Variable pitch control method for permanent magnet direct-drive wind power system of floating offshore wind turbine

Abstract

The invention discloses a variable pitch control method for a permanent magnet direct-drive wind power system of a floating offshore wind turbine, comprising the following steps: S1, obtaining the output power of the generator set; S2, performing a difference operation between the output power of the generator set and the rated power to obtain the power deviation of the generator set, and calculating the power deviation rate of the generator set within a period of time; S3, designing a dual-input, three-output fuzzy PID control model, inputting the power deviation and the power deviation rate obtained in step S2 into the fuzzy PID control model, the fuzzy PID control model making corresponding decisions through neural fuzzy reasoning, and adjusting PID parameters online to meet the self-tuning requirements of the power deviation and the power deviation rate at different times on the PID parameters, thereby obtaining the best adjustment of the pitch angle change target; S4, repeated compensation control, embedding repeated control based on the internal model principle into the fuzzy PID control, and forming a fuzzy PID control based on repeated compensation.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power generation, and in particular relates to a variable pitch control method for a permanent magnet direct-drive wind power system of a floating offshore wind turbine unit. Background Art

[0002] With the exhaustion of traditional fossil energy and the intensification of environmental pollution and global warming, energy issues have become a severe challenge facing the economic and social development of countries around the world. Wind energy is a renewable energy source with large reserves, mature technical conditions and low development costs. Distributed power generation represented by wind power has become the focus of attention of governments, industries and academia around the world as a clean and environmentally friendly flexible power generation method. The rapid increase in demand for wind power generation has put forward higher requirements for the unit capacity of wind turbines, which has also prompted the rapid increase in the structural size of the wind turbines themselves. The most obvious feature is that the tower height is getting higher and higher and the wind rotor diameter is getting larger and larger. The loads borne by wind turbines during operation are mostly loads that vary with time and space, and most of their components are high-cycle fatigue components. Therefore, they must not only meet the requirements of ultimate strength, but also fatigue strength. At the wind farm level, how to reduce the maximum utilization of wind energy through reasonable control of wind turbines and reduce the cost of wind power operation and maintenance has become an issue of increasing concern to the wind power industry.

[0003] Modern wind power generation emerged in the 1970s. After years of development, from the initial fixed pitch to the current variable pitch, from constant speed and constant frequency to today's variable speed and constant frequency, wind power generation technology has become relatively mature, basically achieving the ultimate goal of wind turbines from being able to supply power to the grid to ideally supply power to the grid.

[0004] In recent years, variable pitch units have gradually become the mainstream model of wind power generation. In order to ensure that large and medium-sized wind turbines can operate safely above the rated wind speed and stably output the rated power, variable pitch control is usually adopted, that is, by changing the pitch angle of the wind rotor blades to change the wind energy utilization coefficient of the wind turbine, so that the output power of the unit remains stable.

[0005] Due to the special working environment of floating offshore wind turbines, there is often a deviation between the actual output power and the rated power. When the wind speed is above the rated wind speed, the control goal of the permanent magnet direct-drive wind power system of the floating offshore wind turbine is to achieve constant power operation. In order to better realize the power regulation of the wind power system, variable pitch control is usually adopted.

[0006] Most electric variable pitch wind turbines use variable gain PI or PID variable pitch control methods, which adjust the blade pitch angle by measuring the deviation, change the angle of attack of the airflow on the blade, and thus change the aerodynamic torque obtained by the wind wheel to keep the unit output power stable. There is a high-order strongly coupled nonlinear relationship between the wind energy utilization coefficient and the input pitch angle of the wind turbine. The variable pitch actuator of the wind turbine is a large inertia system with serious lag, and the working environment of the wind turbine is usually harsh and subject to serious external interference. Therefore, there are uncertainties in the parameters of the wind turbine model and the variable pitch actuator. The control accuracy of the traditional PID control is heavily dependent on the accurate modeling of the controlled object. When the model parameters are uncertain and there is serious external interference, the control accuracy is difficult to guarantee.

[0007] The variable speed and variable pitch wind turbine is a nonlinear, multivariable, time-varying complex system, so it is difficult to establish an accurate mathematical model. For the variable pitch system of the wind turbine, the traditional PID feedback control method has the problem of feedback signal correction action lag, that is, the controller will only start to adjust when the actual power of the unit deviates from the power setting value. Due to the inertia and delay of the variable pitch system, the dynamic adjustment time of the control process is long and the overshoot is large. In addition, the wind turbine will inevitably cause load impact on the wind turbine during the pitch change process. For the variable pitch control of large variable speed and variable pitch wind turbines, how to reduce the load on the key parts of the wind turbine while improving the power quality has become the main problem.

