CN107975457B - Wind turbine generator variable pitch control method for inhibiting wind speed fluctuation interference - Google Patents
Wind turbine generator variable pitch control method for inhibiting wind speed fluctuation interference Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/022—Adjusting aerodynamic properties of the blades
- F03D7/0224—Adjusting blade pitch
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
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- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/04—Automatic control; Regulation
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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Abstract
The invention discloses a wind turbine generator variable pitch control method for inhibiting wind speed fluctuation interference, and relates to the field of wind power generation variable pitch control. Firstly, in order to avoid a control chaotic effect caused by nonlinearity of a wind turbine generator, linearization is carried out on the wind turbine generator at a constant power point above a rated wind speed; meanwhile, a sliding mode controller is designed in consideration of the fact that the traditional PID controller cannot achieve a satisfactory effect on power precision control. Secondly, aiming at the problem of jitter in the control process caused by sliding mode control, a method for pre-estimating and compensating the wind speed fluctuation interference is adopted. Finally, in order to solve the time lag effect in the control process caused by the large inertia of the unit, a Kalman filter and a Newton Raphson algorithm are utilized to predict the effective wind speed, so that the pitch angle is compensated. The control method provided by the invention can well reduce the sliding mode jitter, improve the response speed of the system, and can well stabilize the output power, thereby having certain practical value.
Description
Technical Field
The invention belongs to the field of wind power generation variable pitch control, and particularly relates to a variable pitch control method for controlling output power of a wind turbine.
Background
Due to the advantages of green and sustainable renewable energy sources, much attention is paid to countries in the world. Wind power generation is an important renewable energy source, and the installed capacity accounts for an important proportion of power consumption, so that the improvement of the power generation quality has an important significance. Above the rated wind speed, the output power must be kept constant. The pitch control technology is one of the important means for keeping the output power constant[1-2]。
Due to the characteristics of time lag, nonlinearity and the like of a wind turbine, a conventional PID controller is difficult to meet the control requirement, and in order to improve the control precision and the system stability, a plurality of scholars have conducted intensive research on the problem and put forward a plurality of schemes. Such as fuzzy control, neural network, sliding mode variable structure control, feedforward-feedback control and other advanced control theories. The document [3] deduces an affine nonlinear model of the wind turbine generator, designs a nonlinear controller on the basis, and carries out accurate linearization. Document [4] adopts an intelligent genetic algorithm to optimize PID parameters and improve the performance of the variable pitch controller. According to known characteristics of a wind turbine and a sliding mode variable structure system theory, a state equation of equivalent control on a switching surface and sliding mode motion on the switching surface is deduced in a literature [5 ]. In order to weaken buffeting, a fuzzy sliding mode control method based on maximum wind energy tracking control is adopted. In document [6], after a wind turbine is linearized in the vicinity of a balance point, a linear parameter variation algorithm is used to improve the control accuracy. In the literature [7], a Kalman filter is designed for solving the problem that the effective wind speed actually acting on the wind turbine is difficult to measure, and the wind speed is calculated recursively by performing optimal estimation on the wind turbine aerodynamic torque and the relationship between the optimal estimation and the wind speed. Meanwhile, a variable pitch prediction controller is designed by taking the stability of the rotating speed of the wind wheel and the minimization of the displacement of the tower top as an optimization control target. Document [8] researches how to suppress output power fluctuation caused by feedback signal lag in a variable speed variable pitch wind generating set. On the basis of traditional PID feedback control and feedforward control based on measured wind speed, a variable pitch control strategy combining feedforward of effective wind speed estimation and traditional PID feedback is provided, effective wind speed estimation is carried out through Kalman filtering and Newton-Raphson algorithm, and proper feedforward pitch angle is provided according to the estimated effective wind speed to realize dynamic feedforward compensation. According to the feedforward feedback control strategy established in the document [9], a differentiation link is added in feedback control, and a fuzzy control rule is adopted by a feedforward controller, so that a good control effect is achieved. Document [10] optimizes PI controller parameters using a radial basis function neural network, and optimizes the neural network using a particle swarm algorithm. Document [11] designs a PI controller with optimized parameters while employing delay interference estimation and signal compensation techniques.
In the research, various control methods are tried for solving the problem of power stability in the control system of the wind turbine generator. Which respectively solve some problems and improve the control performance. On the basis of the researches, the invention adopts sliding mode variable structure control, simultaneously carries out pre-estimation compensation on wind speed fluctuation interference, reduces the jitter, carries out prediction compensation on the pitch angle by utilizing the Kalman algorithm and improves the response speed of the system.
