CN108547735B - The integrated optimization control method of wind power plant active output and unit fatigue - Google Patents
The integrated optimization control method of wind power plant active output and unit fatigue Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
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- 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
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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- Y02P70/50—Manufacturing or production processes characterised by the final manufactured product
Abstract
The invention discloses the integrated optimization control methods of wind power plant active output and unit fatigue, by carrying out DEL data modeling to the Wind turbines component under active shaping modes, and the wind power plant optimal control of complicated landform are carried out based on the DEL data model；Wherein, the Compound Control Strategy that control strategy uses the active optimization smart allocation predicted based on wind regime feature to combine with active auxiliary adjustment；Therefore one aspect of the present invention, pass through the integrated optimization control method of wind power plant active adjusting and unit fatigue, the manufacturing and maintenance cost of wind power plant is effectively reduced, solves the problems, such as the grid-connected consumption of generation of electricity by new energy distributing based on wind power plant；On the other hand, the integrated optimization control method of the active adjusting of the wind power plant and unit fatigue is suitable for the wind power plant of the lesser complicated landform of wake effect.
Description
Technical field
The present invention relates to technical field of wind power, specifically, being related to a kind of complexity place small suitable for wake effect
The integrated optimization control method of the wind power plant of wind power plant active output and unit fatigue.
Background technique
In China, as ideal wind power plant development of resources is petered out, it is high rapid that land Wind Power Development has turned to low wind speed
The complicated landform wind power plant of stream.According to statistics, available low wind speed resource area accounts for national wind energy resources area in the whole country
68%；And it is all complicated landform that these low wind speed areas are most of, and close to the receiving end area of network load.With simple landform
The well-regulated arrangement of wind power plant Wind turbines tool is compared, and complicated landform wind power plant unit has significant difference in arrangement.For
Complicated landform wind power plant will carry out active power regulation with certain difficulty to it, and geographical location difference makes wind power plant each
There are larger differences for unit wind regime, and then result between unit using wind power with larger difference.Therefore, how will be electric
Netting active demand and can carrying out reasonable distribution using wind power according to each unit of wind power plant is the active adjusting of complicated landform wind power plant
Important technological problems.
To carry out efficiently active adjusting to complicated landform wind power plant, the distribution of work can be had using the estimation of wind power based on unit
Method and active closed loop control method based on PI algorithm are suggested.However, the above method is solving active regulation problem
Meanwhile but bringing new problem: it is tired between complicated landform wind power plant unit only to consider that the allocation strategy of active demand results in
Labor distribution is serious uneven, mean wind speed and turbulence intensity it is high the other units of machine group parts fatigue loading ratio it is much larger, most final minification
Short service life.
Therefore, the integrated optimization control method of the wind power plant active output and unit fatigue of a kind of complicated landform is now provided.
Summary of the invention
For this purpose, the present invention provides a kind of wind power plant the active integrated optimization control method exported with unit fatigue, including with
Lower step:
S1, it is based on Wind turbines fatigue load simulation software, establishes wind turbine model and sets artificial variable input group
It closes, obtains and calculate the DEL data set of the machine group parts under different input combinations, wherein
Wherein, DEL is (Damage Equivalent Load), indicates damage equivalent load, niWithTo pass through rain stream
The recurring number and circulation amplitude that counting method is calculated；T is load history assessment cycle；F is given sinusoidal loading frequency
Rate；M is the characterisitic parameter of material；
S2, skewness and kurtosis computing index are carried out to the DEL data set of several machine group parts under different input combinations