[0008] Therefore, it is of great theoretical and practical significance to design a variable pitch control method for a permanent magnet direct-drive wind power system of a floating offshore wind turbine to solve the problems existing in the existing technology. Summary of the invention

[0009] The purpose of the present invention is to provide a variable pitch control method for a permanent magnet direct-drive wind power system of a floating offshore wind turbine in view of the problems existing in the prior art.

[0010] To achieve the above object, the technical solution adopted by the present invention is:

[0011] A variable pitch control method for a permanent magnet direct-drive wind power system of a floating offshore wind turbine generator system comprises the following steps:

[0012] S1, obtain the output power of the generator set;

[0013] S2, performing a difference operation between the output power of the generator set and the rated power to obtain the power deviation e of the generator set, and calculating the power deviation rate ec of the generator set within the time Δt;

[0014] S3, design a dual-input, three-output fuzzy PID control model, input the power deviation e and power deviation rate ec obtained in step S2 into the fuzzy PID control model, and the fuzzy PID control model makes corresponding decisions through neuro-fuzzy reasoning and adjusts the PID parameter K online p , K i , K d , to meet the self-tuning requirements of the power deviation e and the power deviation rate ec at different times on the PID parameters, so as to obtain the best adjustment of the pitch angle change target;

[0015] S4, repeated compensation control, embeds the repeated control based on the internal model principle into the fuzzy PID control to form a fuzzy PID control based on repeated compensation.

[0016] Specifically, in step S3, the fuzzy PID control model includes a TS fuzzy controller and a PID controller; the TS fuzzy controller adjusts the PID parameter K according to the input power deviation e and power deviation rate ec. p , K i , K d Adjustment is carried out, and then the correction values ​​of the three parameters are output through fuzzification, fuzzy reasoning and function clarification; the correction values ​​of the three parameters are then input into the PID controller to perform real-time tuning of the three parameters.

[0017] Furthermore, the rule of the TS fuzzy controller is expressed as:

[0018] if e is A i1 ,ec is A i2 , then ui=f(e,ec)=c i0 +c i1 e+c i2 ec

[0019] Among them, A i1 , A i2 are the two fuzzy sets in the i-th rule; c i0 、c i1 and c i2 is a constant coefficient, which is determined by identification based on measured data and is used to reflect the inherent properties of the system;

[0020] Assume that the system has n rules. When a certain input triggers l of them, l<n, the total output of the system is:

[0021]

[0022] Among them, w i The weight of each rule is:

[0023] w i =Ri A i1 (e)A i2 (ec)

[0024] R i is the weight factor.

[0025] Furthermore, the formula for adjusting the correction amount of the three parameters of the PID controller is as follows:

[0026] K p =K p0 +k′ p

[0027] K i =K i0 +k′ i

[0028] K d =K d0 +k′ d

[0029] Among them, K p0 , K i0 , K d0 is the initial PID parameter; k′ p , k′ i , k′ d is the correction value of PID parameters.

[0030] Specifically, in step S4, in the repetitive control based on the internal model principle, in addition to the error signal at the current moment added to the controlled object, the error signal at the previous moment is also superimposed to form a positive feedback with a time lag link, and a second-order low-pass filter is connected in series in the time lag link to suppress the gain of the repetitive control in the high frequency band.

[0031] Further, the second-order low-pass filter is expressed as:

[0032]

[0033] Where ξ is the damping ratio of the low-pass filter, ω c is the cutoff frequency of the low-pass filter.

[0034] Furthermore, the control signal obtained by the repetitive controller is outputted after a certain delay time, and a repetitive compensation controller is provided to repeat PID on the output of the second-order low-pass filter within the delay time before outputting it, and to perform compensation before outputting it again.

[0035] Further, the repetitive compensation controller is expressed as:

[0036]

[0037] Where K is the repetitive controller gain and 1 / (Ts+1) is the filter;

[0038] The repetitive controller gain K is used to provide amplitude compensation for the control object. The value of K will affect the stability range, convergence speed and steady-state error of the system. The smaller the K value, the more stable the system but the larger the stability range. The larger the K value, the faster the system converges.

