Two-blade wind turbine hierarchical fuzzy control method based on independent variable pitch technology of IPC

Abstract

The application discloses a two-blade fan grading fuzzy control method based on an IPC independent variable pitch technology, the method is to optimize PID control by using a fuzzy algorithm, takes power deviation E and power deviation change rate EC of the fan as input, realizes self-adaptive adjustment of PID parameters, and outputs a pitch angle; a power fuzzy controller and a stress-variable pitch fuzzy controller are established, the stress-variable pitch fuzzy controller determines the pitch angle change amount under different load conditions according to the change of the measured load, the load change rate and the fuzzy control rule, and the pitch angle change amount is superposed with the pitch angle output by the PID control as the variable pitch given value of a single blade, meanwhile, the power fuzzy controller is used to stabilize the power output, so that the independent variable pitch action of each variable pitch system is realized, and the output power fluctuation of the fan is reduced; through the variable pitch control of the application, the problem that the fatigue load of the large two-blade wind turbine generator set is too large and the power fluctuation is large can be effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fan independent variable pitch, in particular to a two-blade fan hierarchical fuzzy control method based on IPC independent variable pitch technology. BACKGROUND

[0002] Under the development trend of gradually large-scale unit, the single machine power grade of wind turbine is rising, and the impeller diameter is increasing. Due to the change of wind speed on the rotating plane of the impeller, the blades of the fan will be subjected to asymmetric or periodic load, which will cause fatigue load problem, seriously affecting the service life of the fan. The independent variable pitch technology is an effective means to solve the imbalance of the impeller. The principle of the control technology is to superimpose an independent variable pitch signal on each blade based on unified variable pitch control, so that each blade has different aerodynamic characteristics, compensates the additional load caused by the unevenness of wind speed, improves the reliability of the system and prolongs the service life of the unit, thereby effectively improving the economic benefit of the wind turbine generator unit. The control technology is mainly used to reduce the fatigue load of the fan hub, main shaft and hub-main shaft bolt, and reduce the fatigue load of the blade root and bearing-hub bolt.

[0003] At present, the independent variable pitch technology based on coordinate transformation is widely studied and used because it can feedback control the load. This method mainly applies the method of multi-coordinate transformation to partially decouple the multi-variable coupling control system of large-scale wind turbine generator unit, converts the control state parameters in the rotating coordinate system into fixed coordinate state for calculation, and realizes decoupling control of multiple control loops. However, the PI controller parameters in the multi-closed loop control system are difficult to set, and the nonlinear characteristics of large-scale wind turbine generator unit also affect the control effect. In addition, there is also a linear quadratic Gaussian function for optimal control of power and load, which designs an independent variable pitch controller with robustness and stability. However, due to the strong nonlinearity of the fan and the randomness of the wind speed and direction, the system is more complex, and the accurate model of the actual unit is more difficult to establish. Therefore, it is necessary to study the load control strategy of large-scale unit independent variable pitch to improve the reliability of the system and prolong the service life of large-scale wind turbine generator unit, thereby effectively improving the economic benefit of large-scale wind turbine generator unit. SUMMARY

[0004] The purpose of the present application is to solve the problems in the prior art and provide a two-blade fan hierarchical fuzzy control method based on IPC independent variable pitch technology. A fuzzy controller with power feedback is proposed to output the corresponding pitch angle increment. On the basis of unified variable pitch control, an independent variable pitch increment signal is superimposed on each blade, so that each blade has different aerodynamic characteristics, compensates the additional load caused by the unevenness of wind speed, and realizes the independent variable pitch action of each variable pitch system, reduces the output power fluctuation of the unit, improves the aerodynamic characteristics of the blades, and further improves the fatigue load of the unit.

[0005] The objective of this invention is achieved through the following technical solution: a hierarchical fuzzy control method for two-bladed wind turbines based on IPC independent pitch control technology. This method utilizes a fuzzy algorithm to optimize PID control, taking the wind turbine's power deviation E and power deviation change rate EC as inputs to realize the PID parameter k. p k i k d The adaptive adjustment outputs the pitch angle; a power fuzzy controller and a force-pitch fuzzy controller are established. The force-pitch fuzzy controller determines the pitch angle change under different load conditions based on the measured load change and load change rate and fuzzy control rules. This change is superimposed with the pitch angle output by the PID control as the pitch setpoint for a single blade. At the same time, the power fuzzy controller is used to stabilize the power output, thereby realizing the independent pitch action of each pitch system and reducing the output power fluctuation of the wind turbine.