It calculates, and is selected with symmetrical and spike distribution characteristics DEL data set according to calculated result as optimization object；
S3, quantization unit control strategy and the active influence degree set to machine group parts fatigue, as optimization object
DEL data set in, the unit Partial controll plan that selects the lesser unit allocation strategy of machine group parts DEL to control as wind power plant
Slightly, it and selects DEL data to the more sensitive machine group parts of active setting as target component, and then selects unit local controlled
The DEL data set of target component under system strategy is for modeling；
S4, the DEL data set for the target component under unit Partial controll strategy under different input combinations, using formula
(2) Density Estimator method shown in calculates separately their distribution density function:
Wherein, K () is kernel function, and h is bandwidth, and x is the DEL data of target component, XiFor the number of corresponding DEL data set
According to unit, n is the data amount check that DEL data set includes；
S5, formula (2) are based on, calculate DEL distribution density function maximum value and its corresponding DEL virtual value DEL (eq):
S6, according to formula (2) and formula (3), calculate DEL (eq) data sets of target component under different input combinations, and press
According to turbulence intensity TI to data set carry out taxonomic revision, thus obtain under different TI by mean wind speedWith active setting Pset
DEL (eq) data form that two inputs determine；It is quasi- that function is carried out to DEL (eq) data form by the way of surface fitting
It closes:
Wherein, ai,j(i=0...k, j=0...l) is the data for needing to be fitted；
If quantity >=2 of S7, target component, repeatedly S4, S5 and S6, number of repetition are that the quantity of target component subtracts 1 time；
S8, the complex optimal controlled strategy to realize wind power plant machine group parts active output and unit fatigue, by optimization problem table
It states are as follows:
0≤Pset(j)≤Pavail(j), j=1 ..., D (6b)
Wherein, D indicates that wind power plant unit number, j are jth unit, DELiIt (j) is No. i-th component of jth Wind turbines
DEL estimated value,For the DEL average value of wind power plant No. i-th component of all units, i=1 is expressed as tired distribution optimization
Component, PrefFor active regulatory demand, PsetIt (j) is the active setting value of jth Wind turbines, PavailIt (j) is jth Wind turbines
Using wind power, M indicates the M component sensitive to active output, σ1For the power deviation range of permission, σiBetween unit
The deviation range that No. i-th component fatigue load allows；
S9, the control strategy are that the active optimization smart allocation predicted based on wind regime feature is mutually tied with active auxiliary adjustment
The Compound Control Strategy of conjunction；
For the active optimization smart allocation strategy predicted based on wind regime feature, using time series forecasting algorithm to input
Input variable in combination is predicted, and predicted value is substituted into formula (4) and formula (6), calculates separately out next optimizing cycle
Component DEL predicted value and each unit can utilize wind power prediction value；Then solution P is carried out using particle swarm intelligence algorithmset
(j) after, by Pset(j) it distributes to wind power plant partial control system；
Active auxiliary adjustment strategy is only more than σ in the deviation of the active output of wind power plant and requirements1When be activated, specifically retouch
It states as follows:
The active output mean value in real-time sampling wind power plant each unit sampling period in communication mechanism allowed band calculates wind
Deviation delta P between the active total output of electric field and active requirements,
Pout(j) actual value of the active output mean value of wind power plant jth Wind turbines is indicated；
P is obtained after handling Δ Pex,
By PexAs the input of PI controller, the output of PI controller obtains P divided by unit quantityadd；By Padd+Pset(j) make
For the output setting value of every unit, wherein Pset(j) the active optimization intelligence to be set as predicting based on wind regime feature with control strategy
P obtained when energy allocation strategyset(j)。
In S1, wind generation set control strategy controls the active output of Wind turbines according to active regulating command, and design is based on
The Wind turbines novel active regulation and control system of equivalent wind speed estimation realizes that the Wind turbines under different set revolving speed are active
The tracking of regulating command.
In S2, machine group parts include turbines vane, wheel hub, yaw and the big component of pylon four, to the three of the four big component
The DEL data set of axle power square Mx, My and the Mz in a direction carry out skewness and kurtosis computing index is calculated, the DEL data set
Include n data.
