The real-time dynamic adjustment and control method for fan operation based on environmental data according to claim 1 is characterized in that: The wind turbine pitch angle , yaw angle , generator torque The upper and lower limits of the emergency tolerance interval include: Generated by Monte Carlo simulation, simulated environmental parameters are at predicted values Extreme working conditions within the range; When the real-time monitoring detects that the strain rate at the root of the blade exceeds 0.15με / s, it automatically switches to the load reduction priority mode and triggers the emergency pre-compression action of the hydraulic pitch system.

6. The real-time dynamic adjustment and control method for fan operation based on environmental data according to claim 1 is characterized in that: The incremental learning mechanism is based on which the model parameters are dynamically updated according to the deviation between the actual environmental parameters and the predicted values, and the update cycle is adaptively adjusted as the turbulence intensity changes, specifically including: Get real-time environmental parameter values (t), predicted value (t), calculate its error value: The model parameters are adjusted based on the gradient descent method. To update, the formula is: in, is the learning rate, is the gradient of the error; Based on the value of turbulence intensity, obtain its impact on the update cycle and adjust the next update cycle. The formula is: in, To adjust the coefficient, control the impact of turbulence intensity on the update period.

7. A method for real-time dynamic adjustment and control of fan operation based on environmental data is combined to realize a real-time dynamic adjustment and control system for fan operation based on environmental data as claimed in any one of claims 1 to 6, characterized in that: include: Distributed sensors: Real-time distributed sensors are mainly used to obtain multi-dimensional dynamic parameters, including wind speed sensors, temperature sensors, humidity sensors, air pressure sensors, lidar, etc. Prediction model module: The prediction model module is mainly used to obtain the environmental state prediction matrix within the future time window through the two-way verification module; Bidirectional verification module: The bidirectional verification module is mainly used for the operation and processing of LSTM neural network and physical constraint equations; Adjustment function module: The adjustment function module is mainly used to adjust the pitch angle of the wind turbine under different environmental conditions. , yaw angle , generator torque Adjustment of data; Joint regulation instruction set module: The joint regulation instruction set is mainly used to coordinate and optimize multiple control variables to improve the performance and stability of the system, and to set an emergency tolerance interval to ensure the operation of the wind turbine; Parameter optimization module: The parameter optimization module is mainly used to dynamically update model parameters according to the deviation between actual environmental parameters and predicted values; Processor: The processor is mainly used for the calculation process of each formula and the construction calculation process of each model.

8. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the real-time dynamic adjustment and control method for fan operation based on environmental data as described in any one of claims 1-6.

9. A computer-readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by a processor, the real-time dynamic adjustment and control method for wind turbine operation based on environmental data described in any one of claims 1-6 is implemented.

Images