Variable pitch control system and method for multi-objective optimization of wind turbine generator

Abstract

The invention discloses a variable pitch control system and method for multi-objective optimization of a wind turbine generator, and the system comprises an aerodynamic load analysis module which is used for building a blade waving and shimmy moment model based on a blade element momentum theory, and is used for analyzing a load source; the aerodynamic noise analysis module is used for analyzing an aerodynamic noise generation mechanism and establishing a mathematical model; the WSA-ICN optimization PID controller module is used for optimizing PID parameters so as to realize multi-target control of the system; the invention discloses a Coleman transformation and inverse transformation module, and particularly relates to the technical field of wind turbine generators. According to the method, on load suppression, by optimizing the independent variable pitch control strategy, the blade root waving bending moment mean square error is remarkably reduced, compared with traditional unified variable pitch control (CPC), the blade root waving bending moment mean square error is reduced by 29.17%, compared with existing independent variable pitch control (IPC), the maximum value is also greatly reduced, meanwhile, the pitching and yawing moment mean square error is also remarkably reduced, fatigue damage of the blade is effectively reduced, and the service life of the blade is prolonged. And the service life of the blade is prolonged by 15-20%.

Description

Technical Field

[0001] This invention relates to the field of wind turbine technology, specifically to a multi-objective optimization pitch control system and method for wind turbines. Background Technology

[0002] With the continuous development of wind power technology, large wind turbines face numerous challenges when operating at high wind speeds. On the one hand, the rotor surface is affected by factors such as shear and yaw error, resulting in uneven stress on the blades and periodic fluctuations in blade root bending moment. Traditional unified pitch control (CPC) is difficult to dynamically adjust for individual blades, accelerating blade fatigue damage and causing large power fluctuations. On the other hand, airfoil self-noise and tip vortex noise account for more than 70% of the total wind turbine noise. Traditional control strategies do not optimize the coordination between pitch angle and speed, resulting in severe noise pollution. Furthermore, traditional PID controller parameter tuning relies on experience, and algorithms such as particle swarm optimization (PSO) are prone to getting trapped in local optima and have slow convergence. Existing independent pitch control (IPC) methods do not effectively combine global optimization algorithms, resulting in insufficient dynamic response and difficulty in balancing load suppression and noise reduction. Summary of the Invention

[0003] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0004] In view of the problems existing in the existing multi-objective optimization pitch control system and method for wind turbines, the present invention is proposed.

[0005] Therefore, the purpose of this invention is to provide a pitch control system and method for multi-objective optimization of wind turbine generators.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a multi-objective optimized pitch control system for wind turbine generators, comprising:

[0007] The aerodynamic load analysis module establishes a blade flapping and oscillation moment model based on blade element momentum theory to analyze the load sources.

[0008] The aerodynamic noise analysis module is used to analyze the generation mechanism of aerodynamic noise and establish a mathematical model;

[0009] The WSA-ICN optimized PID controller module is used to optimize PID parameters to achieve multi-objective control of the system.

[0010] The Coleman transformation and inverse transformation module is used to convert the blade root load in the rotating coordinate system into the pitching moment and yaw moment in the stationary coordinate system, and obtain the pitch angle fine-tuning amount through the inverse transformation.

[0011] The noise prediction module combines BP neural network and multivariate regression analysis to establish the dynamic relationship between noise sound pressure level and multiple parameters, enabling real-time noise prediction and optimization.

[0012] As a preferred embodiment of the multi-objective optimization pitch control system for wind turbines described in this invention, wherein: in the aerodynamic load analysis module, the formula for calculating the relative wind speed at the blade element is: Swinging torque at leaf root Swinging torque Where v is the incoming wind speed, ω is the blade rotation angular velocity, a is the axial induction factor, b is the tangential induction factor, r is the distance between the blade element and the blade root, c is the chord length, r0 is the hub radius, and R is the wind turbine radius.

[0013] As a preferred embodiment of the multi-objective optimization pitch control system for wind turbines described in this invention, wherein: in the aerodynamic noise analysis module, the mathematical model of the wind turbine sound power level is as follows: Where L A For the sound power level of the wind turbine, L Ai Let R be the sound power level of the i-th noise source, R be the measurement distance, and C be the correction factor.

[0014] As a preferred embodiment of the multi-objective optimization pitch control system for wind turbines described in this invention, wherein: in the WSA-ICN optimized PID controller module, the control quantity calculation formula is as follows: The position iteration formula for the whale swarm algorithm is: Where w is the control variable, K p K i K d Here are the parameters of the PID controller, and e(t) is the error signal. Let ξ1 be the position of the i-th element of the whale in the t-th iteration, T be the initial ultrasonic intensity, and x be the maximum number of iterations. * This is the current globally optimal position.

