CN109209768A - A kind of constant output control method of large scale wind power machine - Google Patents

A kind of constant output control method of large scale wind power machine Download PDF

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CN109209768A
CN109209768A CN201811008114.5A CN201811008114A CN109209768A CN 109209768 A CN109209768 A CN 109209768A CN 201811008114 A CN201811008114 A CN 201811008114A CN 109209768 A CN109209768 A CN 109209768A
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formula
fuzzy
domain
control
deviation
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CN109209768B (en
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任海军
邓广
吉昊
郑智文
郭儒
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Chongqing University of Post and Telecommunications
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    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03DWIND MOTORS
    • F03D7/00Controlling wind motors 
    • F03D7/02Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/022Adjusting aerodynamic properties of the blades
    • F03D7/0236Adjusting aerodynamic properties of the blades by changing the active surface of the wind engaging parts, e.g. reefing or furling
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03DWIND MOTORS
    • F03D17/00Monitoring or testing of wind motors, e.g. diagnostics
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03DWIND MOTORS
    • F03D7/00Controlling wind motors 
    • F03D7/02Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/0256Stall control
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03DWIND MOTORS
    • F03D7/00Controlling wind motors 
    • F03D7/02Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/0264Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor for stopping; controlling in emergency situations
    • F03D7/0268Parking or storm protection
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03DWIND MOTORS
    • F03D7/00Controlling wind motors 
    • F03D7/02Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04Automatic control; Regulation
    • F03D7/042Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/043Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
    • F03D7/044Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with PID control
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00Energy generation through renewable energy sources
    • Y02E10/70Wind energy
    • Y02E10/72Wind turbines with rotation axis in wind direction

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  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Fluid Mechanics (AREA)
  • Feedback Control In General (AREA)

Abstract

A kind of large scale wind power machine constant output control method is claimed in the present invention, is related to wind energy conversion system power control field.Firstly, the present invention on the basis of analyzing wind turbine system influences power output state variable, proposes pitch control and direct torque using series connection and the Compound Control Strategy in parallel combined.Then, the method for proposing to optimize PID controller initial parameter using artificial bee colony algorithm reduces impact of the excessive overshoot of the improper generation of initial time parameter setting to system.Then, it devises fuzzy inference rule and on-line tuning is carried out to pid parameter, and variable universe processing is done to the domain of fuzzy reasoning, improve controller adaptive ability.The present invention improves the adaptive ability of controller on the basis of reducing system overshoot.Therefore, output power quality effectively improves.

Description

A kind of constant output control method of large scale wind power machine
Technical field
The invention belongs to wind energy conversion system constant output control fields, and in particular to one kind belongs to fusion artificial bee colony algorithm The large scale wind power machine Poewr control method obscured with variable universe,.
Background technique
Large-scale variable-pitch variable-speed wind energy conversion system is one of mainstream model of wind-driven generator, with adjustable speed, can pass through change paddle The characteristics of elongation big minor adjustment output power, can be improved output power quality, reduce physical system damage rate [1-3].Control system System design is the core key technology of large scale wind power machine, scholars and the very big effective control of energy exploitation of wind-powered electricity generation enterprise investment Algorithm processed.From most common pid algorithm to the fusion of intelligent control algorithm and multi-intelligence algorithm, it is applied to wind energy conversion system In control system.Moreover, because the strong nonlinearity feature of wind energy conversion system, needs to carry out linearization process or directlys adopt non-linear Control method.Bravely just equal [4] have carried out permanent function to speed-changing oar-changing wind energy conversion system using variable pitch angle and output power as judgment basis to woods Rate and maximal power point tracking control.More than rated wind speed, since propeller pitch angle variation and output power are in non-linear relation, Variable pitch angle change is divided into several stages, each stage uses different pid control parameters, so as to improve power control matter Amount.Guo Peng [5] carries out pitch control in such a way that PID control is combined with fuzzy control.When deviation is larger, using fuzzy Algorithm provides control amount, realizes the quick adjusting of deviation.When deviation is smaller, steady-state error is eliminated using PID controller.This Sample improves control efficiency according to the feature of two kinds of control algolithms respectively.Yin Xiuxing etc. [6] is calculated using two dimension fuzzy vector table Method acquires propeller pitch angle given value, using the tracing control for repeating PID controller realization propeller pitch angle.Due to repeating PID controller not The deviation signal at current time is only accounted for, and also contemplates the control deviation of last time, therefore improves control precision. Han Bing etc. [7] realizes the self adaptive control of independent pitch system using RBF neural, obtains nerve net using Lyapunov The adaptive rate of network, the regulating networks weight in a manner of this on-line tuning, in this way, pitch-controlled system dynamic characteristic is optimized. Geng Hua etc. [8], which is analyzed, carries out in power control process pitch-controlled wind turbine using method of inverse, when parameter perturbation occurs Robustness.Introducing robust compensation item on the basis of inverse system controller is proposed, to system when eliminating parameter wide variation Influence.These methods all optimize variable blade control system to a certain extent, achieve certain research achievement, promote wind The development of power machine technology.
But the output power of wind energy conversion system and wind speed is cube directly proportional, small wind speed variation will cause output power Large change, moreover, output power and variable pitch angle and tip speed ratio be non-linear relation and pitch-controlled system time lag it is special Property, these factors all make Control System Design become complicated, how to realize that be precisely controlled to make great efforts for scholars to solve asks Topic.For the deficiency of PID controller in wind energy conversion system constant output power control, need to design more reasonable control strategy and calculation Method improves controller performance, and when making rated wind speed or more, power swing is small, can stablize output.
After rated wind speed, the constant output power control of wind energy conversion system is one of the hot issue of research.Using adjusting It is typical control method that the mode of variable pitch angle, which controls output power, still, since large scale wind power machine blade is long, quality is big, Variable pitch hysteresis is obvious, and when wind speed is in a small range rapid fluctuations, pitch rate is difficult to match with wind speed change frequency, makes Control effect is obtained to be deteriorated.For this problem, proposition takes pitch control and direct torque to combine when wind speed changes greatly, When wind speed variation is smaller using the Compound Control Strategy of direct torque.Moreover, for PID controller initial value be not easy determine and It is difficult to adapt to the deficiency of external change, proposes the intelligent control side combined using artificial bee colony algorithm with variable universe fuzzy reasoning Method.Bibliography
[1]Pedram Bagheri,Qiao Sun.Adaptive robust control of a class of non- affine variable-speed variable-pitch wind turbines with unmodeled dynamics [J].ISA Transactions,2016,63:233-241.
[2]Zafer Civelek,Murat Lüy,Hayati Mamur. Proportional-integral-derivative parameter optimisation of blade pitch controller in wind turbines by a new intelligent genetic algorithm[J].IET renewable power generation,2016,10(8):1220-1228.
[3]Mao-Hsiung Chiang.A novel pitch control system for a wind turbine drive by a variable-speed pump-controlled hydraulic servo system[J] .Mechatronics,2011, 21:753-761.
[4] Lin Yonggang, Li Wei, Cui Baoling, Liu Hongwei.Wind turbines electric-hydraulic proportion feather technical research [J].Solar energy Journal, 2007,28 (6): 658-662.
[5] Guo Peng.The variable pitch control research [J] in conjunction with fuzzy of Wind turbines nonlinear feedforward.Dynamic engineering Report, 2010,30 (11): 838-843.
[6] Yin Xiuxing, Lin Yonggang, Li Wei, Shi Maoshun, Lou Shan.Electrohydraulic digital motor variable pitch control technology [J].The sun Energy journal, 2014,35 (9): 1627-1633.
[7] Han Bing, Zhou Lawu, Chen Hao, Deng Ningfeng, Tian Meng.Wind turbines independent feathering control based on RBF neural [J].Chinese science: technological sciences, 2016,46 (3): 248-255.
[8] Geng Hua, Zhou Weisong, Yang Geng.The inverse system robust method [J] of feather wind power system power control.Tsing-Hua University is big It learns journal (natural science edition), 2010,50 (5): 718-723.
[9]Abdeldjalil Dahbi,Nasreddine Nait-Said,Mohamed-Said Nait-Said.A novel combined MPPT-pitch angle control for wide range variable speed wind turbine based on neural network[J].International journal of hydrogen energy, 2016,41:9427-9442.
[10]Iman Poultangari,Reza Shahnazi,Mansour Sheikhan.RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm[J].ISA transactions,2012,51:641-648.
[11] Qin Quande, Cheng Shi, Li Li, Shi Yu return artificial bee colony algorithm Review Study [J] intelligence system journal, 2014,9 (2): 127-135.
[12] Xu Longqin, prune river, Liu Shuanyin, the bright in Lee road are based on set empirical mode decomposition and artificial bee colony algorithm Industrial aquaculture pH value predicts [J] Journal of Agricultural Engineering, 2016,32 (3): 202-209.
[13] Sun Tao, the Qin record virtue wind mill pitch-variable Fuzzy PID parameters self-adjusting control [J] lathe with it is hydraulic, 2011,39 (10): 121-123,130.
[14] Gao Shuzhi, Gao Xianwen, Zhu Zhi hold stripper temperature control method [J] east of the based on variable universe fuzzy Northern college journal (natural science edition), 2010,31 (10): 1369-1372.
