A wind speed prediction method and system for wind power regulation

Abstract

The present application relates to the field of wind power generation technology, and provides a wind speed prediction method and system for wind power generation regulation, comprising the following steps: step 1, real-time acquisition of the operating state data of a vertical axis wind turbine is sent to a main controller MCU; step 2, the operating state data of step 1 is input into a preset physical and data fusion hybrid prediction model on the main controller MCU to calculate a predicted instantaneous torque; step 3, the predicted instantaneous torque and the predicted speed obtained by the main controller MCU in step 2 are interacted to a converter control unit VCU, the converter control unit VCU calculates an optimal transmission speed ratio, and the converter control unit VCU controls the gear shifting of a multi-gear gearbox connected to the blades according to the optimal transmission speed ratio to make the generator operate in a high-efficiency power generation area to realize maximum power point tracking regulation. The present application solves the problems of severe feedback control lag and low wind energy capture efficiency of the existing vertical axis wind turbine.

Description

Technical Field

[0001] This invention relates to the field of intelligent model control technology for power generation, and in particular to a wind speed prediction method and system for wind power generation control. Background Technology

[0002] Against the backdrop of escalating global warming and the continuous depletion of fossil fuel reserves, "carbon neutrality" has become a common strategic goal for more than 130 countries and regions. Renewable energy, as a core path to achieving a clean energy structure transformation, is experiencing continuous growth in both industry scale and technological maturity. Wind energy has enormous development potential, but the limitations of traditional horizontal-axis wind turbines are gradually becoming an obstacle to the development of the wind power industry. Horizontal-axis wind turbines suffer from high maintenance costs, high noise levels, and sensitivity to wind direction, among other drawbacks. Vertical-axis wind turbines (VAWT), with their advantages of not requiring wind alignment and simple structure, show great promise in the field of distributed wind power. However, during the rotation cycle of the rotor, the angle of attack of the blades relative to the wind flow continuously changes, causing the instantaneous torque captured by the main shaft to exhibit severe nonlinear periodic fluctuations. Existing vertical-axis wind turbines suffer from severe feedback control lag and low wind energy capture efficiency; relying solely on aerodynamic prediction makes it difficult to balance accuracy and safety; and braking under extreme conditions lacks prediction and safety redundancy.

[0003] Therefore, there is an urgent need for an innovative control system that can achieve physical and data fusion prediction to accurately predict instantaneous torque in advance, and can link variable speed ratio mechanism and safety redundancy hardware for intelligent modulation. Summary of the Invention

[0004] Therefore, in response to the above problems, this invention proposes a wind speed prediction method and system for wind power generation regulation, which can take into account both physical rigor and nonlinear adaptive capability. The system can predict wind conditions in advance, achieve highly accurate prediction of the instantaneous torque of vertical axis wind turbines, eliminate control lag, and achieve truly seamless and smooth maximum power point tracking operation control.

[0005] To solve this technical problem, the present invention adopts the following solution: a wind speed prediction method for wind power generation regulation, comprising the following steps: Step 1: Real-time acquisition of the operating status data of the vertical axis wind turbine and transmission to the main controller MCU. The operating status data includes real-time ambient wind speed, real-time rotational speed of the blade main shaft, and the current azimuth angle of the main shaft. Step 2: Input the operating status data from Step 1 into the preset physical and data fusion hybrid prediction model on the main controller MCU; first, call the aerodynamic function model of the hybrid prediction model to calculate the theoretical instantaneous torque based on the real-time rotational speed of the current blade main shaft and the real-time ambient wind speed, and call the aerodynamic function to calculate the predicted rotational speed based on the real-time ambient wind speed and tip speed ratio; then, construct the operating status data into a feature vector and input it into the LSTM neural network model of the hybrid prediction model. The LSTM neural network model outputs the torque dynamic compensation value representing the dynamic stall error of the flow field; the hybrid prediction model superimposes the obtained theoretical instantaneous torque and the obtained torque dynamic compensation value to calculate the predicted instantaneous torque; Step 3: The predicted instantaneous torque and predicted speed obtained by the main controller MCU in Step 2 are exchanged with the converter control unit VCU. The converter control unit VCU calculates the optimal transmission speed ratio, and the converter control unit VCU controls the multi-speed gearbox connected to the blades to shift gears according to the optimal transmission speed ratio through the hydraulic transmission circuit so that the generator operates in the high-efficiency power generation zone to achieve maximum power point tracking control.