[0015] As a preferred embodiment of the multi-objective optimization pitch control system for wind turbines described in this invention, the Coleman transformation module uses the following formula to convert the blade root load in the rotating coordinate system into pitch and yaw moments in the stationary coordinate system: The formula for obtaining the pitch angle fine-tuning in the Coleman inverse transform module. Where M pitch 、Myaw M represents the pitching moment and yaw moment in the stationary coordinate system. l1 、M l2 、M l3 Let θ1, θ2, and θ3 be the flapping torques of each blade in the rotating coordinate system, θ1, θ2, and θ3 be the azimuth angles of each blade, and Δβ1, Δβ2, and Δβ3 be the fine-tuning amounts of the pitch angle of each blade. pitch Δβ yaw This is the pitch angle adjustment in a stationary coordinate system.

[0016] As a preferred embodiment of the multi-objective optimization pitch control system and method for wind turbines described in this invention, the linear regression equation established in the noise prediction module is Y = a0 + a1X1 + a2X2 + ... + a8X8, where Y is the noise sound pressure level, X1 is the hub wind speed, X2 is the ambient temperature, X3 is the air pressure, X4 is the pitch angle, X5 is the wind direction, X6 is the observation tower wind speed, X7 is the output power, X8 is the rotational speed, and a0, a1, ..., a8 are regression coefficients.

[0017] As a preferred embodiment of the multi-objective optimization pitch control system for wind turbines described in this invention, the noise prediction module uses the least squares method to solve for the regression coefficients, obtaining the multiple linear regression equation Y = 30.3264 + 0.2778x1 + 0.6929x2 - 0.2276x3 - 0.1982x4 + 0.0126x5 + 0.2669x6 - 0.0017x7 + 0.1631x8.

[0018] As a preferred embodiment of the multi-objective optimization pitch control system for wind turbines described in this invention, it further includes a data acquisition module for real-time acquisition of data such as rotor speed, wind speed, wind direction, pitch angle, and blade root bending moment, providing data support for the calculation and analysis of each module.

[0019] As a preferred embodiment of the control method for the multi-objective optimization pitch control system of wind turbine generators described in this invention, it includes the following steps:

[0020] Initialize the WSA-ICN algorithm parameters, the PID controller parameter range, and the relevant fan parameters;

[0021] The wind turbine operating data is collected in real time through the data acquisition module;

[0022] The blade root load is calculated using the aerodynamic load analysis module, and the blade root load in the rotating coordinate system is converted into pitching moment and yaw moment in the stationary coordinate system using the Coleman transformation module.

[0023] The PID parameters are optimized using the WSA-ICN optimization PID controller module.

[0024] The optimized pitch angle signal output by the load control module is calculated based on the optimized PID parameters. The pitch angle fine-tuning amount of each blade is obtained through the Coleman inverse transform module, which drives the pitch angle controller to adjust the pitch angle.

[0025] The collected data is input into the noise prediction module to predict the noise sound pressure level. The target rotation speed is then determined based on the prediction results and input into the power control module to achieve noise reduction control.

[0026] Repeat the above steps to continuously control the wind turbine in real time.

[0027] As a preferred embodiment of the control method for the multi-objective optimization pitch control system of wind turbine generators described in this invention, wherein: in the step of optimizing the PID parameters using the WSA-ICN optimized PID controller module, the objective function model is... For guidance, where γ is the gain margin, γ th This is to preset the minimum amplitude margin; For phase margin, This is the preset minimum phase margin; t s To adjust the time, σ is the preset settling time; σ is the overshoot. th Preset overshoot;

[0028] In summary, the present invention has at least one of the following beneficial effects:

[0029] In terms of load suppression, this invention significantly reduces the root flapping moment variance by optimizing the independent pitch control strategy. Compared to traditional unified pitch control (CPC), it is reduced by 29.17%, and compared to existing independent pitch control (IPC), it is reduced by 26.99%. The maximum value is also significantly reduced. Simultaneously, the pitch and yaw moment variances are also significantly reduced, effectively reducing blade fatigue damage and extending blade life by 15%-20%. Regarding power stability, this strategy enables wind turbines to reach rated power faster at the same wind speed with smaller power fluctuations, performing better in high-wind-speed areas and improving the stability and reliability of output power. In terms of noise control, optimizing the pitch angle and rotational speed coordination reduces noise spikes, achieving effective noise reduction while ensuring stable power output. Furthermore, the employed WSA-ICN algorithm has fast convergence speed and strong global optimization capability, better adapting to complex dynamic conditions and comprehensively improving the operating performance and environmental adaptability of wind turbines. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0031] Figure 1 The flowchart of the WSA-ICN optimized PID algorithm of the present invention is shown. Detailed Implementation

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0033] This invention discloses a pitch control system and method for multi-objective optimization of wind turbine generators.