Summary of the invention
Present invention seek to address that the above problem of the prior art.A kind of overshoot of reduction system initial time is proposed, Improve the constant output control method of the large scale wind power machine of performance of the controller in wide scope, high-frequency variation.This hair Bright technical solution is as follows:
A kind of constant output control method of large scale wind power machine comprising following steps:
Firstly, proposing pitch control on the basis of analyzing wind turbine system influences power output state variable and turning Square controls the Compound Control Strategy combined, i.e., more than rated wind speed, when wind speed changes amplitude greater than limit value, with variable pitch control System and the concatenated mode of direct torque are controlled;It is less than limit value in wind speed variation amplitude and change frequency is greater than the set value f When, it is controlled in a manner of direct torque;
Then, PID controller initial parameter is optimized using artificial bee colony algorithm;
Finally, design fuzzy inference rule carries out on-line tuning to pid parameter, by being multiplied by contraction-expansion factor, with variable universe Mode is adjusted domain.
Further, with pitch control and torque when the wind speed variation amplitude more than rated wind speed is greater than limit value It controls concatenated mode to be controlled, specifically include:
When wind speed variation amplitude is greater than limit value, power is controlled rapidly to the range to setting using pitch control first It is interior, and keep variable pitch angle at this time.Then, the response speed of control system is improved using direct torque mode, reduces power Fluctuation.
Further, when wind speed variation amplitude is less than limit value and change frequency is greater than the set value f, with direct torque Mode is controlled, and is specifically included:
Analysis is when wind speed variation amplitude is less than limit value and change frequency is greater than limit value using pitch control first The advantages of drawback and use direct torque, and combined with artificial bee colony algorithm and variable universe fuzzy algorithmic approach, improve control system Precision.
Further, described that PID controller initial parameter is optimized using artificial bee colony algorithm, it specifically includes:
The position in nectar source is the key message of gathering honey, represents the potential solution of problem, and the position of nectar source i is represented by formula (12);
In formula, t expression current iteration number, k representation dimension,Indicate k location of the nectar source i in t iteration.Nectar source i Initial position may be expressed as:
xik=Lk+rand(0,1)(Uk-Lk) (13)
In formula, LkIndicate the lower limit of search space, UkIndicate the upper limit of search space, rand (0,1) is generated between 0 to 1 Random number;
Bee is led to indicate the new bee source position formula (14) searched;
vik=xik+r(xik-xnk) (14)
In formula, r is equally distributed random number between [- 1,1], and n ∈ { 1,2 ... }, n indicate to select one in nectar source Nectar source not equal to i;
After leading bee to determine the location information in new nectar source, flies back and carry out nectar source information sharing to specified region, follow bee It is followed according to the nectar source information obtained, probability formula (15) is followed to indicate;
In formula, q indicates nectar source sum, fitiIndicate fitness;
Fitness can be indicated with formula (16).
In formula, fiIndicate the functional value of solution;
The fitness in new bee source is evaluated according to fitness expression formula (16), determines and retains bee source, by such process, honey Bee completes the searching to best bee source.This process namely using artificial bee colony algorithm to the optimization process of PID parameter.
Further, the design fuzzy inference rule carries out on-line tuning, and the fixation to fuzzy reasoning to pid parameter Domain replaces with variable universe, specifically includes:
Fuzzy controller is inputted using error and error rate as fuzzy controller, it is necessary first to by the basic of input Domain is converted into fuzzy domain, takes quantizing factor appropriate, and basic domain is converted to fuzzy domain.Quantizing factor can use formula (17), (18) determine;
In formula, KcFor the quantizing factor of deviation, KecFor the quantizing factor of deviation variation rate, emaxFor maximum deviation, eminFor Minimum deflection, ecmaxFor maximum deviation change rate, ecminFor minimum deflection change rate;
Value after fuzzy control is converted to practical control output, the ratio that anti fuzzy method uses after anti fuzzy method The example factor is determined using formula (19);
In formula, Δ KmaxFor the actual change maximum value of the respective domain of ratio, integral, differential parameter, Δ KminFor ratio, product Point, the actual change minimum value of the respective domain of differential parameter;
Contraction-expansion factor is multiplied by the basis of initial domain, input deviation contraction-expansion factor can be determined with formula (20), input variation Rate contraction-expansion factor can be determined with formula (21);
In formula, λ1、λ2∈ [0,1], ε1、ε2For arbitrarily small positive number, xemaxIndicate the maximum value of deviation domain, xecmaxTable Show the maximum value of deviation variation rate domain.
Further, Δ Kp、ΔKi、ΔKdContraction-expansion factor determined by (22), (23), (24) formula.
βp=2 | e | (22)
βd=2 | e | (24)
In formula, Δ Kp、ΔKi、ΔKdRatio, integral, differential coefficient variable quantity, βpFor Δ KpContraction-expansion factor, βiFor Δ KiContraction-expansion factor, βdFor Δ KdContraction-expansion factor.
Further, if taking the quantification gradation of deviation and deviation variation rate is seven grades, obscuring domain is [- 6 6], Using identical method, Kp can be taken, the quantification gradation of Ki, Kd obtain corresponding fuzzy domain;If taking seven fuzzy sons Collection, then linguistic variable be in negative big, negative, bear it is small, zero, it is honest, center, just small.
It advantages of the present invention and has the beneficial effect that:
The present invention establishes large scale wind power machine system model, and analysis system feature is connected using pitch control and direct torque With the composite control method that parallel connection combines.Since the amplitude and frequency of wind speed variation are to influence which kind of control mode taken Key factor, therefore amplitude and frequency are used as to the judgment basis of controlling tactic switch simultaneously.Since PID controller parameter is initial Value is empirically determined mostly, if selection is improper, may cause physical structure damage.Therefore, artificial bee colony algorithm is used first Offline optimization is carried out to controller parameter.On the basis of obtained Optimal Parameters, according to wind speed situation of change, using variable universe mould Inference mode is pasted to adjust dynamic state of parameters.In this way, reduce the overshoot of system initial time, improve controller in width Performance when range, high-frequency variation.
Compared with the existing technology, the innovation of the invention consists in that:
(1) using pitch control with direct torque series connection and the Compound Control Strategy in parallel combined.Specific steps are as follows: when After wind speed overrate, if wind speed variation amplitude is larger, output power is reduced rapidly using pitch control first, reduced To after specified range, keep variable pitch angle at this time, then output power is adjusted with direct torque, this for pitch control with The cascade of direct torque;If wind speed variation amplitude is smaller and change frequency is fast, direct torque is directlyed adopt, this is simultaneously The direct torque of connection form.
(2) offline optimization is carried out to PID controller parameter using artificial bee colony algorithm, obtains controller parameter initial time Value.Specific steps are as follows: after to new nectar source, lead bee in a particular manner by nectar source position information share to information exchange Area, to follow bee to provide information.It follows bee to judge the quality in nectar source in a particular manner, determines target.It is next finding When a nectar source, leads bee to become searching for bee, and find nectar source to search for bee, lead bee shared information, bee determination is followed finally to adopt The process of sweet target is recycled, and achievees the purpose that obtain best nectar source, here it is the searching processes of pid parameter initial value.
(3) on-line control is carried out to PID controller parameter using variable universe fuzzy rule, improves controller adaptability.Tool Body step are as follows: on the basis of the pid parameter initial value that artificial bee colony obtains, on-line tuning is carried out to parameter using fuzzy rule, With the reduction of control error, fuzzy domain is adjusted in real time with the contraction-expansion factor of consecutive variations, accords with fuzzy reasoning more Close control process feature.
Detailed description of the invention
Fig. 1 is that the present invention provides preferred embodiment wind energy conversion system output power strategy block diagram;
Fig. 2 is wind energy conversion system output power analogous diagram.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, detailed Carefully describe.Described embodiment is only a part of the embodiments of the present invention.
The technical solution that the present invention solves above-mentioned technical problem is:
(1) the wind energy conversion system variable pitch based on artificial bee colony adjusts PID controller parameter optimization design
After wind speed overrate, due to the requirement of wind energy conversion system physical structure, it is necessary to which output power value is maintained at rated value. The wind energy of wind energy conversion system capture can indicate [9] with formula (1).
In formula, PaTo export shaft power, ρ is atmospheric density, and A is swept area of rotor, CpFor energy conversion factor, λ is leaf Tip-speed ratio, β are propeller pitch angle, ωrFor wind speed round, R is wind wheel radius, and V is wind speed.
As can be seen that swept area of rotor is definite value, and atmospheric density, which can be generally thought, is after wind energy conversion system determines from formula Certain value.Therefore, output power is determined by energy conversion factor and wind speed.Wind speed can not be arbitrarily controlled produced by nature. Energy conversion factor is determined by tip speed ratio and propeller pitch angle, and tip speed ratio is determined by wind speed round and wind speed, wind wheel therein Revolving speed is controlled variable.Therefore, the output power of wind energy conversion system is mainly determined by two controllable variables of propeller pitch angle and wind speed round, The control of output power can be realized by adjusting propeller pitch angle and wind speed round.But the running speed of motor in practice has Certain range should be remained unchanged when revolving speed reaches rated value, be merely able to change in a small range.Since wind energy conversion system is by passing Dynamic chain drives motor rotation, and motor speed reacts on wind wheel after reaching rated value, so that wind speed round also keeps constant value.By After wind speed overrate, motor will reach rated speed, can only change in small range.Therefore, propeller pitch angle size is controlled As the major way for adjusting output power.But large scale wind power machine blade is long, quality is big, inertia is big, is adjusting variable pitch angle During output power will appear fluctuation.And motor rotary inertia is small, direct torque is easy to accomplish.Therefore, by pitch control It controls with motor torque and is combined with connecting with parallel connection, for improving system control effect.Compound Control Strategy is as shown in Figure 1.
By transmission chain link between wind wheel and motor, it is known as low speed shaft end close to wind wheel side, is known as height close to motor side Fast shaft end.In terms of low speed shaft end, there are following relationship [10]:
In formula, JrFor wind wheel rotary inertia, TaFor wind energy conversion system pneumatic torque, TlsFor low speed shaft torque, KrTo be hindered outside wind wheel Buddhist nun.When not considering out-damping, KrωrIt can omit.
In terms of high speed shaft end, there are following relationship [10]:
In formula, JgFor motor rotary inertia, ωgFor motor speed, ThsFor high speed shaft torque, TgFor electromagnetic torque, KgFor electricity Machine out-damping.When not considering out-damping, KgωgIt can omit.
Relationship between high speed shaft and slow-speed shaft is connected with transmission ratio, and transmission system is thought of as rigidity, uses formula (6) it indicates.
Wherein, k is gear ratio.
Due to the pure lag characteristic of hydraulic variable propeller system, it can be denoted as first order inertial loop, indicated with formula (7).
In formula, TβFor time constant, βrIt (is given by controller) for propeller pitch angle reference value, β is state variable.
According to the analysis above on wind turbine system structure and the factor for influencing output power, in wind speed overrate Afterwards, the strategy combined when wind speed amplitude of variation is larger using variable pitch and direct torque is determined, when wind speed amplitude of variation is smaller And change frequency it is higher when use direct torque.PID controller has many advantages, such as that structure is simple, is easily achieved, good reliability, extensively It is general to be applied to industrial control field.In order to reduce accumulated error, the present invention uses incremental timestamp device, and mathematical model can It is expressed with following formula.
If the control amount at n-1 moment are as follows:
In formula, e is the deviation at corresponding moment, and T is using time interval, TiFor integration time constant, TdIt is normal for derivative time Number.
If
Δ u (n)=u (n)-u (n-1) (9)
So, Δ u (n) may be expressed as: again
It enablesFor integral coefficient,For differential coefficient, formula (10) can simplify are as follows:
Δ u (n)=Kp[e(n)-e(n-1)]+Kie(n)+Kd[e(n)-2e(n-1)+e(n-2)] (11)
PID controller possesses lot of advantages, and still, there are two the problem of aspect in practical applications to need to solve.First It is the setting of controller initial value, in actual condition, due to mechanical, electrical aspect requirement, each components have receiving The limit of external impact and overload, if it exceeds the limit, then can cause the damage of components.It finds in simulations, PID controller The setting of initial value directly determines that the overshoot of output valve, overshoot can cause to impact to system.Followed by ratio, differential, product The on-line tuning for dividing coefficient, since PID controller is to carry out control output according to deviation and deviation variation rate, then the change of wind speed Change amplitude and change frequency are the determinants of determining controller parameter, if controller parameter is unable to on-line tuning, Under wind speed amplitude and the continually changing situation of frequency, one group of preset parameter obviously cannot be met the requirements.
For the initial value design problem in PID controller use process, the present invention joins control using artificial bee colony algorithm Number carries out off-line optimization and determines.Artificial bee colony algorithm [11-12] is a kind of new intelligent optimization that Karaboga in 2005 is proposed Algorithm has the characteristics that structure is simple, control parameter is few, strong robustness, global optimizing ability are strong, moreover, to objective function and Constraint is almost without requiring.Therefore, it is used successfully to the fields such as neural network structure optimization, multiple-objection optimization in recent years.Artificial bee Group's algorithm is to simulate bee colony in nature to find the whole process of nectar source progress gathering honey and propose.Honeybee producting honey by lead bee, Follow the searching completed to nectar source of sharing out the work and help one another between bee, search bee.Firstly, by specific after leading bee to search nectar source Flight path mode pass the information on out, in these information include nectar source quality information.Then, follow bee according to from drawing The information selection nectar source for leading bee to obtain carries out gathering honey.When nectar source has not had extraction value, leads bee to make and abandon exploitation Decision begins look for new nectar source, at this point, leading the diversification in role of bee for search bee.When searching out the new nectar source of high quality, search Changing role is to lead bee, and nectar source relevant information is passed out to rope bee again.A process in this way, bee colony realize honey Maximization exploitation.
According to bee colony gathering honey process, optimization method can be abstracted as mathematical model.The position in nectar source is the key that gathering honey letter Breath, represents the potential solution of problem, the position of nectar source i is represented by formula (12).
In formula, t indicates current iteration number, k representation dimension.