[0006] Furthermore, the formula for calculating the theoretical instantaneous torque by calling aerodynamic functions in step 2 is as follows: , in the formula, air density; The radius of the wind turbine; The swept area of ​​the wind turbine; The instantaneous torque coefficient is determined by the current tip speed ratio. and azimuth Decide, Real-time ambient wind speed; This represents the real-time rotational speed of the blade spindle. The current azimuth angle of the main axis; And call the aerodynamic function to calculate the predicted rotational speed. The calculation formula is as follows: ; Real-time environmental wind speed based on operational status data using time series characteristics. Real-time rotational speed of the blade spindle Current azimuth angle of the main axis To construct the input vector The input is fed into the LSTM neural network model within the main controller; the LSTM neural network model uses a gating mechanism to handle the dynamic stall nonlinear error generated by the vertical axis fan during its rotation cycle. The gating mechanism is as follows: The forget gate decides to discard useless flow field interference information from the previous moment: , The input gate determines which new wind features are stored in the cell state: , Candidate cell states generate new features at the current moment: , Cellular state updates complete the fusion of long-term and short-term aerodynamic memories: , Output gate and hidden layer state extraction of nonlinear error features at the current time step: , The hidden layer temporal features extracted at the last time step The input is linearly mapped to a fully connected layer, and the output is a dynamic torque compensation value. , Adding the theoretical instantaneous torque to the dynamic torque compensation value yields the predicted instantaneous torque used for subsequent control: .

[0007] Furthermore, the maximum power point tracking control process in step 3 includes: the main controller MCU first calls the target speed calculation function to calculate the target optimal main shaft speed based on the optimal blade tip transmission speed ratio and the real-time ambient wind speed; then it calls the transmission speed ratio mapping function to calculate the ideal transmission speed ratio based on the ratio between the generator's optimal rated speed and the target optimal main shaft speed; the ideal transmission speed ratio is compared with the inherent transmission ratio of each gear in the multi-speed gearbox, and the gear with the smallest difference is selected as the optimal target gear. The main controller MCU interacts with the converter control unit VCU to control and drive the shift actuator connected to the multi-speed gearbox to perform shifting to the optimal target gear.

[0008] Furthermore, the ideal transmission ratio Obtained by: The optimal tip speed ratio corresponding to the peak value of the wind turbine's energy utilization coefficient is retrieved by the main controller MCU. It then calls the target rotation speed calculation function to calculate the target optimal spindle speed required to capture maximum wind energy at the current wind speed. : , Obtain the optimal rated speed from the generator efficiency MAP in the main controller MCU. Based on the aforementioned target optimal spindle speed, the current ideal transmission ratio is calculated. .

[0009] Furthermore, the multi-speed gearbox in step 3 includes three gears with different transmission ratios. The hydraulic transmission circuit includes a hydraulic motor pump, three sets of shift actuators, and three sets of proportional control valves. The hydraulic motor pump provides hydraulic power to the three sets of proportional control valves. The three sets of proportional control valves are connected to the three gears of the multi-speed gearbox through the three sets of shift actuators to control the switching of the three gears respectively. The converter control unit (VCU) compares the optimal transmission ratio with the transmission ratio of the three gears and controls the corresponding shift actuator to adjust to the corresponding gear through the proportional control valve connected to the pre-switched gear to achieve maximum power point tracking and control.

[0010] Furthermore, the hydraulic transmission circuit also includes a hydraulic accumulator, a second proportional control valve, a third proportional control valve, and a braking actuator. The hydraulic motor pump is connected to the hydraulic accumulator via the second proportional control valve, and the hydraulic accumulator is connected to the brake's actuation oil circuit via the third proportional control valve and the braking actuator.

[0011] A system based on a physical data fusion-based intelligent wind speed prediction and control method includes a vertical axis wind turbine rotor on the main shaft of a wind turbine, a brake, a multi-speed gearbox, a generator, a generator control unit, a power distribution unit (PDU), a power battery, multiple sets of first control valves, second control valves, and third control valves adapted to the number of gears in the multi-speed gearbox, a brake actuator, multiple sets of shift actuators adapted to the number of first control valves, a main controller (MCU), a hydraulic accumulator, a hydraulic motor pump, a converter control unit (VCU), an absolute encoder, and an anemometer. The vertical axis wind turbine rotor has blades. The brake is coaxially connected to the vertical axis wind turbine rotor. The multi-speed gearbox is coaxially connected to the brake. The generator is coaxially connected to the multi-speed gearbox and electrically connected to the motor control unit. The motor control unit is electrically connected to the power battery via the power distribution unit (PDU). The hydraulic motor pump is used for each set of first control valves, second control valves, and third control valves. The control valve provides a hydraulic power source. The hydraulic motor pump is connected to the hydraulic accumulator via the second control valve. The hydraulic accumulator is connected to the brake's execution oil circuit via the third control valve through the brake actuator for emergency braking and energy replenishment in case of power failure. The anemometer is installed on the main shaft of the wind turbine to detect the real-time ambient wind speed and send it to the main controller MCU. The absolute encoder is installed on the main shaft of the wind turbine to synchronously acquire the real-time rotational speed of the blades and the current azimuth angle of the main shaft and send it to the main controller MCU. The main controller MCU interacts with the converter control unit VCU based on the calculated predicted instantaneous torque and predicted speed. The converter control unit VCU calculates the optimal transmission speed ratio and controls the gear switching and regulation through each group of first control valves and each group of shift actuators connected to each gear of the multi-speed gearbox to make the generator operate in the high-efficiency power generation zone and achieve maximum power point tracking regulation.

[0012] Furthermore, it also includes solar photovoltaic panels and lead-acid batteries. The solar photovoltaic panels are connected to the lead-acid batteries to charge them. The lead-acid batteries are connected to the inverter control unit (VCU) and the power distribution unit (PDU) to store photovoltaic energy and maintain power for instantaneous torque prediction and braking protection.

[0013] By adopting the aforementioned technical solution, the beneficial effects of this invention are as follows: Compared with existing technologies, it employs an innovative intelligent model control method based on physical data fusion for wind speed prediction, balancing physical rigor with nonlinear adaptive capability, thus breaking through the traditional single calculation method. A hybrid prediction model integrating physical and data fusion is preset on the main controller MCU, using a classical aerodynamic function model as a foundation, combined with an LSTM neural network dynamic model to fit dynamic stall and turbulence disturbance errors in the flow field. This achieves extremely high-precision advance prediction of the instantaneous torque of the vertical axis wind turbine, eliminating control lag and achieving truly seamless and smooth maximum power point tracking. Relying on the high-precision advance prediction results, the system can predict wind trends in advance and smoothly switch between different gears in the multi-speed gearbox before the generator deviates from the high-efficiency zone, maximizing wind energy capture efficiency and significantly reducing the hysteresis impact of the mechanical transmission chain. This invention can accurately predict the instantaneous torque peak before damage occurs, and combined with the brake and accumulator emergency energy replenishment mechanism in the hardware system, it realizes an intelligent safety defense system. Attached Figure Description

[0014] Figure 1 This is a system schematic diagram of an embodiment of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] The terms "first," "second," "third," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0017] refer to Figure 1The preferred system of the wind speed prediction method for wind power generation regulation of the present invention includes a vertical axis wind turbine rotor on the main shaft of the wind turbine, a brake 2, a multi-speed gearbox 3, a generator 4, a generator control unit 5, a power distribution unit (PDU) 6, a power battery 7, three sets of first control valves 8, second control valves 9, and third control valves 10 adapted to the number of gears in the multi-speed gearbox 3, a brake actuator 11, multiple sets of shift actuators 12 adapted to the number of first control valves 8, a main controller (MCU) 13, a hydraulic accumulator 14, a hydraulic motor pump 15, a converter control unit (VCU) 16, and a solar photovoltaic panel 1. 7. Lead-acid battery 18, absolute encoder and anemometer; the vertical axis wind turbine rotor has blades 1; the brake 2 is coaxially connected to the vertical axis wind turbine rotor; the multi-speed gearbox 3 is coaxially connected to the brake 2; the generator 4 is coaxially connected to the multi-speed gearbox 3 and electrically connected to the motor control unit 5; the motor control unit 5 is electrically connected to the power battery 7 via the power distribution unit PDU6; the first control valve 8, the second control valve 9, and the third control valve 10 are all proportional control valves; the hydraulic motor pump 15 provides hydraulic power to each group of the first control valve 8 and the second control valve 9; the hydraulic motor pump 15 is connected via... The second control valve 9 is connected to the hydraulic accumulator 14. The hydraulic accumulator 14 is connected to the brake 2 via the third control valve 10 and the brake actuator 11 to perform emergency braking and energy replenishment in case of power failure. The anemometer is installed on the main shaft of the wind turbine to detect the real-time ambient wind speed and send it to the main controller MCU13. The absolute encoder is installed on the main shaft of the wind turbine to synchronously acquire the real-time rotational speed of the blades and the current azimuth angle of the main shaft and send it to the main controller MCU13. The main controller MCU13 interacts with the converter control unit VCU16 based on the calculated predicted instantaneous torque and predicted speed. CU16 calculates the optimal transmission ratio, and the converter control unit VCU16 controls the gear switching and regulation by connecting each group of first control valves 8 to each gear position of the multi-speed gearbox 3 through each group of shift actuators 12, so that the generator 4 operates in the high-efficiency power generation zone to achieve maximum power point tracking regulation. The solar photovoltaic panel 17 is connected to the lead-acid battery 18 to charge the lead-acid battery 18. The lead-acid battery 18 is connected to the converter control unit VCU16 and the power distribution unit PDU6 to store photovoltaic energy and maintain power for instantaneous torque prediction and braking protection.

[0018] The wind speed prediction method for wind power generation regulation based on the aforementioned system includes the following steps: Step 1: Acquire real-time operating status data of the vertical axis wind turbine and send it to the main controller MCU13. The operating status data includes real-time ambient wind speed. Real-time rotational speed of blade 1 main shaft and the current azimuth angle of the main axis ; Step 2: Input the operating status data from Step 1 into the preset physical and data fusion hybrid prediction model on the main controller MCU13; first, call the aerodynamic function model of the hybrid prediction model to calculate the theoretical instantaneous torque based on the real-time rotational speed of the current blade main shaft and the real-time ambient wind speed, and call the aerodynamic function to calculate the predicted rotational speed based on the real-time ambient wind speed and blade tip speed ratio; then, construct the operating status data into a feature vector and input it into the LSTM neural network model of the hybrid prediction model. The LSTM neural network model outputs the torque dynamic compensation value characterizing the dynamic stall error of the flow field; the hybrid prediction model superimposes the obtained theoretical instantaneous torque and the obtained torque dynamic compensation value to calculate the predicted instantaneous torque; The formula for calculating the theoretical instantaneous torque using aerodynamic functions is as follows: , in the formula, air density; The radius of the wind turbine; The swept area of ​​the wind turbine; The instantaneous torque coefficient is determined by the current tip speed ratio. and azimuth Decide, Real-time ambient wind speed; This represents the real-time rotational speed of the blade spindle. The current azimuth angle of the main axis; And call the aerodynamic function to calculate the predicted rotational speed. The calculation formula is as follows: ; Real-time environmental wind speed based on operational status data using time series characteristics. Real-time rotational speed of the blade spindle Current azimuth angle of the main axis To construct the input vector The input is fed into the LSTM neural network model within the main controller; the LSTM neural network model uses a gating mechanism to handle the dynamic stall nonlinear error generated by the vertical axis fan during its rotation cycle. The gating mechanism is as follows: The forget gate decides to discard useless flow field interference information from the previous moment: , The input gate determines which new wind features are stored in the cell state: , Candidate cell states generate new features at the current moment: , Cellular state updates complete the fusion of long-term and short-term aerodynamic memories: , Output gate and hidden layer state extraction of nonlinear error features at the current time step: , The hidden layer temporal features extracted at the last time step The input is linearly mapped to a fully connected layer, and the output is a dynamic torque compensation value. , Adding the theoretical instantaneous torque to the dynamic torque compensation value yields the predicted instantaneous torque used for subsequent control: .

[0019] Step 3: The predicted instantaneous torque and predicted speed obtained by the main controller MCU13 in Step 2 are exchanged with the converter control unit VCU16. The converter control unit VCU16 calculates the optimal transmission speed ratio, and according to the optimal transmission speed ratio, the converter control unit VCU16 controls the gear switching and regulation by connecting each group of first control valves 8 to each gear position of the multi-speed gearbox 3 through each group of shift actuators 12, so that the generator 4 operates in the high-efficiency power generation zone to achieve maximum power point tracking regulation.

[0020] The process of achieving maximum power point tracking (MPPT) control includes: the main controller MCU13 first calls the target speed calculation function to calculate the target optimal spindle speed based on the optimal tip transmission speed ratio and the real-time ambient wind speed; then it calls the transmission speed ratio mapping function based on the generator's optimal rated speed. With the target optimal spindle speed The ideal transmission ratio is calculated based on the proportional relationship. The difference between the ideal transmission ratio and the inherent transmission ratio of each gear in the multi-speed gearbox 3 is compared, and the gear with the smallest difference is selected as the optimal target gear. The main controller MCU13 interacts with the converter control unit VCU16 to control the shift actuator 12 of the multi-speed gearbox 3 to perform the shift when the optimal target gear is selected.

[0021] Among them, ideal transmission ratio Obtained by: The optimal tip speed ratio corresponding to the peak value of the wind turbine's energy utilization coefficient is retrieved by the main controller MCU13. It then calls the target rotation speed calculation function to calculate the target optimal spindle speed required to capture maximum wind energy at the current wind speed. : , The optimal rated speed is obtained from the generator 4 efficiency MAP in the main controller MCU13. Based on the aforementioned target optimal spindle speed, the current ideal transmission ratio is calculated. .

[0022] In the above embodiments, the vertical axis wind turbine rotor, brake, multi-speed gearbox, generator, generator control unit, power distribution unit (PDU), power battery, first control valve, second control valve, third control valve, brake actuator, gear shifting actuator, main controller (MCU), hydraulic accumulator, hydraulic motor pump, converter control unit (VCU), solar photovoltaic panel, and lead-acid battery are all existing components.

[0023] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention. Although the present invention has been specifically shown and described in conjunction with preferred embodiments, those skilled in the art should understand that various changes in form and detail can be made to the present invention without departing from the spirit and scope of the invention as defined in the appended claims, and all such changes are within the scope of protection of the present invention.

Claims

1. A wind speed prediction method for wind power generation regulation, characterized in that: Includes the following steps: Step 1: Real-time acquisition of the operating status data of the vertical axis wind turbine and transmission to the main controller MCU. The operating status data includes real-time ambient wind speed, real-time rotational speed of the blade main shaft, and the current azimuth angle of the main shaft. Step 2: Input the operating status data from Step 1 into the preset physical and data fusion hybrid prediction model on the main controller MCU; first, call the aerodynamic function model of the hybrid prediction model to calculate the theoretical instantaneous torque based on the real-time rotational speed of the current blade main shaft and the real-time ambient wind speed, and call the aerodynamic function to calculate the predicted rotational speed based on the real-time ambient wind speed and tip speed ratio; then, construct the operating status data into a feature vector and input it into the LSTM neural network model of the hybrid prediction model. The LSTM neural network model outputs the torque dynamic compensation value representing the dynamic stall error of the flow field; the hybrid prediction model superimposes the obtained theoretical instantaneous torque and the obtained torque dynamic compensation value to calculate the predicted instantaneous torque; Step 3: The predicted instantaneous torque and predicted speed obtained by the main controller MCU in Step 2 are exchanged with the converter control unit VCU. The converter control unit VCU calculates the optimal transmission speed ratio, and the converter control unit VCU controls the multi-speed gearbox connected to the blades to shift gears according to the optimal transmission speed ratio through the hydraulic transmission circuit so that the generator operates in the high-efficiency power generation zone to achieve maximum power point tracking control.

2. The wind speed prediction method for wind power generation regulation according to claim 1, characterized in that: The formula for calculating the theoretical instantaneous torque in step 2, which involves calling aerodynamic functions, is as follows: , in the formula, air density; The radius of the wind turbine; The swept area of ​​the wind turbine; The instantaneous torque coefficient is determined by the current tip speed ratio. and azimuth Decide, Real-time ambient wind speed; This represents the real-time rotational speed of the blade spindle. The current azimuth angle of the main axis; And call the aerodynamic function to calculate the predicted rotational speed. The calculation formula is as follows: ; Real-time environmental wind speed based on operational status data using time series characteristics. Real-time rotational speed of the blade spindle Current azimuth angle of the main axis To construct the input vector The input is fed into the LSTM neural network model within the main controller; the LSTM neural network model uses a gating mechanism to handle the dynamic stall nonlinear error generated by the vertical axis fan during its rotation cycle. The gating mechanism is as follows: The forget gate decides to discard useless flow field interference information from the previous moment: ， The input gate determines which new wind features are stored in the cell state: ， Candidate cell states generate new features at the current moment: ， Cellular state updates complete the fusion of long-term and short-term aerodynamic memories: ， Output gate and hidden layer state extraction of nonlinear error features at the current time step: ， The hidden layer temporal features extracted at the last time step The input is linearly mapped to a fully connected layer, and the output is a dynamic torque compensation value. ， Adding the theoretical instantaneous torque to the dynamic torque compensation value yields the predicted instantaneous torque used for subsequent control: 。 3. The wind speed prediction method for wind power generation regulation according to claim 1, characterized in that: The maximum power point tracking control process in step 3 includes: the main controller MCU first calls the target speed calculation function to calculate the target optimal main shaft speed based on the optimal blade tip transmission speed ratio and the real-time ambient wind speed; then it calls the transmission speed ratio mapping function to calculate the ideal transmission speed ratio based on the ratio between the generator's optimal rated speed and the target optimal main shaft speed; the ideal transmission speed ratio is compared with the inherent transmission ratio of each gear in the multi-speed gearbox, and the gear with the smallest difference is selected as the optimal target gear. The main controller MCU interacts with the converter control unit VCU to control and drive the shift actuator connected to the multi-speed gearbox to perform shifting to the optimal target gear.