In S9, P is solved by particle swarm intelligence algorithmset(j):
Population size is N,
Position of i-th of particle in search space indicates are as follows: xi=(xi1, xi2..., xiD),,
Its flying speed is expressed as: vi=(vi1, vi2..., viD),
T+1 for when, i-th of particle renewal speed and position:
vid(t+1)=wvid(t)+c1r1(pid(t)-xid(t))+c2r2(pgd(t)-xid(t)) (9a)
xid(t+1)=xid(t)+vid(t) (9b)
Wherein, i=1,2 ..., N；D=1,2 ..., D, D are Wind turbines quantity, Pid(t) it was once arrived for i-th of particle
The optimum position crossed, Pgd(t) optimum position searched at present for entire population, w is inertia weight coefficient；C1 and c2 is to add
Velocity coeffficient, r1 and r2 are the random numbers on (0,1)；
PsetIt (j) is particle position xid, corresponding fitness is
The process of particle swarm intelligence algorithm are as follows:
1) x, is initializedidAnd vid, and constrain in formula (6) range；
2), joint type (4) and (5), calculate the P of each particleid(t)；
3) P of population, is calculatedgd(t)；
4), x is updated according to formula (9)idAnd vid, constrain in the range of formula (6)；
5), the number of iterations t=t+1；
If 6), reach iteration upper limit value, optimal solution is provided；If 2) not up to iteration upper limit value, returns
And process is continued cycling through, until reaching iteration upper limit value.
In S9, when control strategy is set as the active allocation strategy based on wind regime pattern measurement, integrated based on autoregression
Sliding average algorithm (ARIMA) and the hybrid algorithm of Kalman filter (KF) carry out the input variable in input combination pre-
It surveys.
In S9, the input variable in input combination includes turbulence intensity, mean wind speed and using wind performance number.
The above technical solution of the present invention has the following advantages over the prior art:
In the present invention, by carrying out DEL data modeling to the Wind turbines component under active shaping modes, and being based on should
The wind power plant optimal control of DEL data model progress complicated landform；Wherein, control strategy is active to be predicted based on wind regime feature
Intelligent Optimal distributes the Compound Control Strategy combined with active auxiliary adjustment；Therefore on the one hand the present embodiment, passes through wind power plant
It is active adjusting and unit fatigue integrated optimization control method, be effectively reduced wind power plant the manufacturing and safeguard at
This, solves the problems, such as the grid-connected consumption of generation of electricity by new energy distributing based on wind power plant；On the other hand, the wind power plant
The integrated optimization control method of active adjusting and unit fatigue applies also for the wind power plant of the lesser complicated landform of wake effect.
Detailed description of the invention
In order to make the content of the present invention more clearly understood, it below according to specific embodiments of the present invention and combines
Attached drawing, the present invention is described in further detail, wherein
Fig. 1 is the Wind turbines component DEL data modeling technology path signal under active shaping modes of the present invention
Figure；
Fig. 2 is the complicated landform wind-powered electricity generation of the active optimization smart allocation strategy of the present invention based on the prediction of wind regime feature
Station control system structure；
Fig. 3 is the complicated landform wind power station control system structure of Compound Control Strategy of the present invention；
Fig. 4 is that wind power plant of the present invention has the distribution of work to be based on particle colony intelligence optimizing algorithm process；
Fig. 5 is the design cycle of the present invention based on ARIMA-KF hybrid prediction model.
Specific embodiment
Below in conjunction with attached drawing, detailed description of the preferred embodiments.It should be understood that this place is retouched
The specific embodiment stated is merely to illustrate and explain the present invention, and is not used to the limit value present invention.