[0034] Please refer to Figure 1 As shown, a multi-objective optimization pitch control system for wind turbine generators is characterized by comprising:

[0035] The aerodynamic load analysis module establishes a blade flapping and swaying moment model based on blade element momentum theory to analyze load sources; in the aerodynamic load analysis module, the formula for calculating the relative wind speed at the blade element is as follows: Swinging torque at leaf root Swinging torque Where v is the incoming wind speed, ω is the blade rotation angular velocity, a is the axial induction factor, b is the tangential induction factor, r is the distance between the blade element and the blade root, c is the chord length, r0 is the hub radius, and R is the wind turbine radius.

[0036] The aerodynamic noise analysis module is used to analyze the generation mechanism of aerodynamic noise and establish a mathematical model; in the aerodynamic noise analysis module, the mathematical model of the fan sound power level is as follows: Where L A For the sound power level of the wind turbine, L Ai Let R be the sound power level of the i-th noise source, R be the measurement distance, and C be the correction factor.

[0037] The WSA-ICN optimized PID controller module is used to optimize PID parameters to achieve multi-objective control of the system; the control quantity calculation formula in the WSA-ICN optimized PID controller module is as follows: The position iteration formula for the whale swarm algorithm is: Where w is the control variable, K p K i K d Here are the parameters of the PID controller, and e(t) is the error signal. Let ξ1 be the position of the i-th element of the whale in the t-th iteration, T be the initial ultrasonic intensity, and x be the maximum number of iterations. * This is the current globally optimal position.

[0038] The Coleman transform and inverse transform module is used to convert blade root loads in a rotating coordinate system into pitch and yaw moments in a stationary coordinate system, and obtains the pitch angle fine-tuning amount through inverse transform. The formula for converting blade root loads in a rotating coordinate system into pitch and yaw moments in a stationary coordinate system in the Coleman transform module is as follows: The formula for obtaining the pitch angle fine-tuning in the Coleman inverse transform module. Where M pitch 、M yaw M represents the pitching moment and yaw moment in the stationary coordinate system. l1 、M l2 、M l3 Let θ1, θ2, and θ3 be the flapping torques of each blade in the rotating coordinate system, θ1, θ2, and θ3 be the azimuth angles of each blade, and Δβ1, Δβ2, and Δβ3 be the fine-tuning amounts of the pitch angle of each blade. pitch Δβ yaw This is the pitch angle adjustment in a stationary coordinate system.

[0039] The noise prediction module combines a BP neural network and multiple regression analysis to establish a dynamic relationship between noise sound pressure level and multiple parameters, enabling real-time noise prediction and optimization. The linear regression equation established in the noise prediction module is Y = a0 + a1X1 + a2X2 + ... + a8X8, where Y is the noise sound pressure level, X1 is the hub wind speed, X2 is the ambient temperature, X3 is the air pressure, X4 is the propeller pitch angle, X5 is the wind direction, X6 is the observation tower wind speed, X7 is the output power, X8 is the rotational speed, and a0, a1, ..., a8 are regression coefficients. The noise prediction module uses the least squares method to solve for the regression coefficients, obtaining the multiple linear regression equation Y = 30.3264 + 0.2778x1 + 0.6929x2 - 0.2276x3 - 0.1982x4 + 0.0126x5 + 0.2669x6 - 0.0017x7 + 0.1631x8.

[0040] It also includes a data acquisition module, which is used to collect data such as wind turbine speed, wind speed, wind direction, blade pitch angle, and blade root bending moment in real time, providing data support for the calculation and analysis of each module.

[0041] A control method for a multi-objective optimization pitch control system of a wind turbine generator, characterized by comprising the following steps:

[0042] Initialize the WSA-ICN algorithm parameters, the PID controller parameter range, and the relevant fan parameters;

[0043] The wind turbine operating data is collected in real time through the data acquisition module;

[0044] The blade root load is calculated using the aerodynamic load analysis module, and the blade root load in the rotating coordinate system is converted into pitching moment and yaw moment in the stationary coordinate system using the Coleman transformation module.

[0045] The PID parameters are optimized using the WSA-ICN optimization PID controller module.

[0046] The optimized pitch angle signal output by the load control module is calculated based on the optimized PID parameters. The pitch angle fine-tuning amount of each blade is obtained through the Coleman inverse transform module, which drives the pitch angle controller to adjust the pitch angle.

[0047] The collected data is input into the noise prediction module to predict the noise sound pressure level. The target rotation speed is then determined based on the prediction results and input into the power control module to achieve noise reduction control.

[0048] Repeat the above steps to continuously control the wind turbine in real time.

[0049] In the step of optimizing PID parameters using the WSA-ICN optimized PID controller module, the objective function model is used. For guidance, where γ is the gain margin, γ th This is to preset the minimum amplitude margin; For phase margin, This is the preset minimum phase margin; t s To adjust the time, σ is the preset settling time; σ is the overshoot. th Preset overshoot; In each iteration, the parameters are updated, and a better solution is found by escaping local extrema and searching the neighborhood.

[0050] Finally, a few points should be explained: First, in the description of this application, it should be noted that, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense, and may refer to mechanical or electrical connections, internal communication between two components, or direct connection. "Up," "down," "left," and "right" are only used to indicate relative positional relationships. When the absolute positions of the objects being described change, the relative positional relationships may also change.

[0051] The specific implementation method is as follows:

[0052] Initialization: Set WSA-ICN algorithm parameters, such as population size, maximum number of iterations, initial ultrasonic intensity, attenuation factor, etc.; initialize PID controller parameters K. p K i K d The range of values; input relevant wind turbine parameters, such as rated power, impeller radius, rated wind speed, etc.

[0053] Data acquisition: Real-time data such as rotor speed, wind speed, wind direction, blade pitch angle, and blade root bending moment are collected through sensors.

[0054] Control calculation: Based on the collected data, calculate the input of the power control module (the difference between the real-time speed of the wind turbine and the rated speed); calculate the blade root load (swaying moment and flapping moment) using the blade element momentum theory; and convert the blade root load in the rotating coordinate system into the pitching moment and yaw moment in the stationary coordinate system through Coleman transformation.

[0055] PID Parameter Optimization: Using the WSA-ICN algorithm to optimize the PID controller parameter K p K i K d Optimization is performed by updating parameters in each iteration, guided by the objective function model, and finding a better solution by escaping local extrema and neighborhood search.

[0056] Pitch angle adjustment: Based on the optimized PID parameters, the optimized pitch angle signal output by the load control module is calculated; the pitch angle fine-tuning amount of each blade is obtained through inverse Coleman transformation, and the final pitch angle change is obtained by adding it to the basic pitch angle, which drives the pitch angle controller to adjust the pitch angle of the wind turbine.

[0057] Noise prediction and control: The collected data such as wind speed, blade pitch angle, and power are input into the noise prediction module, which uses a combination of BP neural network and multivariate regression analysis to predict the noise sound pressure level; the target rotational speed is deduced from the prediction results and input into the power control module to achieve noise reduction control.

[0058] Cyclic execution: Repeat the steps to continuously control the wind turbine in real time, adjust the pitch angle according to dynamic wind conditions, and achieve multi-objective optimization.

[0059] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0060] In conclusion, 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 spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-objective optimized pitch control system for wind turbine generators, characterized in that, include: The aerodynamic load analysis module establishes a blade flapping and oscillation moment model based on blade element momentum theory to analyze the load sources. The aerodynamic noise analysis module is used to analyze the generation mechanism of aerodynamic noise and establish a mathematical model; The WSA-ICN optimized PID controller module is used to optimize PID parameters to achieve multi-objective control of the system. The Coleman transformation and inverse transformation module is used to convert the blade root load in the rotating coordinate system into the pitching moment and yaw moment in the stationary coordinate system, and obtain the pitch angle fine-tuning amount through the inverse transformation. The noise prediction module combines BP neural network and multivariate regression analysis to establish the dynamic relationship between noise sound pressure level and multiple parameters, enabling real-time noise prediction and optimization.

2. The multi-objective optimization pitch control system for wind turbines according to claim 1, characterized in that, In the aerodynamic load analysis module, the formula for calculating the relative wind speed at the blade element is as follows: Swinging torque at leaf root Swinging torque Where v is the incoming wind speed, ω is the blade rotation angular velocity, a is the axial induction factor, b is the tangential induction factor, r is the distance between the blade element and the blade root, c is the chord length, r0 is the hub radius, and R is the wind turbine radius.

3. The multi-objective optimization pitch control system for wind turbines according to claim 1, characterized in that, In the aerodynamic noise analysis module, the mathematical model for the fan's sound power level is as follows: Where L A For the sound power level of the wind turbine, L Ai Let R be the sound power level of the i-th noise source, R be the measurement distance, and C be the correction factor.

4. The multi-objective optimization pitch control system for wind turbines according to claim 1, characterized in that, In the WSA-ICN optimized PID controller module, the formula for calculating the control quantity is as follows: The position iteration formula for the whale swarm algorithm is: Where w is the control variable, K p K i K d Here are the parameters of the PID controller, and e(t) is the error signal. Let ξ1 be the position of the i-th element of the whale in the t-th iteration, T be the initial ultrasonic intensity, and x be the maximum number of iterations. * This is the current globally optimal position.

5. The multi-objective optimization pitch control system for wind turbines according to claim 1, characterized in that, In the Coleman transformation module, the formula for converting the blade root load in the rotating coordinate system into the pitching moment and yaw moment in the stationary coordinate system is as follows: The formula for obtaining the pitch angle fine-tuning in the Coleman inverse transform module. Where M pitch 、M yaw M represents the pitching moment and yaw moment in the stationary coordinate system. l1 、M l2 、M l3 Let θ1, θ2, and θ3 be the flapping torques of each blade in the rotating coordinate system, θ1, θ2, and θ3 be the azimuth angles of each blade, and Δβ1, Δβ2, and Δβ3 be the fine-tuning amounts of the pitch angle of each blade. pitch Δβ yaw This is the pitch angle adjustment in a stationary coordinate system.

6. The multi-objective optimization pitch control system for wind turbines according to claim 1, characterized in that, In the noise prediction module, the established linear regression equation is Y=a0+a1X1+a2X2+…+a8X8, where Y is the noise sound pressure level, X1 is the hub wind speed, X2 is the ambient temperature, X3 is the air pressure, X4 is the propeller pitch angle, X5 is the wind direction, X6 is the observation tower wind speed, X7 is the output power, X8 is the rotational speed, and a0, a1, …, a8 are regression coefficients.

7. The multi-objective optimization pitch control system for wind turbines according to claim 1, characterized in that, The noise prediction module uses the least squares method to solve for the regression coefficients, obtaining the multiple linear regression equation Y = 30.3264 + 0.2778x1 + 0.6929x2 - 0.2276x3 - 0.1982x4 + 0.0126x5 + 0.2669x6 - 0.0017x7 + 0.1631x8.

8. The multi-objective optimization pitch control system for wind turbines according to any one of claims 1-7, characterized in that, It also includes a data acquisition module, which is used to collect data such as wind turbine speed, wind speed, wind direction, blade pitch angle, and blade root bending moment in real time, providing data support for the calculation and analysis of each module.

9. A control method for a pitch control system of a wind turbine generator based on any one of claims 1-8, characterized in that, Includes the following steps: Initialize the WSA-ICN algorithm parameters, the PID controller parameter range, and the relevant fan parameters; The wind turbine operating data is collected in real time through the data acquisition module; The blade root load is calculated using the aerodynamic load analysis module, and the blade root load in the rotating coordinate system is converted into pitching moment and yaw moment in the stationary coordinate system using the Coleman transformation module. The PID parameters are optimized using the WSA-ICN optimization PID controller module. The optimized pitch angle signal output by the load control module is calculated based on the optimized PID parameters. The pitch angle fine-tuning amount of each blade is obtained through the Coleman inverse transform module, which drives the pitch angle controller to adjust the pitch angle. The collected data is input into the noise prediction module to predict the noise sound pressure level. The target rotation speed is then determined based on the prediction results and input into the power control module to achieve noise reduction control. Repeat the above steps to continuously control the wind turbine in real time.

10. The control method for the multi-objective optimization pitch control system of a wind turbine generator according to claim 9, characterized in that, In the step of optimizing PID parameters using the WSA-ICN optimized PID controller module, the objective function model is used. For guidance, where γ is the gain margin, γ th This is to preset the minimum amplitude margin; For phase margin, To preset the minimum phase margin; t s To adjust the time, σ is the preset settling time; σ is the overshoot. th Preset overshoot; In each iteration, the parameters are updated, and a better solution is found by escaping local extrema and searching the neighborhood.

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