Reference documents:
[1]Xiu-xing Yin,Yong-gang Lin,Wei Li,Ya-jing Gu,Xiao-jun Wang,Peng-fei Lei.Design,modeling and implementation of a novel pitch angle controlsystem for wind turbine.Renewable Energy,2015,81:599-608.
[2] yangjunhua, Zheng and frugal, Yangmenli, Wujie, constant power feedback linearization control of variable pitch wind generating set, control theory and application, 2012, 29 (10): 1365-1370.
[3] The wind turbine has the advantages of energy covering success, full leaf branches, active nonlinear control of a horizontal axis vector type wind turbine [ J ]. solar energy science, 2004,25(4): 519-524.
[4]ZaferCivelek,MuratLüy,HayatiMamur.Proportional-integral-derivative parameter optimisation ofblade pitch controller in wind turbinesby a new intelligent genetic algorithm.IET Renewable Power Generation.2016,10(8):1-9.
[5] Qin bin, Zhonhao, Qiu Li, Guobeisan, Wangxin wind power generation system based on fuzzy sliding mode control maximum wind energy tracking [ J ] Shanghai university of traffic bulletin 2014,48(07): 993-.
[6]Fernando A.Inthamoussou,Hernán De Battista,Ricardo J.Mantz.LPV-based active power control ofwind turbines covering the complete wind speedrange.Renewable Energy.2016,99:996-1007.
[7] The wind turbine disturbance feedforward and prediction feedback composite control of load inhibition is carried out by Wangxiang, Thanghuim, Baoguang, Zhang Xiaoying and Lioghen, J.the report of electrotechnical science, 2016,31(02): 230-.
[8] Joyulin, huangshuai, dujing, soudongxiag, lisjun. wind turbine generator set pitch control based on feedforward [ J ] power system protection and control, 2012,40(03):15-20.
[9] Gupeng, fuzzy feedforward and fuzzy PID combined wind generating set variable pitch control, Chinese Motor engineering newspaper, 2010, 30 (8): 123-128.
[10]Iman Poultangari,Reza Shahnazi,Mansour Sheikhan.RBF neural network based PI pitch controller for a class of5-MW windturbines usingparticle swarm optimization algorithm.ISA Transaction,2012,51:641-648.
[11]Richie Gao,Zhiwei Gao.Pitch control for wind turbine systems using optimization,estimation andcompensation.Renewable Energy.2016,91:501-515.
[12] Xuhong, liu dong le, observer-based wind power generation system slide rheological structure control power system and its automated chemistry report [ J ], 2013, 25 (2): 20-25.
[13]Endusa Billy Muhando,Tomonobu Senjyu,Naomitsu Urasaki,et al.Gain scheduling control of variable speed WTG under widely varying turbulenceloading[J].Renewable Energy,2007,32:2407-2423.
[14]Boukhezzar B,Siguerdidjane H,Maureen Hand M.Nonlinear control of variable-speed wind turbines generator torque limiting and power optimization[J].Journal of Solar Energy Engineering,2006,128:516-530.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a wind turbine generator variable pitch control method for suppressing wind speed fluctuation interference, which reduces the jitter problem in the control process, improves the control precision and improves the response speed of a system. The technical scheme of the invention is as follows:
a wind turbine generator variable pitch control method for inhibiting wind speed fluctuation interference comprises the following steps:
1) firstly, linearizing a wind turbine generator model at a constant power point above a rated wind speed, and designing a sliding mode controller; design slideThe mold controller is us＝ψx1+ φ sgn(s), where: psi is the error coefficient of wind wheel, phi is the gain coefficient of sliding mode interference switching, x1For input errors, s is the switching function.
2) Secondly, aiming at the jitter generated in the control process caused by the sliding mode control in the step 1), a method for pre-estimating and compensating the wind speed fluctuation interference is adopted, firstly, a Kalman filtering algorithm is used for estimating the pneumatic torque, then, a Newton-Raphson algorithm is used for estimating the effective wind speed, the estimated interference amount is obtained, and the switching gain is reduced;
3) and finally, compensating the pitch angle in real time according to the effective wind speed estimated in the step 2) and the relation between the wind speed and the pitch angle.
Further, the step 2) estimates the aerodynamic torque by using a kalman filter algorithm, and estimates the effective wind speed by using a newton-raphson algorithm, so as to obtain an estimated disturbance variable specifically including;
the first order markov process for pneumatic torque is:
in the formula: t isaFor pneumatic torque, TψIs a torque-related coefficient.
The equation of state for the transmission can be found as:
in the formula: omegarIs the rotational speed of the wind wheel, ZtIs a constant of moment of resistance, JzFor the reduced moment of inertia, T, of the wind power systemaIs a pneumatic torque, U is a control input torque,
according to actual needs, the system output equation can be set as follows:
Y＝HX (27)
in the formula: h ═ 10;
according to sampling requirements, taking the period of the system as 0.001S to discretize a system equation, and obtaining a discretization model of the transmission system as follows:
in the formula: f (k +1, k) is a state transition matrix; b (k +1, k) is a control matrix; x (k) is a state vector; h is a state observation matrix; upsilon is system measurement noise; omega is system process noise;
estimating the wind speed by adopting a Kalman filtering algorithm, and estimating the optimal wind speed by utilizing a Newton-Raphson algorithm on the basis of obtaining an estimated value of the pneumatic torque;
according to the iteration idea of the Newton Raphson algorithm, the iteration expression of the optimal wind speed estimation is as follows:
in the formula:the optimal wind speed at the current moment is obtained; knTo optimize the derivative function; ρ is the air density; cpThe wind energy utilization coefficient; r is the radius of the wind wheel; λ is tip speed ratio;the optimal wind speed at the next moment;estimating a pneumatic torque; j. the design is a squarenIs an optimized objective function.
Further, in the step 3), the relationship between the wind speed and the pitch angle is as follows: the nonlinear mapping relation model between the wind speed V input quantity and the pitch angle beta output value is as follows
β(v)＝a0+a1v+…anvn，
β＝f(Pd,V)，Pa＝(1+k％)Pd (31)
In the formula: a isnAs fitting coefficient, PdAt a constant value, P, for rated output poweraThe power absorbed by the impeller is represented, and the energy loss of the wind turbine is set to be k% according to specific conditions.
The invention has the following advantages and beneficial effects:
(1) because the wind turbine generator is a complex system with strong nonlinearity and large inertia, the wind turbine generator is linearized by adopting a constant power point above a rated wind speed so as to control the system in a global range, and meanwhile, a sliding mode variable structure controller is designed so as to improve the control precision.
(2) The wind speed fluctuation interference item exists in a linearization system, and in order to eliminate the interference, the invention carries out real-time pre-estimation compensation on the wind speed fluctuation interference so as to reduce the interference switching gain coefficient in the designed control rate and further reduce the jitter problem in the control process.
(3) Due to the large inertia of the wind turbine generator, delay of control signals is easily caused, and therefore pitch variation is not timely.
Drawings
FIG. 1 is a block diagram of a wind power system according to a preferred embodiment of the present invention;
FIG. 2 is a schematic diagram of a control strategy;
FIG. 3 is an input wind speed plot;
FIG. 4 is a graph of output power versus simulation;
FIG. 5 is a graph of sliding mode control output power;
FIG. 6 is a pitch comparison simulation.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
1) linearizing a wind turbine model and designing a control rate
Wind turbines convert wind energy into mechanical energy according to the Betz theory of aerodynamics (Betz the)[2]Therefore, the following steps are carried out:
Pa＝0.5ρπR2Cp(λ,β)V3 (1)
in the formula: paPower absorbed for the fan impeller; ρ is the air density; r is the radius of the fan impeller; cpThe coefficient of utilization of wind energy is a function of lambda and beta when capturing the wind energy; v is the actual wind speed; beta is the pitch angle of the fan; λ is the tip speed ratio, which is a function of the rotor speed and pitch angle, i.e.
λ＝ωrR/V (2)
In the formula: omegarIs the rotational speed of the wind wheel.
Coefficient of wind energy utilization CpIs a function of lambda, beta, which is related to the structural performance of the wind turbine. Generally, the following characteristic curve is selected according to experience to approximately fit the characteristic of the wind turbine for absorbing wind energy.
Cp＝(0.44-0.0167β)sin(π(λ-3)/(15-0.3β))-0.00184(λ-3)β (3)
The transmission model of the wind turbine is as follows (the model is shown in figure 1):
in the formula: j. the design is a squarefIs the rotational inertia of the fan impeller; t iszIs the resisting moment, which is assumed to be concentrated at the low speed end of the gearbox; t islThe low-speed end torque of the speed increasing gear box; j. the design is a squaredgIs the rotational inertia of the generator; omegahThe rotating speed of the high-speed end of the gear box; t ishOutputting torque for the gearbox; t iseIs the electromagnetic torque of the generator, since here is the rated wind speedThe upper constant power control can be set to be a fixed value, and the value is set according to specific power generation requirements.
The system drag torque characteristics and gear ratios are:
Tz＝Ztωr (6)
k＝ωh/ωr (7)
in the formula: ztIs a moment of resistance constant, and is related to a force transmission structure at a low speed end. k is the gear ratio of the gear.
The characteristic equation of the whole wind turbine transmission system part can be obtained by bringing the equations (5), (6) and (7) into the equation (4):
Jz＝k2Jdg+Jf (9)
in the formula: j. the design is a squarezAnd converting the converted rotational inertia of the whole wind power system.
The variable-pitch actuating mechanism is in hydraulic transmission and is a first-order inertia link:
the invention aims at the constant power control above the rated wind speed, so that the electromagnetic torque and the reference rotating speed are constant values, and the system resisting torque can be regarded as a constant.
Since the aerodynamic torque of the fan is a non-linear function of three variables, i.e.
The formula (11) is set at a constant power point (v) above the rated wind speedp，βp，ωp) Is unfolded[12]
Wherein Δ v-vp，Δβ＝β-βp，Δω＝ω-ωpThen there is
Ignore the high order infinitesimal quantities and define:
can be obtained by combining the vertical type (8), (9) and (13)
According to the formulae (10) and (14), the compounds
Setting the reference rotation speed of the flying and wind wheel as omegarefDefining: x is the number of1＝ωref-ω，Then equation (15) can be converted to a matrix form
According to the linearized system, Δ ω is used as the controller input, and u ═ Δ βr- Δ β is the output of the converter,for interference, the sliding mode controller is designed as
us＝ψx1+φsgn(s) (17)
In the formula, psi is a wind wheel error coefficient, and phi is a sliding mode interference switching gain coefficient.
Having a switching function of
s＝cx1+x2 (18)
In the formula: c is a normal number.
According to the design requirement, the requirements c, psi and phi meet the following requirements
φ＞|d|max(21)
Derived from the switching function
Then
The design requirements according to the formula (20) show
As can be seen from the design requirement equation (21), as the interference d decreases, the switching interference gain Φ also decreases, thereby achieving the effect of reducing jitter.
2) Compensating for wind speed fluctuation interference
In order to reduce sliding mode jitter, the invention designs a wind speed disturbance compensation method. According to the method, firstly, the pneumatic torque is estimated by using a Kalman filtering algorithm, and then the effective wind speed is estimated by using a Newton-Raphson algorithm, so that the estimated interference amount is obtained, and the switching gain is reduced.
The first order Markov process of the pneumatic torque is[13]：
By taking equation (25) into equation (8), the state equation of the transmission system can be obtained as follows:
according to actual needs, the system output equation can be set as follows:
Y＝HX (27)
in the formula: h ═ 10.
According to sampling requirements, taking the period of the system as 0.001S to discretize a system equation, and obtaining a discretization model of the transmission system as follows:
in the formula: f (k +1, k) is a state transition matrix; x (k) is a state vector; h is a state observation matrix; upsilon is system measurement noise; ω is the system process noise.
Due to the limitation of anemometer measurement caused by wind speed time-varying property, turbulence and tower influence, a Kalman filtering algorithm is adopted to estimate the wind speed. And estimating the optimal wind speed by utilizing a Newton Raphson algorithm on the basis of obtaining the pneumatic torque estimated value.
Iterative thought based on Newton Raphson algorithm[14]The iterative expression of the optimal wind speed estimation is as follows:
in the formula:the optimal wind speed at the current moment is obtained;the optimal wind speed at the next moment;estimating a pneumatic torque; j. the design is a squarenFor optimized objective function
3) Compensating for pitch angle input
After the wind turbine blade is produced and shaped, the wind energy utilization curve is determined, so CpIs not changed and the rotor speed should be maintained around the nominal value, so CpThe relation to the actual wind speed V becomes a certain non-linear relation between the pitch angles β and V. Therefore, the mechanical energy captured by the wind turbine can be abbreviated as[9]：
Pa＝f(V,β) (30)
The energy loss of the wind turbine generator is basically kept unchanged after production and sizing, mainly comprises some friction losses in some transmission processes, the value of the friction losses can be set to be k% according to specific conditions, and in order to keep the output power constant, the power absorbed by the impeller is as follows:
Pa＝(1+K％)Pd (31)
in the formula: pdThe rated output power is a constant value.
According to formulae (30) and (31) having
β＝f(Pd,V) (32)
The functional relationship in equation (32) is necessarily particularly complex due to the non-linearity of the actual wind power system and its complex structure. In order to obtain a simplified and accurate nonlinear mapping relation between the pitch angle and the actual wind speed, the article firstly calculates and obtains the corresponding pitch angle between the rated wind speed and the cut-out wind speed under a complex condition through a Newton Raphson algorithm, and then fits the nonlinear mapping relation between the wind speed input quantity and the pitch angle output value by using a least square method.
The nonlinear mapping relation model between the wind speed V input quantity and the pitch angle beta output value is as follows
β(v)＝a0+a1v+…anvn (33)
On an MATLAB platform, a wind power system control model (shown in an attached figure 2) is simulated by taking random wind speed (shown in an attached figure 3) as actual input, and experimental analysis results are as follows: compared with the traditional PID control and sliding mode variable structure control method, the control precision of the output power is improved, the fluctuation error is reduced, and the jitter is more serious; after wind speed fluctuation interference compensation and control input pitch angle prediction compensation are added, the jitter in the variable pitch process in the simulation attached figure 6 is reduced to a certain extent, and meanwhile the response speed of variable pitch output is improved. After the interference is compensated, the switching gain is reduced, so that the effect of reducing the sliding mode jitter is achieved; after the pitch angle prediction compensation is added, the advance compensation can be carried out on the pitch changing process, and output power fluctuation caused by pitch changing delay due to large inertia of a wind power system is avoided.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (2)
1. A wind turbine generator variable pitch control method for inhibiting wind speed fluctuation interference is characterized by comprising the following steps:
1) firstly, linearizing a wind turbine generator model at a constant power point above a rated wind speed, and designing a sliding mode controller; the sliding mode controller is designed asIn the formula: psi is the wind wheel error coefficient,switching gain factor, x, for sliding mode disturbances1S is a switching function to represent the input error;
2) secondly, aiming at the jitter generated in the control process caused by the sliding mode control in the step 1), a method for pre-estimating and compensating the wind speed fluctuation interference is adopted, firstly, a Kalman filtering algorithm is utilized to estimate the pneumatic torque, then, a Newton-Raphson algorithm is utilized to estimate the effective wind speed, the estimated interference amount is obtained, the switching gain is reduced, then, a least square method is utilized to fit the nonlinear mapping relation between the wind speed input amount and the pitch angle output value,
the nonlinear mapping relation model between the wind speed V input quantity and the pitch angle beta output value is as follows
β(v)＝a0+a1v+…anvn，anRepresenting the fitting coefficient;
3) finally, compensating the pitch angle in real time according to the effective wind speed estimated in the step 2) and the relation between the wind speed and the pitch angle;
the step 2) utilizes a Kalman filtering algorithm to estimate the pneumatic torque, and then utilizes a Newton-Raphson algorithm to estimate the effective wind speed, so as to obtain the estimated interference amount;
the first order markov process for pneumatic torque is:
in the formula: t isaFor pneumatic torque, TψIs a torque correlation coefficient;
the equation of state for the transmission can be found as:
in the formula: omegarIs the rotational speed of the wind wheel, ZtIs a constant of moment of resistance, JzFor the reduced moment of inertia, T, of the wind power systemaIs a pneumatic torque, U is a control input torque,
the system output equation is set as:
Y＝HX
in the formula: h ═ 10;
taking the period of the system as 0.001S to discretize the system equation, and obtaining a discretization model of the transmission system as follows:
in the formula: f (k +1, k) is a state transition matrix; b (k +1, k) is a control matrix; x (k) is a state vector; h is a state observation matrix; upsilon is system measurement noise; omega is system process noise;
estimating the wind speed by adopting a Kalman filtering algorithm, and estimating the optimal wind speed by utilizing a Newton-Raphson algorithm on the basis of obtaining an estimated value of the pneumatic torque;
according to the iteration idea of the Newton Raphson algorithm, the iteration expression of the optimal wind speed estimation is as follows:
in the formula:the optimal wind speed at the current moment is obtained; knTo optimize the derivative function; ρ is the air density; cpThe wind energy utilization coefficient; cqIs a torque coefficient;the estimated value of the rotating speed of the wind wheel is shown, and R is the radius of the wind wheel; λ is tip speed ratio;the optimal wind speed at the next moment;estimating a pneumatic torque; j. the design is a squarenIs an optimized objective function.
2. The wind turbine generator pitch control method for suppressing wind speed fluctuation interference according to claim 1, wherein the relationship between the wind speed and the pitch angle in step 3) is a determined nonlinear relationship: the nonlinear mapping relation model between the wind speed V input quantity and the pitch angle beta output value is as follows
β(v)＝a0+a1v+…anvn，
β＝f(Pd,V)，Pa＝(1+k％)Pd
In the formula: a isnAs fitting coefficient, PdFor rated output power to be constant, PaThe power absorbed by the impeller is represented, and the energy loss of the wind turbine is set to be k% according to specific conditions.
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