As shown in Figure 1, the complex optimal controlled strategy side of a kind of wind power plant active output and unit fatigue provided in this embodiment
Method, comprising the following steps:
S1, it is based on Wind turbines fatigue load simulation software, establishes wind turbine model and sets artificial variable input group
It closes, obtains and calculate the DEL data set of the machine group parts under different input combinations, wherein
Wherein, DEL, that is, Damage Equivalent Load indicates damage equivalent load, niWithTo pass through rain flowmeter
The recurring number and circulation amplitude that number method is calculated；T is load history assessment cycle；F is given sinusoidal loading frequency；
M is the characterisitic parameter of material；
S2, skewness and kurtosis computing index are carried out to the DEL data set of several machine group parts under different input combinations
It calculates, and is selected with symmetrical and spike distribution characteristics DEL data set according to calculated result as optimization object；
S3, quantization unit control strategy and the active influence degree set to machine group parts fatigue, as optimization object
DEL data set in, the unit Partial controll plan that selects the lesser unit allocation strategy of machine group parts DEL to control as wind power plant
Slightly, it and selects DEL data to the more sensitive machine group parts of active setting as target component, and then selects unit local controlled
The DEL data set of target component under system strategy is for modeling；
S4, the DEL data set for the target component under unit Partial controll strategy under different input combinations, using formula
(2) Density Estimator method shown in calculates separately their distribution density function:
Wherein, K () is kernel function, and h is bandwidth, and x is the DEL data of target component, XiFor the number of corresponding DEL data set
According to unit, n is the data amount check that DEL data set includes；
S5, formula (2) are based on, calculate DEL distribution density function maximum value and its corresponding DEL virtual value DEL (eq):
S6, according to formula (2) and formula (3), calculate DEL (eq) data sets of target component under different input combinations, and press
According to turbulence intensity TI to data set carry out taxonomic revision, thus obtain under different TI by mean wind speedWith active setting Pset
DEL (eq) data form that two inputs determine；It is quasi- that function is carried out to DEL (eq) data form by the way of surface fitting
It closes:
Wherein, ai,j(i=0...k, j=0...l) is the data for needing to be fitted；
If quantity >=2 of S7, target component, repeatedly S4, S5 and S6, number of repetition are that the quantity of target component subtracts 1 time；
S8, the complex optimal controlled strategy to realize wind power plant machine group parts active output and unit fatigue, by optimization problem table
It states are as follows:
0≤Pset(j)≤Pavail(j), j=1 ..., D (6b)
Wherein, D indicates that wind power plant unit number, j are jth unit, DELiIt (j) is No. i-th component of jth Wind turbines
DEL estimated value,For the DEL average value of wind power plant No. i-th component of all units, i=1 is expressed as tired distribution optimization
Component, PrefFor active regulatory demand, PsetIt (j) is the active setting value of jth Wind turbines, PavailIt (j) is jth Wind turbines
Using wind power, M indicates the M component sensitive to active output, σ1For the power deviation range of permission, σiBetween unit
The deviation range that No. i-th component fatigue load allows；
S9, the control strategy are that the active optimization smart allocation predicted based on wind regime feature is mutually tied with active auxiliary adjustment
The Compound Control Strategy of conjunction；
For the active optimization smart allocation strategy predicted based on wind regime feature, using time series forecasting algorithm to input
Input variable in combination is predicted, and predicted value is substituted into formula (4) and formula (6), calculates separately out next optimizing cycle
Component DEL predicted value and each unit can utilize wind power prediction value；Then solution P is carried out using particle swarm intelligence algorithmset
(j) after, by Pset(j) it distributes to wind power plant partial control system；
Active auxiliary adjustment strategy is only more than σ in the deviation of the active output of wind power plant and requirements1When be activated, specifically retouch
It states as follows:
The active output mean value in real-time sampling wind power plant each unit sampling period in communication mechanism allowed band calculates wind
Deviation delta P between the active total output of electric field and active requirements,
Pout(j) actual value of the active output mean value of wind power plant jth Wind turbines is indicated；
P is obtained after handling Δ Pex,
By PexAs the input of PI controller, the output of PI controller obtains P divided by unit quantityadd；By Padd+Pset(j) make
For the output setting value of every unit, wherein Pset(j) the active optimization intelligence to be set as predicting based on wind regime feature with control strategy
P obtained when energy allocation strategyset(j)。
The present embodiment is based on the DEL by carrying out DEL data modeling to the Wind turbines component under active shaping modes
The wind power plant optimal control of data model progress complicated landform；Wherein, control strategy can be set to based on the prediction of wind regime feature
Active optimization smart allocation strategy also can be set to the complex controll that active optimization smart allocation is combined with active auxiliary adjustment
Strategy；Therefore on the one hand the present embodiment, by the integrated optimization control method of wind power plant active adjusting and unit fatigue, effectively drops
The manufacturing and maintenance cost of low wind power plant solve the generation of electricity by new energy dispersion based on wind power plant
The grid-connected consumption problem of formula；On the other hand, the integrated optimization control method of the active adjusting of the wind power plant and unit fatigue applies also for
The wind power plant of the lesser complicated landform of wake effect.
As shown in Figures 2 and 3, in the present embodiment, two different control strategies all include wind power plant central control system
(A is expressed as in figure) and Wind turbines partial control system (B is expressed as in figure) two parts；Wind power plant N platform Wind turbines part
Control system (be expressed as in figure WTC (1) ..., WTC (N)), carry out wind regime feature (table in figure with wind power plant central control system
Be shown as V (1) ..., V (N)) and active command (P is expressed as in figureset(1)、…、Pset(N)) information exchanges such as.
Specifically, in S1, Wind turbines partial control system uses the active adjusting strategy estimated based on equivalent wind speed,
Wind generation set control strategy controls the active output of Wind turbines according to active regulating command, designs the wind estimated based on equivalent wind speed
The novel active regulation and control system of motor group realizes the tracking of the active regulating command of Wind turbines under different set revolving speed.
Wherein, in S2, machine group parts include turbines vane, wheel hub, yaw and the big component of pylon four, to the four big component
The DEL data set of axle power square Mx, My and Mz in three directions carry out skewness and kurtosis computing index and calculated, the DEL number
It include n data according to collection.The present embodiment is for statistical analysis to DEL data set by S2, so that clear Wind turbines difference has
Function adjusts control strategy and active output to the Influencing Mechanism of machine group parts fatigue load.
Further, in S9, P is solved by particle swarm intelligence algorithmset(j):
Population size is N,
Position of i-th of particle in search space indicates are as follows: xi=(xi1, xi2..., xiD),,
Its flying speed is expressed as: vi=(vi1, vi2..., viD),
T+1 for when, i-th of particle renewal speed and position:
vid(t+1)=wvid(t)+c1r1(pid(t)-xid(t))+c2r2(pgd(t)-xid(t)) (9a)
xid(t+1)=xid(t)+vid(t) (9b)
Wherein, i=1,2 ..., N；D=1,2 ..., D, D are Wind turbines quantity, Pid(t) it was once arrived for i-th of particle
The optimum position crossed, Pgd(t) optimum position searched at present for entire population, w is inertia weight coefficient；C1 and c2 is to add
Velocity coeffficient, r1 and r2 are the random numbers on (0,1)；
PsetIt (j) is particle position xid, corresponding fitness is
As shown in figure 4, the process of particle swarm intelligence algorithm are as follows:
1) x, is initializedidAnd vid, and constrain in formula (6) range；
2), joint type (4) and (5), calculate the P of each particleid(t)；
3) P of population, is calculatedgd(t)；
4), x is updated according to formula (9)idAnd vid, constrain in the range of formula (6)；
5), the number of iterations t=t+1；
If 6), reach iteration upper limit value, optimal solution is provided；If 2) not up to iteration upper limit value, returns
And process is continued cycling through, until reaching iteration upper limit value.
On the basis of the above embodiments, in S9, when control strategy is set as having the distribution of work based on wind regime pattern measurement
When tactful, the hybrid algorithm based on autoregression integral sliding average algorithm (ARIMA) and Kalman filter (KF) is to input group
Input variable in conjunction is predicted that basic procedure is as shown in figure 5, details are not described herein.The wherein input in input combination
Variable may include turbulence intensity, mean wind speed and using wind performance number.
Obviously, the above embodiments are merely examples for clarifying the description, and does not limit the embodiments.It is right
For those of ordinary skill in the art, can also make on the basis of the above description it is other it is various forms of variation or
It changes, such as wind regime feature is predicted using other prediction techniques in S9, or calculated using other intelligence such as genetic algorithm
Method realizes the solution etc. of described control problem.There is no necessity and possibility to exhaust all the enbodiments.And thus
The obvious changes or variations extended out are still within the protection scope of the invention.