[0006] Furthermore, the fuzzy algorithm includes:

[0007] There are seven fuzzy linguistic values ​​for the deviation E between the actual power value and the given power value of the wind turbine: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, abbreviated as {NB, NM, NS, Z, PS, PM, PB}, and the universe of discourse of the corresponding fuzzy subset is {-3, -2, -1, 0, 1, 2, 3}; there are also seven fuzzy linguistic values ​​for the deviation change rate EC: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, abbreviated as {NB, NM, NS, Z, PS, PM, PB}, and the universe of discourse of the corresponding fuzzy subset is {-6, -4, -2, 0, 2, 4, 6}.

[0008] Table 1 shows k p Fuzzy control rule table:

[0009]

[0010] Table 2 shows k i Fuzzy control rule table:

[0011]

[0012] Table 3 shows k d Fuzzy control rule table:

[0013]

[0014] The output variable k is selected based on the control objective of the fuzzy PID parameter adaptive controller. p k i k d .

[0015] Further, the fuzzy control rules include:

[0016] The fuzzy language values of the force F inputted by the force-pitch fuzzy controller are defined as five: {very high, high, high, higher, slightly high}, abbreviated as {EH, VH, H, RH, LH}, and the corresponding domain of the fuzzy subsets is {1, 2, 3, 4, 5}; the fuzzy language values of the force deviation ΔF are defined as seven: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, abbreviated as {NB, NM, NS, Z, PS, PM, PB}, and the corresponding domain of the fuzzy subsets is {-3, -2, -1, 0, 1, 2, 3};

[0017] The fuzzy language values of the output of the force-pitch fuzzy controller, the pitch angle increment Δβ, are defined as seven: {negative large 2, negative large 1, negative medium 2, negative medium 1, negative small 2, negative small 1, zero, positive small 1, positive small 2, positive medium 1, positive medium 2, positive large 1, positive large 2}, abbreviated as {NB2, NB1, NM2, NM1, NS2, NS1, Z, PS1, PS2, PM1, PM2, PB1, PB2}, and the corresponding domain U of the fuzzy subsets is U = {-6, -5, -4, -3, -2, -1, 0, +1, +2, +3, +4, +5, +6};

[0018] Table 4 is the fuzzy control rule table of the pitch angle increment Δβ:

[0019]

[0020] The corresponding pitch angle increment Δβ is outputted through the above fuzzy control rules.

[0021] Further, the establishment of the power fuzzy controller and the force-pitch fuzzy controller includes the following steps:

[0022] The control targets of the controlled system are determined: the force F of the blade 1 a1 , the force F of the blade 2 a2 , and the output power P of the unit;

[0023] According to the control targets, three target fuzzy controllers are designed: one power fuzzy controller to reduce the output fluctuation of the fan, and two force-pitch fuzzy controllers to reduce the flapping vibration of the two blades, according to the azimuth angle θ i of each blade, a dynamic coefficient k i is set, i = (1, 2), and the pitch angle β r of the two blades is unified and corrected to the independent pitch angle β ri of the single blade:

[0024] β ri = k i × β r ;

[0025] wherein the dynamic coefficient is

[0026]

[0027] wherein, θ i is the azimuth angle of the i-th blade, i=(1, 2), R is the impeller radius, H0 is the hub height, three target fuzzy controllers have different effects in the fan control system, a dynamic coefficient k1, k2 and k3 is defined for each of the three sub-target controllers, and the control quantity of the system is obtained through the dynamic coefficient:

[0028]

[0029] k1 and k2 are respectively the dynamic coefficients of the force fuzzy controller of the i-th blade, and k3 represents the dynamic coefficient of the power fuzzy controller; when the force of the blade changes greatly, the control effect of the force-pitch fuzzy controller needs to be increased, and the error value of the force is taken as the dynamic coefficient determination standard of the force-pitch fuzzy controller, that is, the dynamic coefficients k1 and k2 are adjusted, when the output power of the fan has reached the rated power, the input of the power fuzzy controller is small at this time, the force control of the fan is much greater than the power control, which is not conducive to the stable power output of the fan, at this time, the absolute value of the power error of the fan is taken as the standard to adjust the dynamic coefficient k3 of the power fuzzy controller, and the dynamic classification coefficient is determined according to the variables in the system and is generated by the hierarchical fuzzy controller, and the dynamic coefficient is decomposed into:

[0030]

[0031] wherein, is a part for realizing basic functions, for ensuring the stability of the system and realizing the basic target, B i is a hierarchical adjustment range, Δk i is an actual fuzzy reasoning adjustment part, Δk i ∈[0.0,1.0], which is used for real-time adjustment of each target fuzzy controller according to requirements, realizing the optimal control of multiple targets and obtaining the preset control effect.

[0032] The two-blade fan hierarchical fuzzy control system based on the IPC independent pitch technology provided by the application is used for realizing the two-blade fan hierarchical fuzzy control method based on the IPC independent pitch technology, and comprises the following steps:

[0033] The fuzzy PID parameter adaptive controller is used for realizing the adaptive adjustment of the adjustment parameters k p , k i and k d of the pitch system PID controller through fuzzy reasoning rules.

[0034] The unit's pitch control system uses a PID controller to output a pitch angle suitable for the current wind conditions.

[0035] The force-pitch fuzzy controller is used to output the corresponding pitch angle increment Δβ according to fuzzy control rules;

[0036] The pitch angle superposition module is used to superimpose the pitch angle increment Δβ with the pitch angle and use it as the pitch setpoint of the unit's pitch system.

[0037] The present invention provides a non-transitory computer-readable medium storing instructions that, when executed by a processor, perform the steps of the graded fuzzy control method for a two-bladed wind turbine based on IPC independent pitch technology described above.

[0038] The present invention provides a computing device including a processor and a memory for storing processor-executable programs. When the processor executes the program stored in the memory, it implements the above-mentioned hierarchical fuzzy control method for two-bladed wind turbines based on IPC independent pitch technology.

[0039] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0040] The force-pitch fuzzy controller proposed in this invention determines the pitch angle change under different load conditions based on the change of the measured load and the load change rate through fuzzy control rules. This change is then superimposed with the pitch angle output by the PID control as the pitch setpoint for a single blade. In addition, a power fuzzy controller is used to improve the control effect, making the power output more stable and smooth. Through the pitch control of this invention, the problems of excessive fatigue load and power fluctuation in large two-bladed wind turbines can be effectively improved. Attached Figure Description

[0041] Figure 1 This is a control logic block diagram of the present invention.

[0042] Figure 2 This is a schematic diagram of the fuzzy control principle.

[0043] Figure 3 This is a schematic diagram of a two-dimensional fuzzy controller.

[0044] Figure 4 This is a diagram showing the input-output correspondence of a fuzzy rule controller.

[0045] Figure 5 This is a diagram showing the input-output surface relationship of a fuzzy controller.

[0046] Figure 6 For parameter k p The control surface relationship diagram.

[0047] Figure 7 For parameter ki The control surface relationship diagram.

[0048] Figure 8 For parameter k d The control surface relationship diagram.

[0049] Figure 9 This is a simulation structure diagram of a fuzzy controller.

[0050] Figure 10 The simulation structure diagram for integrating the force-pitch fuzzy controller and the fuzzy PID parameter adaptive controller into the wind turbine simulation model. Detailed Implementation

[0051] The present invention will be further described below with reference to specific embodiments.

[0052] Example 1

[0053] See Figures 1 to 2 As shown in this embodiment, a hierarchical fuzzy control method for a two-bladed wind turbine based on IPC independent pitch technology is provided. This method utilizes a fuzzy algorithm to optimize PID control, taking the wind turbine's power deviation E and power deviation change rate EC as inputs to realize the PID parameter k. p k i k d The adaptive adjustment outputs the pitch angle. A power fuzzy controller and a force-pitch fuzzy controller are established. The force-pitch fuzzy controller determines the pitch angle change under different load conditions based on the measured load changes, load change rate, and fuzzy control rules. This change is then superimposed with the pitch angle output from the PID control as the pitch setpoint for a single blade. Simultaneously, the power fuzzy controller stabilizes the power output, thereby achieving independent pitch control of each pitch system and reducing power output fluctuations in the wind turbine. (See also...) Figure 3 As shown, this embodiment selects a two-dimensional fuzzy controller.

[0054] In this embodiment, the applied unit is a 1MW wind turbine with an inlet wind speed of 3.5m / s and an outlet wind speed of 25m / s, and a generator rated speed of 1680rpm. The error E and the rate of change of error EC are used as input variables, and the three parameters k of the PID controller are... p k i k d As the output variable, fuzzy control rules are used to modify the PID parameters online to meet the self-tuning requirements of E and EC for PID parameters at different times.

[0055] The fuzzy algorithm includes:

[0056] The basic universe of discourse for the power deviation E of the generator set is defined as [-50kW, 50kW], and the basic universe of discourse for its deviation change rate EC is defined as [-400kW / s, 400kW / s]. There are seven fuzzy linguistic values ​​for the deviation E between the actual power value and the power setpoint of the generator set: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, abbreviated as {NB, NM, NS, Z, PS, PM, PB}. The universe of discourse for the corresponding fuzzy subset is {-3, -2, -1, 0, 1, 2, 3}, as shown in Table a below. Similarly, there are seven fuzzy linguistic values ​​for the deviation change rate EC: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, abbreviated as {NB, NM, NS, Z, PS, PM, PB}. The universe of discourse for the corresponding fuzzy subset is {-6, -4, -2, 0, 2, 4, 6}, as shown in Table b below.

[0057]

[0058] Fuzzy processing of the deviation E between the actual power value and the power setpoint of unit a in Table a

[0059]

[0060] Fuzzification of the deviation change rate EC in Table b

[0061] Table 1 shows k p Fuzzy control rule table:

[0062]

[0063] Table 2 shows k i Fuzzy control rule table:

[0064]

[0065] Table 3 shows k d Fuzzy control rule table:

[0066]

[0067] The output variable k is selected based on the control objective of the fuzzy PID parameter adaptive controller. p k i k d The above fuzzy control rules are analyzed as follows:

[0068] 1) When the power deviation E of the wind turbine unit is large, the primary control task for the pitch actuator is to reduce the error between the output power and the set value as quickly as possible. To accelerate the adjustment process of the pitch system, it is necessary to adjust k... p Set it to a large value, and in order to reduce the system overshoot, k needs to be... iis set to 0 to prevent integral saturation; to prevent differential saturation, k d is set to a small value.

[0069] 2) When the power deviation E of the fan unit and the change trend of the deviation change rate EC are opposite, the absolute value of the power error E will continuously decrease. When the power deviation E of the unit is not large, the parameter k p , k i is set to a large value, and the value of k d is adjusted appropriately; when the power deviation EC of the unit is relatively large, the response speed of the system should be improved, at which time the parameter k p is set to a medium-sized value, and the parameter k i is set to a small value, and k d is set to a medium value.

[0070] 3) When the power deviation E of the unit and the deviation change rate EC are of the same sign, the absolute value of the error E will continuously increase. When the power deviation E and the deviation change rate EC are both medium-sized values, to reduce the power deviation as soon as possible, the parameters k i , k d are set to a medium value, and k p is set to a small value; when the power deviation E is small, the system does not need to have a faster response speed, and k p is taken as a medium-sized value, and the parameter k i is set to a large value, and the parameter k d is set to a small value. When the power deviation E is relatively large, to speed up the response process of the system, the parameter k p is set to a large value, and the parameter k i is set to a small value, and the parameter k d is set to a medium value.

[0071] There are 49 control rules for fuzzy PID parameter adjustment, and the control surface is determined according to the above rules, as shown in Figures 5 to 8 .

[0072] The establishment of the power fuzzy controller and the force-pitch fuzzy controller includes the following steps:

[0073] The control targets of the controlled system are determined: the blade 1 force F a1 , the blade 2 force F a2 , and the unit output power P;

[0074] Three target fuzzy controllers are designed according to the control targets: one power fuzzy controller to reduce the output fluctuation of the fan, and two force-pitch fuzzy controllers to reduce the flap vibration of the two blades, according to the azimuth angle θ iDifferent, set the dynamic coefficient k i , i = (1, 2), the two blades are unified to the pitch angle β r , the correction is the single blade independent pitch angle β ri :

[0075] β ri = k i × β r ;

[0076] Where the dynamic coefficient is

[0077]

[0078] Where, θ i is the azimuth angle of the i-th blade, i = (1, 2), R is the radius of the impeller, H0 is the hub height, three target fuzzy controllers have different effects in the fan control system, and a dynamic coefficient k1, k2 and k3 is defined for each of the three sub-target controllers, and the control quantity of the system is obtained through the dynamic coefficient:

[0079]

[0080] k1, k2 are the dynamic coefficients of the force fuzzy controller of the i-th blade, and k3 represents the dynamic coefficient of the power fuzzy controller; when the blade force changes greatly, the control effect of the force-pitch fuzzy controller needs to be increased, and the error value of the force is taken as the dynamic coefficient of the force-pitch fuzzy controller, that is, the dynamic coefficients k1 and k2 are adjusted, when the output power of the fan has reached the rated power, the input of the power fuzzy controller is small at this time, and the force control of the fan is much greater than the power control, which is not conducive to the stable power output of the fan, at this time, the absolute value of the power error of the fan is taken as the standard to adjust the dynamic coefficient k3 of the power fuzzy controller, and the dynamic classification coefficient is determined according to the variables in the system, which is generated by the hierarchical fuzzy controller, and the dynamic coefficient is decomposed into:

[0081]

[0082] In the formula, is part of the basic function, which is used to ensure the stability of the system and the realization of the basic target, B i is the hierarchical adjustment range, Δk i is the actual fuzzy reasoning adjustment part, Δk i ∈ [0.0, 1.0], which is adjusted in real time according to the requirements of each target fuzzy controller to realize the optimal control of multiple targets and obtain the preset control effect.

[0083] A one-dimensional hierarchical fuzzy controller is designed for the power controller and the force controller, and the control rules are shown in the following table:

[0084] Δk1, Δk2 rule table

[0085]

[0086] Δk3 rule table

[0087]

[0088] The output of the controller, i.e. the control variable U of the system, is

[0089]

[0090] In the formula, and B i It can be obtained by experience and trial and error.

[0091] The fuzzy control rule includes:

[0092] The fuzzy language value of the force F input by the force-pitch fuzzy controller is defined as five: {very high, high, high, high, slightly high}, abbreviated as {EH, VH, H, RH, LH}, and the corresponding fuzzy subset domain is {1, 2, 3, 4, 5}; the fuzzy language value of the force deviation ΔF is defined as seven: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, abbreviated as {NB, NM, NS, Z, PS, PM, PB}, and the corresponding fuzzy subset domain is {-3, -2, -1, 0, 1, 2, 3};

[0093] The basic domain of the force-pitch fuzzy controller output is the pitch angle increment Δβ, and the fuzzy language value of the force-pitch fuzzy controller output is seven: {negative large 2, negative large 1, negative medium 2, negative medium 1, negative small 2, negative small 1, zero, positive small 1, positive small 2, positive medium 1, positive medium 2, positive large 1, positive large 2}, abbreviated as {NB2, NB1, NM2, NM1, NS2, NS1, Z, PS1, PS2, PM1, PM2, PB1, PB2}, and the corresponding fuzzy subset domain U is U={-6, -5, -4, -3, -2, -1, 0, +1, +2, +3, +4, +5, +6};

[0094] Table 4 is the fuzzy control rule table of the pitch angle increment Δβ:

[0095]

[0096] The corresponding pitch angle increment Δβ is output through the above fuzzy control rule, Figure 4 The input-output corresponding relationship diagram of the fuzzy rule controller can be seen from the figure.

[0097] According to the design of the above control rule, a force-pitch fuzzy controller based on force and a parameter adaptive fuzzy PID control module based on power are respectively established, the force-pitch fuzzy controller based on force is described with reference to Figure 9 ; finally, the above two controllers are packaged and connected to the wind turbine simulation model, as shown in Figure 10 .

[0098] Embodiment 2

[0099] The two-blade wind turbine hierarchical fuzzy control system based on the IPC independent pitch technology provided in the embodiment is used to implement the two-blade wind turbine hierarchical fuzzy control method based on the IPC independent pitch technology, and includes the following.

[0100] The fuzzy PID parameter adaptive controller is used to realize adaptive adjustment of the adjustment parameters k p , k i and k d of the wind turbine pitch system PID controller through fuzzy inference rules.

[0101] The wind turbine pitch system PID controller is used to output a pitch angle suitable for the current wind condition.

[0102] The force-pitch fuzzy controller is used to output a corresponding pitch angle increment Δβ through fuzzy control rules.

[0103] The pitch angle superposition module is used to superimpose the pitch angle increment Δβ and the pitch angle to serve as a pitch given value of the wind turbine pitch system.

[0104] Embodiment 3

[0105] The embodiment discloses a non-transitory computer readable medium storing instructions, when the instructions are executed by a processor, the steps of the two-blade wind turbine hierarchical fuzzy control method based on the IPC independent pitch technology according to embodiment 1 are executed.

[0106] The non-transitory computer readable medium in the embodiment can be a disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), a U disk, a mobile hard disk, and the like.

[0107] Embodiment 4

[0108] The embodiment discloses a computing device including a processor and a memory for storing a program executable by the processor, when the processor executes the program stored in the memory, the two-blade wind turbine hierarchical fuzzy control method based on the IPC independent pitch technology according to embodiment 1 is implemented.

[0109] The computing device described in this embodiment can be a desktop computer, a notebook computer, a smart phone, a PDA handheld terminal, a tablet computer, a programmable logic controller (PLC), or other terminal device with processor function.

[0110] The above-described embodiments are only preferred embodiments of the present application, and are not intended to limit the scope of the present application. Any changes made in the shape or principle of the present application should be covered within the scope of the present application.

Claims

1. A hierarchical fuzzy control method for a two-bladed wind turbine based on IPC independent pitch control technology, characterized in that: This method utilizes a fuzzy algorithm to optimize PID control, taking the power deviation E and the rate of change of power deviation EC of the fan as inputs to realize the PID parameter k. p k i k d The adaptive adjustment outputs the pitch angle; a power fuzzy controller and a force-pitch fuzzy controller are established. The force-pitch fuzzy controller determines the pitch angle change under different load conditions based on the change of the measured load and the load change rate and fuzzy control rules. This change is superimposed with the pitch angle output by the PID control as the pitch setpoint for a single blade. At the same time, the power fuzzy controller is used to stabilize the power output, thereby realizing the independent pitch action of each pitch system and reducing the output power fluctuation of the wind turbine. The steps to establish the power fuzzy controller and the force-pitch fuzzy controller are as follows: Determine the control objective of the controlled system: Blade 1 is subjected to force F a1 Blade 2 is subjected to force F a2 and the unit's output power P; Three target fuzzy controllers were designed based on the control objectives: one power fuzzy controller to reduce turbine output fluctuations, and two force-pitch fuzzy controllers to reduce the flapping vibration of the two blades, based on the azimuth angle θ of each blade. i Different, setting the dynamic coefficient k i i = (1, 2), the two blades are adjusted to the same pitch angle β. r Corrected to independent pitch angle β for each blade. ri : β ri ＝k i ×β r ； Where the dynamic coefficient is Where, θ i Let be the azimuth angle of the i-th blade, i = (1, 2), R be the impeller radius, and H0 be the hub height. The three target fuzzy controllers play different roles in the wind turbine control system. Dynamic coefficients k1, k2, and k3 are defined for each of the three target controllers. The control quantity of the system is obtained through these dynamic coefficients: k1 and k2 are the dynamic coefficients of the force fuzzy controller for the i-th blade, and k3 represents the dynamic coefficient of the power fuzzy controller. When the blade force changes significantly, it is necessary to enhance the control effect of the force-pitch fuzzy controller. The force error value is used as the criterion for judging the dynamic coefficients of the force-pitch fuzzy controller, i.e., adjusting the dynamic coefficients k1 and k2. When the wind turbine's output power has reached its rated power, the input of the power fuzzy controller becomes smaller, and the force control of the wind turbine is much greater than the power control, which is not conducive to the stability of the wind turbine's power output. At this time, the absolute value of the wind turbine's power error needs to be used as the standard to adjust the dynamic coefficient k3 of the power fuzzy controller. The dynamic hierarchical coefficients are determined based on the variables in the system and are generated by inference from the hierarchical fuzzy controller. The dynamic coefficients are decomposed into: In the formula, To implement the basic functions, and to ensure the stability of the system and the achievement of its basic objectives, B i For the graded adjustment range, Δk i For the actual fuzzy inference adjustment part, Δk i ∈[0.0,1.0], adjust the fuzzy controllers of each target in real time according to the requirements to achieve optimized control of multiple targets and obtain the preset control effect.

2. The hierarchical fuzzy control method for a two-bladed wind turbine based on IPC independent pitch technology according to claim 1, characterized in that, The fuzzy algorithm includes: There are seven fuzzy linguistic values ​​for the deviation E between the actual power value and the given power value of the wind turbine: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, abbreviated as {NB, NM, NS, Z, PS, PM, PB}, and the universe of discourse of the corresponding fuzzy subset is {-3, -2, -1, 0, 1, 2, 3}; there are also seven fuzzy linguistic values ​​for the deviation change rate EC: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, abbreviated as {NB, NM, NS, Z, PS, PM, PB}, and the universe of discourse of the corresponding fuzzy subset is {-6, -4, -2, 0, 2, 4, 6}. Table 1 shows k p Fuzzy control rule table: Table 2 shows k i Fuzzy control rule table: Table 3 shows k d Fuzzy control rule table: The output variable k is selected based on the control objective of the fuzzy PID parameter adaptive controller. p k i k d .

3. The hierarchical fuzzy control method for a two-bladed wind turbine based on IPC independent pitch technology according to claim 1, characterized in that, The fuzzy control rules include: Five fuzzy linguistic values ​​are defined for the force F input to the force-pitch fuzzy controller: {very high, very high, high, relatively high, slightly high}, abbreviated as {EH, VH, H, RH, LH}, and the universe of discourse of the corresponding fuzzy subset is {1, 2, 3, 4, 5}; seven fuzzy linguistic values ​​are defined for the force deviation ΔF: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, abbreviated as {NB, NM, NS, Z, PS, PM, PB}, and the universe of discourse of the corresponding fuzzy subset is {-3, -2, -1, 0, 1, 2, 3}. There are 7 fuzzy linguistic values ​​for the output of the force-pitch fuzzy controller as the pitch angle increment Δβ: {negative large 2, negative large 1, negative medium 2, negative medium 1, negative small 2, negative small 1, zero, positive small 1, positive small 2, positive medium 1, positive medium 2, positive large 1, positive large 2}, abbreviated as {NB2, NB1, NM2, NM1, NS2, NS1, Z, PS1, PS2, PM1, PM2, PB1, PB2}, and the universe of discourse U of the corresponding fuzzy subset is U = {-6, -5, -4, -3, -2, -1, 0, +1, +2, +3, +4, +5, +6}; Table 4 shows the fuzzy control rules for the pitch angle increment Δβ: The corresponding pitch angle increment Δβ is output through the above fuzzy control rules.

4. A hierarchical fuzzy control system for a two-bladed wind turbine based on IPC independent pitch technology, characterized in that, The hierarchical fuzzy control method for a two-bladed wind turbine based on IPC independent pitch technology as described in any one of claims 1-3 includes: A fuzzy PID parameter adaptive controller is used to achieve the adjustment parameter k of the PID controller in the unit's pitch system through fuzzy inference rules. p k i k d Adaptive adjustment; The unit's pitch control system uses a PID controller to output a pitch angle suitable for the current wind conditions. The force-pitch fuzzy controller is used to output the corresponding pitch angle increment Δβ according to fuzzy control rules; The pitch angle superposition module is used to superimpose the pitch angle increment Δβ with the pitch angle and use it as the pitch setpoint of the unit's pitch system.

5. A non-transitory computer-readable medium storing instructions, characterized in that, When the instruction is executed by the processor, the steps of the hierarchical fuzzy control method for two-bladed wind turbines based on IPC independent pitch technology according to any one of claims 1 to 3 are performed.

6. A computing device, comprising a processor and a memory for storing a processor-executable program, characterized in that, When the processor executes the program stored in the memory, it implements the hierarchical fuzzy control method for two-bladed wind turbines based on IPC independent pitch technology as described in any one of claims 1 to 3.

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