4. The wind speed prediction method for wind power generation regulation according to claim 3, characterized in that: Ideal transmission ratio Obtained by: The optimal tip speed ratio corresponding to the peak value of the wind turbine's energy utilization coefficient is retrieved by the main controller MCU. It then calls the target rotation speed calculation function to calculate the target optimal spindle speed required to capture maximum wind energy at the current wind speed. : ， Obtain the optimal rated speed from the generator efficiency MAP in the main controller MCU. Based on the aforementioned target optimal spindle speed, the current ideal transmission ratio is calculated. .

5. The wind speed prediction method for wind power generation regulation according to claim 1, characterized in that: The multi-speed gearbox in step 3 includes three gears with different transmission ratios. The hydraulic transmission circuit includes a hydraulic motor pump, three sets of shift actuators, and three sets of proportional control valves. The hydraulic motor pump provides hydraulic power to the three sets of proportional control valves. The three sets of proportional control valves are connected to the three gears of the multi-speed gearbox through the three sets of shift actuators to control the switching of the three gears. The converter control unit (VCU) compares the optimal transmission ratio with the transmission ratio of the three gears and controls the corresponding shift actuator to adjust to the corresponding gear through the proportional control valve connected to the pre-switched gear to achieve maximum power point tracking and control.

6. The wind speed prediction method for wind power generation regulation according to claim 5, characterized in that: The hydraulic transmission circuit also includes a hydraulic accumulator, a second proportional control valve, a third proportional control valve, and a braking actuator. The hydraulic motor pump is connected to the hydraulic accumulator via the second proportional control valve, and the hydraulic accumulator is connected to the brake's actuation oil circuit via the third proportional control valve and the braking actuator.

7. The system for wind speed prediction method for wind power generation regulation according to any one of claims 1-6, characterized in that: The system includes a vertical axis wind turbine rotor on the main shaft of the wind turbine, a brake, a multi-speed gearbox, a generator, a generator control unit, a power distribution unit (PDU), a power battery, multiple sets of first control valves, second control valves, and third control valves adapted to the number of gears in the multi-speed gearbox, a brake actuator, multiple sets of shift actuators adapted to the number of first control valves, a main controller (MCU), a hydraulic accumulator, a hydraulic motor pump, a converter control unit (VCU), an absolute encoder, and an anemometer. The vertical axis wind turbine rotor has blades. The brake is coaxially connected to the vertical axis wind turbine rotor. The multi-speed gearbox is coaxially connected to the brake. The generator is coaxially connected to the multi-speed gearbox and electrically connected to the motor control unit. The motor control unit is electrically connected to the power battery via the power distribution unit (PDU). The hydraulic motor pump provides hydraulic power to each set of first and second control valves. The electric motor pump is connected to the hydraulic accumulator via the second control valve. The hydraulic accumulator is connected to the brake's actuator oil circuit via the third control valve for emergency braking and energy replenishment in case of power failure. The anemometer is installed on the main shaft of the wind turbine to detect the real-time ambient wind speed and send it to the main controller MCU. The absolute encoder is installed on the main shaft of the wind turbine to synchronously acquire the real-time rotational speed of the blades and the current azimuth angle of the main shaft and send it to the main controller MCU. The main controller MCU interacts with the converter control unit (VCU) based on the calculated predicted instantaneous torque and predicted rotational speed. The converter control unit (VCU) calculates the optimal transmission speed ratio and controls the gear switching and regulation based on the optimal transmission speed ratio through each group of first control valves, which are connected to each gear of the multi-speed gearbox via each group of shift actuators, so that the generator operates in the high-efficiency power generation zone to achieve maximum power point tracking regulation.

8. The system for wind speed prediction method for wind power generation regulation according to claim 7, characterized in that: It also includes a solar photovoltaic panel and a lead-acid battery. The solar photovoltaic panel is connected to the lead-acid battery to charge the lead-acid battery. The lead-acid battery is connected to the inverter control unit (VCU) and the power distribution unit (PDU) respectively to store photovoltaic energy and maintain power for instantaneous torque prediction and braking protection.

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