[0039] The filter is used to attenuate high-frequency signals, enhance the anti-interference ability of the system, and improve the stability of the system.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows: the repetitive control based on the internal model principle of the present invention reduces the error by compensating the control error, thereby reducing the steady-state error of the control system and suppressing the disturbance of the load. In addition to the error signal at the current moment added to the controlled object, the repetitive control system also superimposes the error signal at the previous moment. Since the repetitive control is composed of positive feedback with a time-delay link, the open-loop transfer function of the system contains countless poles on the imaginary axis, so the system error for any input signal and interference signal tends to zero, and the robustness is strong. A low-pass filter is connected in series in the time-delay link, and at the same time, the gain of the repetitive control in the high-frequency band is reduced, thereby ensuring the stability of the system. Even if there is interference inside the system, during the delay period required to eliminate the influence of the interference on the output signal, the repetitive control based on the internal model principle is embedded in the fuzzy reasoning self-adjusting PID closed-loop control based on the neural network to form a repetitive compensation neural fuzzy PID control, so that the system is still in a closed-loop state. During the delay period after the interference occurs, the fuzzy PID performs real-time control and adjustment of the output error. When the system is in a stable state, the repetitive compensation controller can be adjusted so that the output signal in the steady state can track the input signal well; when there is a large interference, the fuzzy PID can adjust the input signal to reduce the signal error and improve the tracking accuracy of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flow chart of a variable pitch control method of a permanent magnet direct-drive wind power system of a floating offshore wind turbine unit according to the present invention.

[0042] Figure 2 A schematic block diagram of a basic repetitive controller.

[0043] Figure 3 It is a schematic block diagram of a repetitive controller after adding a feedforward term in an embodiment of the present invention.

[0044] Figure 4 1 is a schematic block diagram of the structure of a TS fuzzy PID controller in an embodiment of the present invention.

[0045] Figure 5Schematic diagram of the membership function of the power deviation e and the power deviation rate ec in an embodiment of the present invention.

[0046] Figure 6 k in the embodiment of the present invention p ′, k i ′ and k d ′ Schematic diagram of the three-dimensional image of the simulation output.

[0047] Figure 7 This is a schematic diagram of the measured wind speed at Shanghai Yangshan Port from May to November 2019 in an embodiment of the present invention.

[0048] Figure 8 Schematic diagram of step wind speed simulation in an embodiment of the present invention.

[0049] Fig. 9 This is a comparison diagram of the tip speed ratio after simulation using PID pitch control, TS fuzzy PID control and repeated TS fuzzy PID control methods under step wind speed in an embodiment of the present invention.

[0050] Fig.10 This is a comparison diagram of wind energy utilization coefficients after simulation using PID variable pitch control, TS fuzzy PID control, and repeated TS fuzzy PID control methods under step wind speed in an embodiment of the present invention.

[0051] Fig.11 It is a comparison diagram of pitch angles after simulation using PID variable pitch control, TS fuzzy PID control and repeated TS fuzzy PID control methods under step wind speed in an embodiment of the present invention.

[0052] Fig.12 This is a comparison diagram of the generator speed after simulation using PID pitch control, TS fuzzy PID control and repeated TS fuzzy PID control methods under step wind speed in an embodiment of the present invention.

[0053] Fig.13 This is a comparison diagram of wind turbine output power after simulation using PID variable pitch control, TS fuzzy PID control and repeated TS fuzzy PID control methods under step wind speed in an embodiment of the present invention.

[0054] Fig.14 Schematic diagram of random wind speed simulation in an embodiment of the present invention.

[0055] Fig.15 This is a comparison diagram of the tip speed ratio after simulation using the traditional PID variable pitch control and the repeated TS fuzzy PID control method under random wind speed in an embodiment of the present invention.

[0056] Fig.16This is a comparison chart of wind energy utilization coefficients after simulation using traditional PID variable pitch control and repeated TS fuzzy PID control methods under random wind speeds in an embodiment of the present invention.

[0057] Fig.17 This is a comparison diagram of pitch angles after simulation using the traditional PID variable pitch control and the repeated TS fuzzy PID control method under random wind speed in an embodiment of the present invention.

[0058] Fig.18 This is a comparison diagram of the generator speed after simulation using the traditional PID variable pitch control and the repeated TS fuzzy PID control method under random wind speed in an embodiment of the present invention.

[0059] Fig.19 This is a comparison diagram of wind turbine output power after simulation using traditional PID variable pitch control and repeated TS fuzzy PID control methods under random wind speeds in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. 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 creative work are within the scope of protection of the present invention.

[0061] like Figure 1 As shown, this embodiment provides a variable pitch control method for a permanent magnet direct-drive wind power system of a floating offshore wind turbine, comprising the following steps:

[0062] S1, obtain the output power of the generator set;

[0063] S2, performing a difference operation between the output power of the generator set and the rated power to obtain the power deviation e of the generator set, and calculating the power deviation rate ec of the generator set within the time Δt;

[0064] S3, design a dual-input, three-output fuzzy PID control model, input the power deviation e and power deviation rate ec obtained in step S2 into the fuzzy PID control model, and the fuzzy PID control model makes corresponding decisions through neuro-fuzzy reasoning and adjusts the PID parameter K online p , K i , K d , to meet the self-tuning requirements of the power deviation e and the power deviation rate ec at different times on the PID parameters, so as to obtain the best adjustment of the pitch angle change target;

[0065] S4, repeated compensation control, embeds the repeated control based on the internal model principle into the fuzzy PID control to form a fuzzy PID control based on repeated compensation.

[0066] Specifically, in step S3, the fuzzy PID control model includes a TS fuzzy controller and a PID controller; the TS fuzzy controller adjusts the PID parameter K according to the input power deviation e and power deviation rate ec. p , K i , K d Adjustment is carried out, and then the correction values ​​of the three parameters are output through fuzzification, fuzzy reasoning and function clarification; the correction values ​​of the three parameters are then input into the PID controller to perform real-time tuning of the three parameters.

[0067] TS fuzzy PID control is a composite control method that combines Takagi-Sugeno (TS) fuzzy logic control and traditional PID control. The structure of the controller is as follows: Figure 4 As shown;

[0068] The main idea of ​​TS fuzzy model is to convert complex nonlinear problems into linear problems in many different small segments. Compared with Mamdani type fuzzy inference system, TS fuzzy inference system is simpler to calculate, convenient for mathematical analysis and description, and conducive to combining with conventional PID control, adaptive control and other methods, and has the advantage of adaptive control ability.

[0069] The typical fuzzy conditional statements of TS fuzzy control are:

[0070] if x1 is A1, ..., x n is A n , then u=f(x1,...,x n )

[0071] It can be seen that the control rules are not completely described by language, and the inference conclusion is not a fuzzy set, but f(x1,...,x n ) value. Where x1 is the output power error e, x2 is the error change rate ec; then the i-th control rule can be written as:

[0072] if e is A i1 ,ec is A i2 , then ui=f(e,ec)=c i0 +c i1 e+c i2 ec

[0073] Among them, A i1 , A i2 are the two fuzzy sets in the i-th rule; c i0、c i1 and c i2 is a constant coefficient, which is determined by identification based on measured data and is used to reflect the inherent properties of the system;

[0074] Assume that the system has n rules. When a certain input triggers l of them, l<n, the total output of the system is:

[0075]

[0076] Among them, w i The weight of each rule is:

[0077] w i =R i A i1 (e)A i2 (ec)

[0078] R i is the weight factor.

[0079] Furthermore, the formula for adjusting the correction amount of the three parameters of the PID controller is as follows:

[0080] K p =K p0 +k′ p

[0081] K i =K i0 +k′ i

[0082] K d =K d0 +k′ d

[0083] Among them, K p0 , K i0 , K d0 is the initial PID parameter; k′ p , k′ i , k′ d is the correction value of PID parameters.

[0084] In this embodiment, the fuzzy domains of the input variables e and ec are defined as {-3, 3}. The input and output both use seven language value variables, namely: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, abbreviated as {NB, NM, NS, Z0, PS, PM, PB}. The membership function uses a combination of triangular function and Gaussian function. The membership function of the input variable is as follows: Figure 5 As shown. Figure 6 The following are k p ′, k i ′ and k d3D image of the simulation output.

[0085] The three parameters K p 、K i and K d of the PID controller have the fuzzy rule control table as shown in Table 1 below:

[0086] Table 1 Fuzzy rule control table of K p 、K i and K d

[0087]

[0088] In this embodiment, the error e of the output power and the error change rate ec are used as input variables. The system is fuzzified to convert the precise input quantity into a fuzzy quantity, which is represented by the corresponding fuzzy set. The fluctuation range of the wind turbine power should generally be controlled within 10%. The rated power of the direct-drive wind turbine in this embodiment is 160KW, so the range of the error is set to {-16KW, 16KW}, and the range of the corresponding error change rate is set to {-32KW / s, +32KW / s}.

[0089] In this embodiment, the fuzzy universes of the error e and the error change rate ec are both {-3, 3}, and the scale factors of the error and the error change rate can be obtained as follows:

[0090]

[0091]

[0092] Specifically, in step S4, in the repetitive control based on the internal model principle, in addition to the error signal at the current moment applied to the controlled object, the error signal at the previous moment is also superimposed to form a positive feedback with a time-delay link. A second-order low-pass filter is connected in series in the time-delay link to suppress the gain of the repetitive control in the high-frequency band.

[0093] There are usually two selection methods for Q(s) as the coefficient of the time-delay link, namely a constant less than 1 and a low-pass filter. In the actual application process, in order to reduce the complexity of the design process, Q(s) is generally taken as a constant less than 1, and usually 0.95 < Q(s) < 0.98 in engineering applications. When Q(s) is taken as a constant, although the stability and robustness of the system are improved, there is still a certain steady-state error in the system, and the static error-free control of the system cannot be achieved. If Q(s) is a low-pass filter, the ability to suppress low-frequency harmonics of the system can be enhanced, and the accuracy of the fundamental wave amplitude can be improved.

[0094] Furthermore, the second-order low-pass filter is expressed as:

[0095]

[0096] Where ξ is the damping ratio of the low-pass filter, ω c is the cutoff frequency of the low-pass filter.

[0097] Furthermore, the control signal obtained by the repetitive controller is outputted after a certain delay time, and a repetitive compensation controller is provided to repeat PID on the output of the second-order low-pass filter within the delay time before outputting it, and to perform compensation before outputting it again.

[0098] Further, the repetitive compensation controller is expressed as:

[0099]

[0100] Where K is the repetitive controller gain and 1 / Ts+1 is the filter;

[0101] The repetitive controller gain K is used to provide amplitude compensation for the control object. The value of K will affect the stability range, convergence speed and steady-state error of the system. The smaller the K value, the more stable the system but the larger the stability range. The larger the K value, the faster the system converges.

[0102] The filter is used to attenuate high-frequency signals, enhance the anti-interference ability of the system, and improve the stability of the system.

[0103] Repetitive control is a control method based on internal model control. Its core is the internal model (internal model for short). The internal model refers to the mathematical model of the external input signal introduced in the closed-loop control system. In order to form a high-precision control system so that it can track the expected given signal or suppress interference without static error, it can be achieved when the controller of the system contains a dynamic model of the external signal. This is the idea of ​​the internal model principle. For an actual system, the frequency of its input signal is generally not single. If you want the system to achieve the characteristic of no static error, then you need to set an internal model for each frequency signal. This not only has a large number of internal models, but also is difficult to implement. In view of this, the internal model generally takes the following form:

[0104]

[0105] Among them, T d is the fundamental period of the external periodic signal, the above formula is expressed by the transfer function block diagram as follows Figure 2 As shown; P(s) is the transfer function of the controlled object, and D(s) is the disturbance signal.

[0106] In order to improve the rapidity and stability of the control system, a feedforward term is usually added to the repetitive controller, such as Figure 3 As shown, the transfer function is:

[0107]

[0108] When the current feed term a(s) is taken as 1, the transfer function is:

[0109]

[0110] In order to verify the feasibility of the control method proposed in this embodiment, a direct-drive permanent magnet synchronous wind power generation system is used as the object, a simulation experiment is carried out using the Matlab-R2018a platform, and a PMSG system model is established through Simulink. Figure 7 The figure shows the measured wind speed of Shanghai Yangshan Port from May 1 to November 1, 2019. It can be seen that the average wind speed is 10.44 m / s, and the wind speed range is mainly distributed between 5 m / s and 15 m / s. Therefore, the wind speed range used in the simulation of this embodiment is 9 m / s to 17 m / s, and the simulated step wind speed and random wind speed are used for simulation respectively.

[0111] Some parameter settings are as follows: The initial PID parameters in the variable pitch control system adopt the best results obtained by traditional PID tuning, K p =0.6, K i =0.0001,K d =0.01. Other related parameters are shown in Table 2:

[0112] Table 2 Simulation related parameters

[0113]

[0114] 1) Simulation results for step wind speed

[0115] The simulation time is set to 10s, and the wind speed input by the wind turbine is as follows: Figure 8 As shown in the figure, the simulated step wind speed is used, ranging from 10m / s to 15m / s. Before 3s, the wind speed is below the rated wind speed, and between 3s and 10s, the wind speed is above the rated wind speed.

[0116] like Fig. 9 , 10 , 11, 12 and Fig.13 is Figure 8 Under the step wind speed, PID variable pitch control, TS fuzzy PID control and the repeated TS fuzzy PID control method proposed in this embodiment are respectively used to compare the tip speed ratio, wind energy utilization coefficient, pitch angle, generator speed and wind turbine output power after simulation.

[0117] from Fig. 9 and Fig.10It can be seen that before 3s, when the wind speed is less than the rated wind speed, when using the three methods of PID, TS fuzzy PID and repeated TS fuzzy PID, the tip speed ratio and wind energy utilization coefficient both reach the maximum value and reach stability in a very short time, indicating that the use of the optimal torque method for maximum power point tracking and the design of the current loop in this embodiment is very effective. After 3s, when the wind speed exceeds the rated wind speed, in order to maintain the constant speed and power, the tip speed ratio and wind energy utilization coefficient change with the wind speed. It can be seen that the tip speed ratio and wind energy utilization coefficient of the proposed method under step wind speed are more sensitive to wind speed changes, and achieve stability faster than PID control and fuzzy PID control.

[0118] Depend on Fig.11 It can be found that when the wind speed is less than the rated wind speed under the step wind speed, the pitch angle is always 0°. When the wind speed exceeds the rated wind speed, in order to maintain constant power and speed, the pitch angle changes with the wind speed and is inversely proportional to the change in wind speed, thereby obtaining a suitable wind energy utilization coefficient. It can also be seen that the pitch angle of the method proposed in this embodiment changes more rapidly and can adapt to wind speed changes more quickly.

[0119] from Fig.12 , 13 It can be found that before 3 seconds, when the wind speed is less than the rated wind speed, the optimal torque control adopted in this embodiment can track the optimal power well, and the generator speed and the wind turbine output power can reach the maximum value at the corresponding wind speed. After 3 seconds, when the wind speed is above the rated wind speed, variable pitch control is performed. It can be seen that under step wind speed, the method proposed in this embodiment has a smaller overshoot and a faster response speed than PI control and TS fuzzy PID control, which proves the effectiveness of the method proposed in this embodiment.

[0120] 2) Simulation results at random wind speed

[0121] The simulation time is set to 15s, and the wind speed input by the wind turbine is as follows: Fig.14 As shown in the figure, the simulated random wind speed is used, ranging from 9m / s to 17m / s. The wind speed is below the rated wind speed before 6s, and above the rated wind speed between 6s and 15s.

[0122] exist Fig.14 At random wind speeds, Fig.15 , 16 , 17, 18, and 19 are comparison diagrams of the tip speed ratio, wind energy utilization coefficient, pitch angle, generator speed, and wind turbine output power after simulation using the traditional PID variable pitch control and the repeated TS fuzzy PID control method proposed in this embodiment.

[0123] from Fig.15 and Fig.16It can be seen that under random wind speed, when the wind speed is less than the rated wind speed, the tip speed ratio and wind energy utilization coefficient controlled by PID, TS fuzzy PID and repeated TS fuzzy PID all reach the maximum value, and reach stability in a very short time, indicating that the optimal torque method used in this embodiment for maximum power point tracking is very effective. After 5s, when the wind speed exceeds the rated wind speed, in order to maintain the constant speed and power, the tip speed ratio and wind energy utilization coefficient change with the wind speed, and show an opposite trend to the wind speed change.

[0124] from Fig.17 It can be seen that when the wind speed is less than the rated wind speed, the pitch angle is always 0°. At this time, in order to obtain the maximum wind energy, according to Fig.17 It is easy to know that the maximum wind energy can be obtained when the pitch angle is 0; when the wind speed exceeds the rated wind speed, in order to maintain constant power and speed, the pitch angle changes with the wind speed and is inversely proportional to the change in wind speed, thereby obtaining a suitable wind energy utilization coefficient.