The initial position of nectar source i may be expressed as:
xik=Lk+rand(0,1)(Uk-Lk) (13)
In formula, LkIndicate the lower limit of search space, UkIndicate the upper limit of search space, rand (0,1) is generated between 0 to 1 Random number.
Bee is led to indicate the new bee source position formula (14) searched.
vik=xik+r(xik-xnk) (14)
In formula, r is equally distributed random number between [- 1,1], and n ∈ { 1,2 ... }, n indicate to select one in nectar source Nectar source not equal to i.
After leading bee to determine the location information in new nectar source, flies back and carry out nectar source information sharing to specified region, follow bee It is followed according to the nectar source information obtained, probability formula (15) is followed to indicate.
In formula, q indicates nectar source sum, fitiIt indicates fitness (corresponding with the quality in nectar source).
Fitness can be indicated with formula (16).
In formula, fiIndicate the functional value of solution.
The fitness in new bee source is evaluated according to fitness expression formula (16), is determined and is retained bee source.By such process, honey Bee completes the searching to best bee source.This process namely using artificial bee colony algorithm to the optimization process of PID parameter.
(2) the PID controller parameter on-line tuning algorithm based on variable universe fuzzy reasoning
By the optimization of artificial bee colony algorithm, the parameter value of PID controller is obtained, makes initial time system state variables Overshoot is greatly reduced, and reduces the impact to mechanical electric structure, can extend the service life of system.But PID control Device is controlled according to deviation and deviation variation rate, and control parameter characteristic is described by taking proportionality coefficient as an example.It is inclined when existing When poor, proportional component generates control action, and to eliminate deviation, the size of proportionality coefficient influences to eliminate the speed of deviation.Due to Wind speed may all change at any time, therefore the size of deviation has differences.Deviation is of different sizes, if using the same ratio Coefficient, then, the time for eliminating deviation will not be suitable duration.Therefore, in order to improve controller performance, using fuzzy rule On-line tuning is carried out to PID controller parameter.
Fuzzy controller [13] is inputted using error and error rate as fuzzy controller, since input is specific The deviation and deviation variables rate of physical quantity, will carry out fuzzy reasoning, it is necessary first to convert fuzzy theory for the basic domain of input Domain takes quantizing factor appropriate, basic domain can be converted to fuzzy domain.Quantizing factor can be determined with formula (17), (18).
In formula, KcFor the quantizing factor of deviation, KecFor the quantizing factor of deviation variation rate, emaxFor maximum deviation, eminFor Minimum deflection, ecmaxFor maximum deviation change rate, ecminFor minimum deflection change rate.
If taking the quantification gradation of deviation and deviation variation rate is seven grades, obscuring domain is [- 6 6].Using identical Method, Kp, the quantification gradation of Ki, Kd, available corresponding fuzzy domain can be taken.If taking seven fuzzy subsets, that Linguistic variable be in negative big, negative, bear it is small, zero, it is honest, center, just small.It should be noted that linguistic variable acquirement is more, It is more detailed to the expression of process, but fuzzy rule is also more, and reasoning process is more complicated.Therefore, it is necessary to choose according to the actual situation Fuzzy rule number, should consider control accuracy, also to consider fuzzy reasoning speed.
Value in domain is integrated into fuzzy subset by subordinating degree function.The ununified side of the determination of subordinating degree function Method, common subordinating degree function mainly have Gauss subordinating degree function, triangle subordinating degree function etc..The variation of Gauss subordinating degree function Smoother, the variation of triangle subordinating degree function is more violent.Therefore, when input deviation and more gentle deviation variation rate variation, Select Gauss subordinating degree function;When input deviation and very fast deviation variation rate variation, triangle subordinating degree function is selected.
Value after fuzzy control is converted to practical control output, the ratio that anti fuzzy method uses after anti fuzzy method The example factor is determined using formula (19).
In formula, Δ KmaxFor the actual change maximum value of the respective domain of ratio, integral, differential parameter, Δ KminFor ratio, product Point, the actual change minimum value of the respective domain of differential parameter.
In mentioned-above fuzzy reasoning process, what is taken is the constant form of domain.In fact, with control process It carrying out, error can be smaller and smaller, if still using changeless error domain, control precision can decline.In order to mention High control precision, each stage in control process use different domains [14].Specific method is on initial domain basis On be multiplied by contraction-expansion factor, in this way can be with the progress of control process, error domain is constantly adjusting, thus control can be improved Precision.Input deviation contraction-expansion factor can determine that input slew rate contraction-expansion factor can be determined with formula (21) with formula (20).
In formula, λ1、λ2∈ [0,1], ε1、ε2For arbitrarily small positive number, xemaxFor deviation domain maximum value, xecmaxFor deviation Change rate domain maximum value.For parameter lambdai、εi, the former value is bigger, and the latter's value is smaller, and controller is sensitiveer to input variation, has The influence die conducive to the control precision of the system of raising and reduction.By multiple verification experimental verification, λ is taken12=10-5、ε1= 10-5、ε2=10-6
Since output contraction-expansion factor directly influences the control precision of entire PID controller, according to Δ Kp、ΔKi、Δ KdFlexible principle as follows: output variable Δ K is established in influence to system control performancep、ΔKdThe variation of contraction-expansion factor should be with The monotonicity of error is consistent, and output variable Δ KiContraction-expansion factor should have and error dullness phase reflexive.Then Δ Kp、Δ Ki、ΔKdContraction-expansion factor determined by (22), (23), (24) formula.
βp=2 | e | (22)
βd=2 | e | (24)
In formula, βpFor Δ KpContraction-expansion factor, βiFor Δ KiContraction-expansion factor, βdFor Δ KdContraction-expansion factor.
After rated wind speed, power can be lowered when wind speed is changed greatly using variable pitch control method.But It is that, since large scale wind power machine blade is long, quality is big, flexible and inertia influence is so that power regulation has fluctuation and lag.For This, using direct torque as the auxiliary link after pitch control.Direct torque can be with the sluggishness of faster velocity compensation variable pitch And the wave phenomenon near elimination rated power.Direct torque herein uses feedback linearization method, feedback linearization process It is identical as patent ZL201510920427.8.
(3) torque system is based on artificial bee colony and variable universe Design of Fuzzy PID Controller
When wind speed variation amplitude is smaller and when frequency is very fast, if using pitch control, control precision and frequency variation by To limitation, moreover, because the factors such as large scale wind power machine blade is long, flexible, quality is big can cause the dynamic characteristic of system to become Change.But direct torque is simply easily realized, moreover, system dynamic characteristic will not be caused to generate larger change during direct torque Change.Therefore, firm power control is carried out by the way of direct torque.Torque controller herein and the torque above used Controller different from, the as can be seen from Figure 1 difference of two torque controllers.The switch logic of pitch control and direct torque It is identical as patent ZL201510920427.8.
Based on control strategy and control algolithm proposed by the invention, simulating, verifying has been carried out.Fig. 2 is the constant function of wind energy conversion system (in figure: ABC-PID is the PID controller using artificial bee colony algorithm to the control simulation result of rate output, and ABC-VF-PID is Using the PID controller of artificial bee colony and variable universe fuzzy algorithmic approach).Wind turbine system parameter are as follows: rated power Pa=3MW, wind Take turns radius R=47.5m, rated wind speed V=13m/s, transmission ratio k=80, wind wheel rotary inertia Jr=6250000kgm2, motor Rotary inertia Jg=15kgm2.The PID controller initial parameter obtained after ant colony algorithm optimizing are as follows: Kp=115.6, Ki= 93.1 Kd=3.4356.During carrying out ratio, integral, differential parameter on-line tuning using fuzzy reasoning, basic domain power Deviation E is [0 20KW], and deviation variation rate EC is [- 0.015 0.04], and corresponding fuzzy domain is [- 3 3].KpMould Pasting domain is [- 6 6], KiFuzzy domain be [- 3 3], KdFuzzy domain be [- 1 1].Subordinating degree function selects triangle letter Several and Gaussian function combining form, and use gravity model appoach ambiguity solution.ΔKp、ΔKi、ΔKdFuzzy rule inference rule be shown in Table 1.It carries out variable universe transformation again on this basis, improves the adaptability of controller.Simulation result shows proposed by the present invention ABC-VF-PID control algolithm can effectively control output power, and control effect is substantially better than ABC-PID algorithm.
1 Δ K of tablep、ΔKiΔKdFuzzy inference rule table
The above embodiment is interpreted as being merely to illustrate the present invention rather than limit the scope of the invention.? After the content for having read record of the invention, technical staff can be made various changes or modifications the present invention, these equivalent changes Change and modification equally falls into the scope of the claims in the present invention.

Claims (7)

1. a kind of constant output control method of large scale wind power machine, which comprises the following steps:
Firstly, proposing pitch control and torque control on the basis of analyzing wind turbine system influences power output state variable Make the Compound Control Strategy that combines, i.e., more than rated wind speed, when wind speed changes amplitude and is greater than limit value, with pitch control and The concatenated mode of direct torque is controlled;Wind speed variation amplitude is less than limit value and when change frequency is greater than the set value f, to turn The mode of square control is controlled;
Then, PID controller initial parameter is optimized using artificial bee colony algorithm;
Finally, design fuzzy inference rule carries out on-line tuning to pid parameter, by being multiplied by contraction-expansion factor, in a manner of variable universe Domain is adjusted.
