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 PDFInfo
- Publication number
- 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
- Authority
- CN
- China
- Prior art keywords
- formula
- fuzzy
- domain
- control
- deviation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 62
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 28
- 238000011217 control strategy Methods 0.000 claims abstract description 8
- 150000001875 compounds Chemical class 0.000 claims abstract description 6
- 241000256844 Apis mellifera Species 0.000 claims description 69
- 230000008859 change Effects 0.000 claims description 37
- 230000008569 process Effects 0.000 claims description 25
- 238000005457 optimization Methods 0.000 claims description 12
- 238000013461 design Methods 0.000 claims description 10
- 235000012907 honey Nutrition 0.000 claims description 8
- 238000011002 quantification Methods 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000013459 approach Methods 0.000 claims description 3
- 230000004044 response Effects 0.000 claims description 2
- 230000000717 retained effect Effects 0.000 claims description 2
- 238000006243 chemical reaction Methods 0.000 abstract description 19
- 230000003044 adaptive effect Effects 0.000 abstract description 4
- 230000005540 biological transmission Effects 0.000 description 4
- 238000013016 damping Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 230000007812 deficiency Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 235000009508 confectionery Nutrition 0.000 description 1
- 238000012885 constant function Methods 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 210000004218 nerve net Anatomy 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- 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
- F03D7/0236—Adjusting aerodynamic properties of the blades by changing the active surface of the wind engaging parts, e.g. reefing or furling
-
- 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
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
-
- 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
- F03D7/0256—Stall control
-
- 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
- F03D7/0264—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor for stopping; controlling in emergency situations
- F03D7/0268—Parking or storm protection
-
- 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
- F03D7/04—Automatic control; Regulation
- F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
- F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
- F03D7/044—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with PID control
-
- Y—GENERAL 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
Landscapes
- 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
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 taken1=λ2=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.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201811008114.5A CN109209768B (en) | 2018-08-31 | 2018-08-31 | Constant output power control method for large wind turbine |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201811008114.5A CN109209768B (en) | 2018-08-31 | 2018-08-31 | Constant output power control method for large wind turbine |
Publications (2)
Publication Number | Publication Date |
---|---|
CN109209768A true CN109209768A (en) | 2019-01-15 |
CN109209768B CN109209768B (en) | 2020-06-16 |
Family
ID=64985362
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201811008114.5A Active CN109209768B (en) | 2018-08-31 | 2018-08-31 | Constant output power control method for large wind turbine |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN109209768B (en) |
Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110083195A (en) * | 2019-03-29 | 2019-08-02 | 广东工业大学 | A kind of Poewr control method based on the wave-power device for improving ant colony algorithm |
CN110362148A (en) * | 2019-07-19 | 2019-10-22 | 昆明理工大学 | Photovoltaic system maximum power tracking method under a kind of shadowed condition based on artificial bee colony algorithm |
CN111513839A (en) * | 2020-04-30 | 2020-08-11 | 湖南菁益医疗科技有限公司 | Electrosurgical system and control method |
CN113485694A (en) * | 2021-07-06 | 2021-10-08 | 算话信息科技(上海)有限公司 | Variable data intelligent middle station system of algorithm |
CN113776911A (en) * | 2021-09-15 | 2021-12-10 | 镇江市科瑞制样设备有限公司 | Intelligent control method of sampling machine with self-adaptive division opening and speed |
CN114510092A (en) * | 2022-02-17 | 2022-05-17 | 太原理工大学 | Transition packet internal temperature control system and method based on fuzzy PID (proportion integration differentiation) of prediction variable universe |
CN115030858A (en) * | 2022-05-16 | 2022-09-09 | 西安交通大学 | Distributed ocean current energy water turbine control power generation system based on cluster intelligent optimization |
CN115199471A (en) * | 2022-06-24 | 2022-10-18 | 兰州理工大学 | Power control method and system based on yaw variable pitch linkage control load shedding |
WO2023045272A1 (en) * | 2021-09-22 | 2023-03-30 | 北京金风科创风电设备有限公司 | Wind-storage combined frequency regulation method and wind-storage combined frequency regulation apparatus |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101196165A (en) * | 2007-12-13 | 2008-06-11 | 苏州市南极风能源设备有限公司 | Regulation control of wind generating set |
CN101603502A (en) * | 2008-06-11 | 2009-12-16 | 武汉事达电气股份有限公司 | A kind of wind energy control method based on artificial-intelligent |
CN101660489A (en) * | 2009-09-23 | 2010-03-03 | 南京盛唐电力控制系统有限公司 | Megawatt wind generating set combination control policy |
CN102720634A (en) * | 2012-07-09 | 2012-10-10 | 兰州交通大学 | Variable universe fuzzy electric pitch control method for optimizing parameters |
CN105508135A (en) * | 2015-12-14 | 2016-04-20 | 沈阳华创风能有限公司 | Variable pitch control method based on combination of fuzzy feedforward and fuzzy PID control |
-
2018
- 2018-08-31 CN CN201811008114.5A patent/CN109209768B/en active Active