[0125] Depend on Fig.18 , Fig.19 It can be seen that under random wind speed, when the wind speed is less than the rated wind speed, the optimal torque control adopted in this embodiment can track the optimal power well, and the generator speed and the wind turbine output power can reach the maximum value under the corresponding wind speed. After 6 seconds, the wind turbine is controlled by the repeated TS fuzzy PID control method that combines the advantages of repeated control and TS fuzzy PID control. Compared with the use of traditional PID control and TS fuzzy PID control, the variable pitch control of the direct-drive wind power generation system is optimized and the steady-state error is reduced. When the wind speed is above the rated wind speed, the pitch change process is smoother, and the output power and the generator speed can be stabilized at the rated value, which improves the stability of the system. The effectiveness of the method proposed in this embodiment is verified.

[0126] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A variable pitch control method for a permanent magnet direct-drive wind power system of a floating offshore wind turbine, characterized in that: It includes the following steps: S1. Obtain the output power of the generator set; S2, calculate the difference between the output power and rated power of the generator set to obtain the power deviation e of the generator set, and calculate the power deviation rate e of the generator set within Δt time c ; S3, design a dual-input, three-output fuzzy PID control model, input the power deviation e and power deviation rate ec obtained in step S2 into the fuzzy PID control model, and the fuzzy PID control model makes corresponding decisions through neuro-fuzzy reasoning and adjusts the PID parameter K online p , K i , K d , to meet the self-tuning requirements of the power deviation e and the power deviation rate ec at different times on the PID parameters, so as to obtain the best adjustment of the pitch angle change target; The fuzzy PID control model includes a TS fuzzy controller and a PID controller; the TS fuzzy controller adjusts the PID parameter K according to the input power deviation e and power deviation rate ec. p , K i , K d Adjustment is performed, and then the correction values ​​of the three parameters are output through fuzzification, fuzzy reasoning and function clarification; the correction values ​​of the three parameters are then input into the PID controller to perform real-time tuning on the three parameters; S4. Repeat the compensation control, embed the repetitive control based on the internal model principle into the fuzzy PID control to form the fuzzy PID control based on repetitive compensation; In the repetitive control based on the internal model principle, in addition to the error signal at the current moment applied to the controlled object, the error signal at the previous moment is also superimposed to form a positive feedback with a time-delay link. A second-order low-pass filter is connected in series in the time-delay link to suppress the gain of the repetitive control in the high-frequency band; The control signal obtained by the repetitive controller needs to be output after a certain delay time. It is set that the repetitive compensation controller repeats the PID for the output of the second-order low-pass filter within the delay time and then outputs it, and compensation is performed before the re-output; 2. A variable pitch control method for a permanent magnet direct-drive wind power system of a floating offshore wind turbine according to claim 1, characterized in that: The rule of the T-S fuzzy controller is expressed as: if e is A i1 ,ec is A i2 ,then ui=f(e,ec)=c i0 +c i1 e+c i2 ec Among them, A i1 , A i2 are the two fuzzy sets in the i-th rule; c i0 、c i1 and c i2 is a constant coefficient, which is determined by identification based on measured data and is used to reflect the inherent properties of the system; Assume that the system has n rules. When a certain input triggers l of them, where l < n, the total output of the system is: Among them, w i The weight of each rule is: w i =R i A i1 (e)A i2 (ec) R i is the weight factor.

3. The variable pitch control method of a permanent magnet direct-drive wind power system of a floating offshore wind turbine according to claim 1 is characterized in that: The formula for the PID controller to tune the correction amounts of the three parameters is as follows: K p =K p0 +k′ p K i =K i0 +k′ i K d =K d0 +k′ d Among them, K p0 , K i0 , K d0 is the initial PID parameter; k′ p , k′ i , k′ d is the correction value of PID parameters.

4. A variable pitch control method for a permanent magnet direct-drive wind power system of a floating offshore wind turbine according to claim 1, characterized in that: The second-order low-pass filter is expressed as:

5. The variable pitch control method of a permanent magnet direct-drive wind power system of a floating offshore wind turbine according to claim 1, characterized in that: The repetitive compensation controller is expressed as: Where, K is the gain of the repetitive controller, and 1 / (Ts + 1) is the filter; The gain K of the repetitive controller is used to provide amplitude compensation for the controlled object. The magnitude of the K value will affect the stability range, convergence speed, and steady-state error of the system; the smaller the K value, the more stable the system but the larger the stability range; the larger the K value, the faster the system convergence speed; The filter is used to attenuate high-frequency signals, enhance the anti-interference ability of the system, and improve the stability of the system.

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