2. a kind of constant output control method of large scale wind power machine according to claim 1, which is characterized in that described It is controlled in such a way that pitch control and direct torque are concatenated when the above wind speed variation amplitude of rated wind speed is larger, it is specific to wrap It includes:
When wind speed variation amplitude is greater than limit value, power is controlled rapidly in the range of setting using pitch control first, And the variable pitch angle of holding at this time;Then, the response speed of control system is improved using direct torque mode, reduces power waves It is dynamic.
3. a kind of constant output control method of large scale wind power machine according to claim 1, which is characterized in that in wind Speed variation amplitude is controlled in a manner of direct torque less than limit value and when change frequency is greater than the set value f, specific to wrap It includes:
The drawbacks of analysis uses pitch control when wind speed variation amplitude is less than limit value and change frequency is greater than limit value first The advantages of with using direct torque, and combined with artificial bee colony algorithm and variable universe fuzzy algorithmic approach, improve Control system resolution.
4. a kind of constant output control method of large scale wind power machine according to claim 1, which is characterized in that described PID controller initial parameter is optimized using artificial bee colony algorithm, is specifically included:
The position in nectar source is the key message of gathering honey, represents the potential solution of problem, and the position of nectar source i is represented by formula (12);
In formula, t expression current iteration number, k representation dimension,Indicate k location of the nectar source i in t iteration;
The initial position of nectar source i may be expressed as:
xik=Lk+rand(0,1)(Uk-Lk) (13)
In formula, LkIndicate the lower limit of search space, UkIndicate the upper limit of search space, rand (0,1) generates random between 0 to 1 Number;
Bee is led to indicate the new bee source position formula (14) searched;
vik=xik+r(xik-xnk) (14)
In formula, r is equally distributed random number between [- 1,1], and n ∈ { 1,2 ... }, n indicate that selecting one in nectar source differs In the nectar source of i;
After leading bee to determine the location information in new nectar source, flies back and carry out nectar source information sharing to specified region, follow bee foundation The nectar source information of acquisition is followed, and probability formula (15) is followed to indicate;
In formula, q indicates nectar source sum, fitiIndicate fitness;
Fitness can be indicated with formula (16).
In formula, fiIndicate the functional value of solution;
The fitness in new bee source is evaluated according to fitness expression formula (16), is determined and is retained bee source, by such process, honeybee is complete The searching in best bee source in pairs.This process namely using artificial bee colony algorithm to the optimization process of pid parameter.
5. a kind of constant output control method of large scale wind power machine according to claim 4, which is characterized in that described It designs fuzzy inference rule and on-line tuning is carried out to pid parameter, and variable universe is replaced with to the fixation domain of fuzzy reasoning, specifically Include:
Fuzzy controller is inputted using error and error rate as fuzzy controller, it is necessary first to by the basic domain of input It is converted into fuzzy domain, takes quantizing factor appropriate, basic domain is converted into fuzzy domain.Quantizing factor can with formula (17), (18) it determines;
In formula, KcFor the quantizing factor of deviation, KecFor the quantizing factor of deviation variation rate, emaxFor maximum deviation, eminFor minimum Deviation, ecmaxFor maximum deviation change rate, ecminFor minimum deflection change rate;
Value after fuzzy control, is converted to practical control output after anti fuzzy method, the ratio that anti fuzzy method uses because Son is determined using formula (19);
In formula, Δ KmaxFor the actual change maximum value of the respective domain of ratio, integral, differential parameter, Δ KminFor ratio, integral, The actual change minimum value of the respective domain of differential parameter;
Contraction-expansion factor is multiplied by the basis of initial domain, input deviation contraction-expansion factor can determine that input slew rate is stretched with formula (20) The contracting factor can be determined with formula (21);
In formula, λ1、λ2∈ [0,1], ε1、ε2For arbitrarily small positive number.xemaxIndicate deviation domain maximum value, xecmaxIndicate that deviation becomes Rate domain maximum value.
6. a kind of constant output control method of large scale wind power machine according to claim 5, which is characterized in that Δ Kp、 ΔKi、ΔKdContraction-expansion factor determined by (22), (23), (24) formula.
βp=2 | e | (22)
βd=2 | e | (24)
In formula, Δ Kp、ΔKi、ΔKdRespectively ratio, integral, differential coefficient variable quantity.βpFor Δ KpContraction-expansion factor, βiFor Δ KiContraction-expansion factor, βdFor Δ KdContraction-expansion factor.
7. a kind of constant output control method of large scale wind power machine according to claim 5, which is characterized in that if Taking the quantification gradation of deviation and deviation variation rate is seven grades, then fuzzy domain is [- 6 6], using identical method, Ke Yiqu The quantification gradation of Kp, Ki, Kd obtain corresponding fuzzy domain;If taking seven fuzzy subsets, linguistic variable be it is negative big, In negative, bear it is small, zero, it is honest, center, just small.
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