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101196165A (en) * | 2007-12-13 | 2008-06-11 | 苏州市南极风能源设备有限公司 | Regulation control of wind generating set |
CN101603502A (en) * | 2008-06-11 | 2009-12-16 | 武汉事达电气股份有限公司 | A kind of wind energy control method based on artificial-intelligent |
CN101660489A (en) * | 2009-09-23 | 2010-03-03 | 南京盛唐电力控制系统有限公司 | Megawatt wind generating set combination control policy |
CN102720634A (en) * | 2012-07-09 | 2012-10-10 | 兰州交通大学 | Variable universe fuzzy electric pitch control method for optimizing parameters |
CN105508135A (en) * | 2015-12-14 | 2016-04-20 | 沈阳华创风能有限公司 | Variable pitch control method based on combination of fuzzy feedforward and fuzzy PID control |
Cited By (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110083195B (en) * | 2019-03-29 | 2021-08-24 | 广东工业大学 | Power control method of wave power generation device based on improved bee colony algorithm |
CN110083195A (en) * | 2019-03-29 | 2019-08-02 | 广东工业大学 | A kind of Poewr control method based on the wave-power device for improving ant colony algorithm |
CN110362148A (en) * | 2019-07-19 | 2019-10-22 | 昆明理工大学 | Photovoltaic system maximum power tracking method under a kind of shadowed condition based on artificial bee colony algorithm |
CN111513839A (en) * | 2020-04-30 | 2020-08-11 | 湖南菁益医疗科技有限公司 | Electrosurgical system and control method |
CN113485694A (en) * | 2021-07-06 | 2021-10-08 | 算话信息科技(上海)有限公司 | Variable data intelligent middle station system of algorithm |
CN113485694B (en) * | 2021-07-06 | 2023-04-28 | 算话信息科技(上海)有限公司 | Variable data intelligent middle platform system of algorithm |
CN113776911A (en) * | 2021-09-15 | 2021-12-10 | 镇江市科瑞制样设备有限公司 | Intelligent control method of sampling machine with self-adaptive division opening and speed |
CN113776911B (en) * | 2021-09-15 | 2024-05-03 | 镇江市科瑞制样设备有限公司 | Intelligent control method of sampling machine with adaptive reduction opening degree and speed |
WO2023045272A1 (en) * | 2021-09-22 | 2023-03-30 | 北京金风科创风电设备有限公司 | Wind-storage combined frequency regulation method and wind-storage combined frequency regulation apparatus |
CN114510092A (en) * | 2022-02-17 | 2022-05-17 | 太原理工大学 | Transition packet internal temperature control system and method based on fuzzy PID (proportion integration differentiation) of prediction variable universe |
CN114510092B (en) * | 2022-02-17 | 2023-02-10 | 太原理工大学 | Transition packet internal temperature control system and method based on fuzzy PID (proportion integration differentiation) of prediction variable universe |
CN115030858A (en) * | 2022-05-16 | 2022-09-09 | 西安交通大学 | Distributed ocean current energy water turbine control power generation system based on cluster intelligent optimization |
CN115199471A (en) * | 2022-06-24 | 2022-10-18 | 兰州理工大学 | Power control method and system based on yaw variable pitch linkage control load shedding |
CN115199471B (en) * | 2022-06-24 | 2024-05-31 | 兰州理工大学 | Power control method and system for controlling load reduction based on yaw variable pitch linkage |
Also Published As
Publication number | Publication date |
---|---|
CN109209768B (en) | 2020-06-16 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN109209768A (en) | A kind of constant output control method of large scale wind power machine | |
Abdelbaky et al. | Design and implementation of partial offline fuzzy model-predictive pitch controller for large-scale wind-turbines | |
Mousavi et al. | Sliding mode control of wind energy conversion systems: Trends and applications | |
Nadour et al. | Comparative analysis between PI & backstepping control strategies of DFIG driven by wind turbine | |
CN101603502B (en) | Wind energy control method based on artificial intelligence | |
Kahla et al. | Maximum power point tracking of wind energy conversion system using multi-objective grey wolf optimization of fuzzy-sliding mode controller | |
Shanmugam et al. | Stabilization of permanent magnet synchronous generator-based wind turbine system via fuzzy-based sampled-data control approach | |
Stol | Disturbance tracking and blade load control of wind turbines in variable-speed operation | |
Hosseini et al. | Partial-or full-power production in WECS: A survey of control and structural strategies | |
Hamoodi et al. | Pitch angle control of wind turbine using adaptive fuzzy-PID controller | |
CN102900603B (en) | Variable pitch controller design method based on finite time non-crisp/guaranteed-cost stable wind turbine generator set | |
Ali et al. | Comparative study of different pitch angle control strategies for DFIG based on wind energy conversion system | |
CN112418553A (en) | Offshore wind power control method based on VMD-CNN network | |
Goyal et al. | Power regulation of a wind turbine using adaptive fuzzy-PID pitch angle controller | |
Manna et al. | A review of control techniques for wind energy conversion system | |
Almaged et al. | Design of an integral fuzzy logic controller for a variable-speed wind turbine model | |
Hai et al. | A novel intelligent method to increase accuracy of hybrid photovoltaic-wind system-based MPPT and pitch angle controller | |
Yao et al. | Pitch angle control of variable pitch wind turbines based on neural network PID | |
Puangdownreong | Optimal PIλDµ controller design based on spiritual search for wind turbine systems | |
Shu et al. | A wind farm coordinated controller for power optimization | |
Ayrir et al. | Fuzzy logic rotor currents control of a DFIG-based wind turbine | |
Ravikumar et al. | Robust controller design for speed regulation of a flexible wind turbine | |
Tan et al. | A Review on Pitch Angle Control Strategy of Variable Pitch Wind Turbines | |
Ayub et al. | Nonlinear super-twisting based speed control of PMSG-ECS using higher order sliding mode control | |
CN102900606B (en) | Wind turbine generator set variable pitch controller design method based on finite time guaranteed cost stabilization |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |