An independent pitch control method for reducing load of large wind turbine generators

Abstract

The application discloses an independent variable pitch control method for reducing load of a large wind turbine. In view of the unbalanced load problem of the large wind turbine caused by the inconsistent wind speed distribution in the wind swept area of the wind wheel, the independent variable pitch PI control technology is used. Firstly, the particle swarm optimization algorithm is used to perform offline optimization on the control parameters of the PI controller, so that the PI control parameters are optimized. Then, based on the optimal parameters, the PI parameters are corrected online based on the fuzzy logic control, so that the PI control parameters are adaptively changed with the load. Compared with the traditional PI control, the method adaptively changes the PI parameters with the load based on the optimized PI parameters, optimizes the control parameters, effectively makes up for the insufficient control effect of the fixed parameter control under certain working conditions, and effectively reduces the load of the large wind turbine.

Description

Technical Field

[0001] This invention belongs to the field of wind power generation technology, and in particular, it is an independent pitch control method for reducing the load on large wind turbine units. Background Technology

[0002] As wind turbines become increasingly larger, the length of their rotor blades also increases. The unbalanced load caused by inconsistent wind speed distribution within the swept surface has become a significant factor affecting wind turbine performance. Load feedback-based independent pitch PI control for wind turbines has been used to address this issue. However, in engineering applications, traditional PI controllers often have fixed proportional and integral coefficients, which may not be optimal. Furthermore, fixed-coefficient PI controllers do not perform well in load reduction control under complex operating conditions, leading to the continuous accumulation of fatigue damage to the rotor and ultimately impacting wind turbine performance.

[0003] Against this backdrop, scholars at home and abroad have conducted numerous studies on independent pitch control strategies to reduce unbalanced loads on wind turbines, such as robust control, active disturbance rejection control, model predictive control, and model-free adaptive control. These strategies have, to some extent, compensated for the shortcomings of traditional PI controllers. However, these controls generally involve complex control algorithms and have deviated from the scope of PI control that is already widely used in engineering.

[0004] Since most wind turbine pitch controllers in engineering still employ traditional PI control, PI control and its improved methods remain highly valuable in the field of wind turbine pitch control. To this end, domestic and international research has proposed control strategies using iterative optimization algorithms such as genetic algorithms, sparrow optimization algorithms, and artificial bee colony optimization to optimize the parameters of independent pitch PI controllers. However, these methods are limited by the design framework of iterative optimization algorithms and are only suitable for offline optimization of PI parameters. Offline optimization, in turn, requires a clearly defined fitness function based on known operating conditions, thus limiting the effectiveness of these methods. Some studies have also proposed adaptive PI pitch control methods based on wind speed or load. However, the initial values ​​of the PI controller in these methods are often designed based on engineering experience, and the design of the adaptive rules also has a significant empirical component, lacking theoretical basis, and their initial values ​​have considerable room for optimization. Therefore, there is a need to design new independent pitch load reduction control strategies for large wind turbines based on PI controllers. Summary of the Invention

[0005] The purpose of this invention is to address the problems existing in the prior art by providing an effective independent pitch control strategy for wind turbines based on a PI controller.

[0006] The technical solution to achieve the objective of this invention is: an independent pitch control method for reducing load on large wind turbine units, the method comprising the following steps:

[0007] Step 1: Use the particle swarm optimization algorithm to perform offline optimization of the control parameters of the PI controller to obtain the optimal PI parameters;

[0008] Step 2: Input the optimal PI parameters into the PI controller;

[0009] Step 3: Based on the fuzzy logic controller, the optimal PI parameters of the PI controller are self-corrected online using fuzzy logic.

[0010] Step 4: The modified PI controller outputs independent pitch command values ​​to obtain the pitch action requirement.

[0011] Step 5: Transmit the independent pitch command value to the wind turbine independent pitch actuator to execute independent pitch control.

[0012] Furthermore, step 1 specifically includes:

[0013] Step 1-1: Initialize the particle swarm optimization algorithm for offline optimization calculation of PI parameters, and set the fitness function F and the initial value of the particle swarm for calculating the unbalanced load and speed deviation.

[0014] Steps 1-2: Calculate the fitness function F;

[0015] Steps 1-3: Determine whether the fitness function F and the number of iterations meet the termination condition. If the termination condition is met, output the best offline optimization PI parameter, i.e., the optimal PI parameter, and execute step 2. If the termination condition is not met, update the parameters and return to step 1-2.

[0016] Further, in steps 1-2, the fitness function F is:

[0017]

[0018] In the formula, F IAE Let M be the fitness function with the absolute error integral criterion as the performance index; tilt |、|M yaw | represents the absolute values ​​of the overturning and yaw moments acting on the hub, obtained by measuring the root bending moment of the wind turbine blades using a torque sensor installed at the root of the blades and then performing coordinate transformation; |e(t)| represents the absolute value of the deviation between the actual rotational speed and the rated rotational speed of the wind turbine; a and b are weighting coefficients; T represents the integration time; dt represents the integration sign.

[0019] Furthermore, the M tilt M yaw The calculation method is as follows:

[0020]

[0021] In the formula, P is the transformation matrix, and M... y1M y2 M y3 These are time-varying variables that rotate with the wind turbine, representing the out-of-plane bending moments at the roots of blades 1, 2, and 3 of a three-bladed horizontal-axis wind turbine, respectively; M tilt M yaw The overturning and yaw moments acting on the wheel hub after coordinate transformation are represented by the transformation matrix P as follows:

[0022]

[0023] In the formula, θ is the azimuth angle of blade 1, which is measured by the blade azimuth sensor.

[0024] Furthermore, the PI controller in step 2 takes the following form:

[0025]

[0026] In the formula, G(s) represents the transfer function, K p K is the proportionality coefficient. i is the integral coefficient, and s is the differential operator.

[0027] Furthermore, step 3 specifically includes:

[0028] Step 3-1: Obtain the current unbalanced load value M of the wind turbine and calculate its differential value;

[0029] Step 3-2: Input the wind turbine unbalanced load value M and its derivative value into the fuzzy logic controller;

[0030] Step 3-3: The fuzzy logic controller determines the magnitude of the PI parameter correction based on the wind turbine unbalanced load value M and its derivative value, and inputs it into the PI controller to perform online correction of the PI controller parameters.

[0031] Furthermore, the corrected formula in step 3-3 is as follows:

[0032] K p =Opt_K p +ΔK p

[0033] K i =Opt_K i +ΔK i

[0034] In the formula, K p K is the proportional gain used in the actual wind turbine pitch controller, and it is a time-varying variable; i Opt_K is the integral coefficient used in the actual wind turbine pitch controller, and it is a time-varying variable. p Opt_K iThese are the optimal proportional coefficient and integral coefficient after offline optimization in step 3, respectively, and are constants; ΔK p ΔK i These are the correction amounts for the proportional and integral coefficients of the PI controller output by the fuzzy logic controller, respectively.

[0035] Furthermore, in step 4, the PI controller output is the coordinate transformation and direct-axis pitch command value β. d and β q After coordinate transformation as shown in the following formula, the pitch command values ​​of the three blades are obtained.

[0036]

[0037] In the formula, β d and β q β1, β2, and β3 are the pitch command values ​​for the direct and quadrature axes, respectively, while β1, β2, and β3 are the pitch command values ​​for the three blades of the horizontal axis wind turbine.

[0038] Compared with the prior art, the significant advantages of this invention are:

[0039] (1) This invention is based on the PI controller widely used in actual engineering. The engineering implementation of the control method is less complex than other intelligent control or nonlinear control algorithms. It can be implemented by improving the existing wind turbine PI controller, which is beneficial for engineering applications.

[0040] (2) The present invention is a control method based on fuzzy logic controller to self-correct PI parameters according to actual unbalanced load. Compared with the existing fixed parameter PI control, it can adaptively correct according to the operating conditions of wind turbine. Since the initial value of the PI parameter of the present invention is an offline optimization parameter, the load reduction effect of the control method proposed in the present invention is significantly better than the existing independent pitch PI control technology.

[0041] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0042] Figure 1 This is a flowchart of an independent pitch control system for reducing load on large wind turbine units.

[0043] Figure 2 This is a schematic diagram of the blade load of a wind turbine in different coordinate systems in one embodiment. Figure 2 (a) and (b) in the figure are load diagrams of the rotating coordinate system at the blade root and the fixed coordinate system at the hub, respectively.

[0044] Figure 3 This is a schematic diagram of the offline parameter optimization process based on the particle swarm optimization algorithm in one embodiment.

[0045] Figure 4This is a schematic diagram of an online self-correction process for PI parameters based on fuzzy logic control in one embodiment.

[0046] Figure 5 This is a schematic diagram of the entire offline optimization-online self-correcting control strategy in one embodiment.

[0047] Figure 6 This is a schematic diagram of turbulent wind speed used for simulation calculation in one embodiment.

[0048] Figure 7 This is a comparison diagram of the effects of the present invention and the conventional method on reducing the hub overturning moment of a wind turbine in one embodiment.

[0049] Figure 8 This is a comparison diagram of the effects of the present invention and the conventional method on reducing the hub yaw moment of a wind turbine in one embodiment.

[0050] Figure 9 This is a comparison diagram of the out-of-plane bending moment at the blade root between the method of the present invention and the conventional method in one embodiment.

[0051] Figure 10 This is a graph showing the mean normalization comparison of the control performance of the method of the present invention and the conventional method in one embodiment.

[0052] Figure 11 This is a graph showing the normalized standard deviation of the control performance of the method of the present invention and the conventional method in one embodiment.

[0053] Figure 12 This is a comparison chart showing the effects of the method of the present invention and the conventional method in reducing the output power fluctuation of a wind turbine in one embodiment. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0055] It should be noted that if the embodiments of the present invention involve descriptions such as "first" and "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" and "second" may explicitly or implicitly include at least one of those features. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0056] In one embodiment, combined Figure 1 and Figure 3 An independent pitch control method for reducing load on large wind turbine units is provided, the method comprising the following steps:

[0057] Step 1: Initialize the particle swarm optimization algorithm for offline optimization calculation of PI parameters, and set the fitness function F and the initial values ​​of the particle swarm (including initializing the position of each particle in the particle swarm) for calculating the unbalanced load and speed deviation.

[0058] Step 2: Calculate the fitness function F based on the initial position of the particle;

[0059] Step 3: Determine whether the fitness function F and the number of iterations meet the termination condition. If the termination condition is met, output the best offline optimization PI parameter, i.e., the optimal PI parameter. If the termination condition is not met, update the parameters and return to step 2.

[0060] Step 4: Input the optimal PI parameters into the PI controller;

[0061] Step 5: Based on the fuzzy logic controller, the optimal PI parameters of the PI controller are self-corrected online using fuzzy logic.

[0062] Step 6: The modified PI controller outputs independent pitch command values ​​to obtain the pitch action requirement.

[0063] Step 7: Transmit the independent pitch command value to the wind turbine independent pitch actuator to execute independent pitch control.

[0064] Furthermore, in one embodiment, the fitness function F in step 1 is as shown in equation (1):

[0065]

[0066] In the formula, F IAE Let M be the fitness function with integrated absolute error (IAE) as the performance index; tilt |、|M yaw | represents the absolute values ​​of the overturning and yaw moments acting on the hub, obtained by measuring the root bending moment of the wind turbine blades using a torque sensor installed at the root of the blades and then performing coordinate transformation; |e(t)| represents the absolute value of the deviation between the actual rotational speed and the rated rotational speed of the wind turbine; a and b are weighting coefficients; T represents the integration time; dt represents the integration sign.

[0067] Here, in some embodiments, the M tilt M yaw The calculation method is as follows:

[0068]

[0069] In the formula, P is the transformation matrix, and M... y1 M y2 M y3 These are time-varying variables that rotate with the wind turbine, representing the out-of-plane bending moments at the roots of blades 1, 2, and 3 of a three-bladed horizontal-axis wind turbine, respectively; M tilt M yaw The overturning and yaw moments acting on the wheel hub after coordinate transformation are represented by the transformation matrix P as follows:

[0070]

[0071] In the formula, θ is the azimuth angle of blade 1, which is measured by the blade azimuth sensor.

[0072] Figure 2 The representation of wind turbine load in different coordinate systems is given, which can be combined with equations (2) and (3).

[0073] Furthermore, in one embodiment, the PI controller in step 4 takes the form of:

[0074]

[0075] In the formula, G(s) represents the transfer function, K p K is the proportionality coefficient. i is the integral coefficient, and s is the differential operator.

[0076] Furthermore, in one embodiment, combined with Figure 4 and Figure 5 Step 5 specifically includes:

[0077] Step 5-1: Obtain the current unbalanced load value M of the wind turbine and calculate its differential value;

[0078] Step 5-2: Input the wind turbine unbalanced load value M and its derivative value into the fuzzy logic controller;

[0079] Step 5-3: The fuzzy logic controller determines the magnitude of the correction amount for the PI parameters based on the wind turbine unbalanced load value M and its derivative value, and inputs it into the PI controller to perform online correction of the PI controller parameters.

[0080] Here, in some embodiments, in step 5-1, the current unbalanced load value of the wind turbine and its differential value are calculated based on the blade root bending moment sensor.

[0081] Here, in some embodiments, the modified formula in step 5-3 is:

[0082] Kp =Opt_K p +ΔK p (5)

[0083] K i =Opt_K i +ΔK i (6)

[0084] In the formula, K p K is the proportional gain used in the actual wind turbine pitch controller, and it is a time-varying variable; i Opt_K is the integral coefficient used in the actual wind turbine pitch controller, and it is a time-varying variable. p Opt_K i These are the optimal proportional coefficient and integral coefficient after offline optimization in step 3, respectively, and are constants; ΔK p ΔK i These are the correction amounts for the proportional and integral coefficients of the PI controller output by the fuzzy logic controller, respectively.

[0085] Here, the offline-optimized-online self-correcting PI controller obtained above can output independent pitch command values ​​based on the current unbalanced load M of the wind turbine, so as to reduce the unbalanced load of the wind turbine.

[0086] Furthermore, in one embodiment, in step 6, the PI controller outputs the coordinate transformation angular and direct-axis pitch command values ​​β. d and β q After coordinate transformation as shown in the following formula, the pitch command values ​​of the three blades are obtained.

[0087]

[0088] In the formula, β d and β q β1, β2, and β3 are the pitch command values ​​for the direct and quadrature axes, respectively, while β1, β2, and β3 are the pitch command values ​​for the three blades of the horizontal axis wind turbine.

[0089] Tables 1 and 2 below are the fuzzy rule tables for the fuzzy logic controller.

[0090] Table 1. Fuzzy rule table for proportional coefficient correction in online self-correcting control based on fuzzy logic control.

[0091]

[0092] Table 2. Fuzzy rule table for integral coefficient correction in online self-correcting control based on fuzzy logic control.

[0093]

[0094]

[0095] Where NB represents positive large, NM represents positive medium, NS represents positive small, ZR represents zero, PS represents negative small, PM represents negative medium, and PB represents negative large. E is the unbalanced load deviation of the input fuzzy logic controller, and EC is the derivative of the unbalanced load deviation of the input fuzzy logic controller.

[0096] The output of the fuzzy controller is obtained by sequentially processing the input through fuzzification, fuzzy inference, and defuzzification. The fuzzy rule table is used in the fuzzy inference process of the fuzzy controller. The input of the fuzzy controller is the hub overturning moment M. tilt Yaw moment M yaw Its derivative, when using independent pitch control, can be obtained from simulations under various operating conditions, M. tilt M yaw The variation range is approximately ±1000 kN·m, and their differential variation ranges are the same; therefore, we take M. tilt M yaw M t ′ ilt M y ′ aw The basic universe of discourse is [-1000kN·m, 1000kN·m], the fuzzy universe of discourse is set to [-3, 3], the quantization factor is 3 / 1000, and the fuzzy subset is divided into 7 intervals, which equally divide the fuzzy universe of discourse into {NB, NM, NS, ZR, PS, PM, PB}, representing negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, respectively. The output is ΔK. p ΔK i The fuzzy universe of discourse is set as above, which is [-3, 3], and is also divided into 7 intervals. Since ΔK p ΔK i As a control parameter correction, the selection of the proportional factor has a significant impact on the control effect. Existing studies on fuzzy PI control generally rely on empirically setting the proportional factor. To minimize empirical factors and improve control performance, this invention proposes the following approach: First, define the ratio η:

[0097]

[0098] Where: ΔK p,max ΔK i,max Opt_K represents the maximum value of the proportional and integral coefficient corrections for the fuzzy controller output. p Opt_K i The PI parameters are optimized using the particle swarm optimization algorithm; therefore, the output scaling factor of the fuzzy controller can be expressed as η1·Opt_K. p / 3、η2·Opt_K i / 3, at this point, η1 and η2 are considered as unknowns, and the offline optimization method described above is used to optimize η. The optimization result shows that the control effect is optimal when η1 = 0.8 and η2 = 0.4. Therefore, the output scaling factor of the fuzzy controller can be written as 0.8·Opt_K respectively. p / 3、0.4·Opt_K i / 3, meaning the maximum value of the proportional and integral coefficient corrections is 0.8 and 0.4 times the optimal coefficients, respectively. This result indicates that the proportional coefficient can be adjusted online over a wide range, while the integral coefficient is only suitable for fine-tuning. Therefore, the correction amount ΔK of the wind turbine unbalanced load M input to the fuzzy controller and its differential output PI parameters can be used to determine the optimal coefficient. p ΔK i .

[0099] In one embodiment, an independent pitch control system for reducing load on large wind turbine units is provided, the system comprising sequentially executing:

[0100] The first module initializes the particle swarm optimization algorithm for offline optimization calculation of PI parameters, setting the fitness function F and the initial value of the particle swarm for calculating the unbalanced load and speed deviation.

[0101] The second module is used to update the independent variable parameters of the particle swarm optimization algorithm.

[0102] The third module is used to calculate the fitness function F of the particle swarm optimization algorithm;

[0103] The fourth module is used to determine whether the fitness function F and the number of iterations meet the termination condition. If the termination condition is met, the best offline optimization PI parameter, i.e. the optimal PI parameter, is output, and then the fifth module is executed; if the termination condition is not met, the parameters are updated, and the second module is executed.

[0104] The fifth module is used to input the optimal PI parameters into the PI controller;

[0105] The sixth module is used to perform online self-correction of the optimal PI parameters of the PI controller based on fuzzy logic controller.

[0106] The seventh module is used to output independent pitch command values ​​from the modified PI controller to obtain the pitch action requirement.

[0107] The eighth module is used to transmit independent pitch command values ​​to the wind turbine independent pitch actuator to perform independent pitch control.

[0108] Specific limitations regarding the independent pitch control system for reducing load on large wind turbines can be found in the limitations of the independent pitch control method for reducing load on large wind turbines mentioned above, and will not be repeated here. Each module in the aforementioned independent pitch control system for reducing load on large wind turbines can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0109] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements:

[0110] Step 1: Use the particle swarm optimization algorithm to perform offline optimization of the control parameters of the PI controller to obtain the optimal PI parameters;

[0111] Step 2: Input the optimal PI parameters into the PI controller;

[0112] Step 3: Based on the fuzzy logic controller, the optimal PI parameters of the PI controller are self-corrected online using fuzzy logic.

[0113] Step 4: The modified PI controller outputs independent pitch command values ​​to obtain the pitch action requirement.

[0114] Step 5: Transmit the independent pitch command value to the wind turbine independent pitch actuator to execute independent pitch control.

[0115] For specific limitations on each step, please refer to the limitations on the independent pitch control method for reducing load on large wind turbine units mentioned above, which will not be repeated here.

[0116] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program being implemented when executed by a processor:

[0117] Step 1: Use the particle swarm optimization algorithm to perform offline optimization of the control parameters of the PI controller to obtain the optimal PI parameters;

[0118] Step 2: Input the optimal PI parameters into the PI controller;

[0119] Step 3: Based on the fuzzy logic controller, the optimal PI parameters of the PI controller are self-corrected online using fuzzy logic.

[0120] Step 4: The modified PI controller outputs independent pitch command values ​​to obtain the pitch action requirement.

[0121] Step 5: Transmit the independent pitch command value to the wind turbine independent pitch actuator to execute independent pitch control.

[0122] For specific limitations on each step, please refer to the limitations on the independent pitch control method for reducing load on large wind turbine units mentioned above, which will not be repeated here.

[0123] As a specific example, the invention is further illustrated in one embodiment.

[0124] A wind turbine simulation model was built in the FAST-MATLAB / Simulink simulation environment, including a 5MW rated power wind turbine. Specific parameters are shown in Table 3. The model was then configured as follows: Figure 6 The turbulent wind speed shown is used to induce unbalanced loads on the wind turbine.

[0125] Table 3 shows the basic parameters of the wind turbine used in the simulation.

[0126]

[0127] Depend on Figure 7 and Figure 8 It can be seen that the load on the wind turbine generator under the method of this invention is significantly lower than that under other control methods. Combined with... Figure 9 , Figure 10 , Figure 11 and Figure 12 It can be seen that the mean and standard deviation of the unbalanced load acting on the hub are significantly improved. Furthermore, based on offline optimization of the optimal parameters, the online self-correction based on fuzzy logic further reduces the hub unbalanced load, demonstrating strong peak-shaving capability without increasing the out-of-plane bending moment at the blade root, and reducing output power fluctuations to some extent. The control method disclosed in this invention has a strong ability to weaken unbalanced load peaks and performs excellently when the unbalanced load is large; this is the reason for the fuzzy control's online self-correction PI coefficient. Therefore, the control method of this invention can effectively reduce the load on the rotor of large wind turbine units and, to some extent, reduce the fluctuations in the output power of the wind turbine units.

[0128] In summary, compared with traditional PI control, the method proposed in this invention optimizes the PI parameters to adapt to load changes, thereby optimizing the control parameters and effectively compensating for the shortcomings of fixed-parameter control in certain operating conditions, and effectively reducing the load on large wind turbine units.

[0129] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. 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.

Claims

1. An independent pitch control method for reducing load on large wind turbine units, characterized in that, The method includes the following steps: Step 1: Use the particle swarm optimization algorithm to perform offline optimization of the control parameters of the PI controller to obtain the optimal PI parameters; Step 2: Input the optimal PI parameters into the PI controller; Step 3: Based on the fuzzy logic controller, the optimal PI parameters of the PI controller are self-corrected online using fuzzy logic. Step 4: The modified PI controller outputs independent pitch command values ​​to obtain the pitch action requirement. Step 5: Transmit the independent pitch command value to the wind turbine independent pitch actuator to execute independent pitch control; Step 1 specifically includes: Step 1-1: Initialize the particle swarm optimization algorithm for offline optimization calculation of PI parameters, and set the fitness function F and the initial value of the particle swarm for calculating the unbalanced load and speed deviation. Steps 1-2: Calculate the fitness function F; Steps 1-3: Determine whether the fitness function F and the number of iterations meet the termination condition. If the termination condition is met, output the best offline optimization PI parameter, i.e., the optimal PI parameter, and execute step 2. If the termination condition is not met, update the PI parameter and return to step 1-2. In steps 1-2, the fitness function F is: In the formula, The fitness function uses the absolute error integral criterion as the performance index. , These are the absolute values ​​of the overturning and yaw moments acting on the hub, obtained by measuring the blade root bending moment through a torque sensor installed at the root of the wind turbine blades and then performing coordinate transformation. This is the absolute value of the deviation between the actual rotational speed and the rated rotational speed of the wind turbine; , dt represents the weighting coefficients; T represents the integration time; dt represents the integral sign. Step 3 specifically includes: Step 3-1: Obtain the current unbalanced load value M of the wind turbine and calculate its differential value; Step 3-2: Input the wind turbine unbalanced load value M and its derivative value into the fuzzy logic controller; Step 3-3: The fuzzy logic controller determines the magnitude of the correction amount of the PI parameter based on the wind turbine unbalanced load value M and its derivative value, and inputs it into the PI controller to perform online correction of the PI controller parameters. The corrected formula in step 3-3 is as follows: In the formula, The proportional coefficient used in the actual wind turbine pitch controller is a time-varying variable. The integral coefficients used in the actual wind turbine pitch controller are time-varying factors; , These are the optimal proportional coefficient and integral coefficient after offline optimization in step 3, respectively, and are constant values. , These are the correction amounts for the proportional and integral coefficients of the PI controller output by the fuzzy logic controller, respectively.

2. The independent pitch control method for reducing load on large wind turbine units according to claim 1, characterized in that, The , The calculation method is as follows: In the formula, P is the transformation matrix. , , These are time-varying variables that rotate with the wind turbine, representing the out-of-plane bending moments at the root of blades 1, 2, and 3 of a three-bladed horizontal-axis wind turbine, respectively. , The overturning and yaw moments acting on the wheel hub after coordinate transformation are represented by the transformation matrix P as follows: In the formula, The azimuth angle of blade 1 is measured by the blade azimuth sensor.

3. The independent pitch control method for reducing load on large wind turbine units according to claim 1, characterized in that, The PI controller in step 2 is in the following form: In the formula, Represents the transfer function. This is the proportionality coefficient. is the integral coefficient, and s is the differential operator.

4. The independent pitch control method for reducing load on large wind turbine units according to claim 1, characterized in that, In step 3-1, the current unbalanced load value of the wind turbine and its differential value are calculated based on the blade root bending moment sensor.

5. The independent pitch control method for reducing load on large wind turbine units according to claim 2, characterized in that, In step 4, the PI controller outputs the coordinate transformation values ​​for both the quadrature and direct-axis pitch control. and After coordinate transformation as shown in the following formula, the pitch command values ​​of the three blades are obtained respectively. In the formula, and These are the direct-axis and quadrature-axis pitch command values, respectively. , , These are the pitch command values ​​for the three blades of a horizontal axis wind turbine.

6. An independent pitch control system for reducing load on large wind turbine units based on the method of any one of claims 1 to 5, characterized in that, The system includes sequential execution of: The first module is used to perform offline optimization of the control parameters of the PI controller using the particle swarm optimization algorithm to obtain the optimal PI parameters. The second module is used to input the optimal PI parameters into the PI controller; The third module is used to perform online self-correction of the optimal PI parameters of the PI controller based on fuzzy logic controller. The fourth module is used to output independent pitch command values ​​from the modified PI controller to obtain the pitch action requirement. The fifth module is used to transmit independent pitch command values ​​to the wind turbine's independent pitch actuator to perform independent pitch control.

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