Claims (6)
1. the integrated optimization control method of wind power plant active output and unit fatigue, it is characterised in that: the following steps are included:
S1, it is based on Wind turbines fatigue load simulation software, establishes wind turbine model and sets artificial variable input combination, obtained
Take and calculate the DEL data set of the machine group parts under different input combinations, wherein
Wherein, DEL, that is, Damage Equivalent Load indicates damage equivalent load, niWithTo pass through rain flow method
The recurring number and circulation amplitude being calculated；T is load history assessment cycle；F is given sinusoidal loading frequency；M is
The characterisitic parameter of material；
S2, the calculating that skewness and kurtosis computing index are carried out to the DEL data set of several machine group parts under different input combinations,
And it is selected with symmetrical and spike distribution characteristics DEL data set according to calculated result as optimization object；
S3, quantization unit control strategy and the active influence degree set to machine group parts fatigue, in the DEL as optimization object
In data set, the unit Partial controll strategy for selecting the lesser unit allocation strategy of machine group parts DEL to control as wind power plant, with
And select DEL data to the more sensitive machine group parts of active setting as target component, and then select unit Partial controll plan
The DEL data set of target component under slightly is for modeling；
S4, the DEL data set for the target component under unit Partial controll strategy under different input combinations, using formula (2)
Shown in Density Estimator method calculate separately their distribution density function:
Wherein, K () is kernel function, and h is bandwidth, and x is the DEL data of target component, XiFor the data sheet of corresponding DEL data set
Member, n are the data amount check that DEL data set includes；
S5, formula (2) are based on, calculate DEL distribution density function maximum value and its corresponding DEL virtual value DEL (eq):
S6, according to formula (2) and formula (3), calculate DEL (eq) data sets of target component under different input combinations, and according to rapids
Intensity of flow TI to data set carry out taxonomic revision, thus obtain under different TI by mean wind speedWith active setting PsetTwo
Input DEL (eq) data form determined；Function Fitting is carried out to DEL (eq) data form by the way of surface fitting:
Wherein, ai,j(i=0...k, j=0...l) is the data for needing to be fitted；
If quantity >=2 of S7, target component, repeatedly S4, S5 and S6, number of repetition are that the quantity of target component subtracts 1 time；
S8, the complex optimal controlled strategy to realize wind power plant machine group parts active output and unit fatigue, optimization problem is stated are as follows:
0≤Pset(j)≤Pavail(j), j=1 ..., D (6b)
Wherein, D indicates that wind power plant unit number, j are jth unit, DELi(j) estimate for the DEL of No. i-th component of jth Wind turbines
Evaluation,For the DEL average value of wind power plant No. i-th component of all units, i=1 is expressed as the component of tired distribution optimization,
PrefFor active regulatory demand, PsetIt (j) is the active setting value of jth Wind turbines, PavailIt (j) can benefit for jth Wind turbines
With wind power, M indicates the M component sensitive to active output, σ1For the power deviation range of permission, σiNo. i-th between unit
The deviation range that component fatigue load allows；
S9, the control strategy are that the active optimization smart allocation strategy predicted based on wind regime feature is mutually tied with active auxiliary adjustment
The Compound Control Strategy of conjunction；
For the active optimization smart allocation strategy predicted based on wind regime feature, input is combined using time series forecasting algorithm
In input variable predicted, and predicted value is substituted into formula (4) and formula (6), calculates separately out the portion of next optimizing cycle
Part DEL predicted value and each unit can utilize wind power prediction value；Then solution P is carried out using particle swarm intelligence algorithmset(j) after,
By Pset(j) it distributes to wind power plant partial control system；
Active auxiliary adjustment strategy is only more than σ in the deviation of the active output of wind power plant and requirements1When be activated, specifically describe such as
Under:
The active output mean value in real-time sampling wind power plant each unit sampling period in communication mechanism allowed band calculates wind power plant
Deviation delta P between active total output and active requirements,
Wherein, Pout(j) actual value of the active output mean value of wind power plant jth Wind turbines is indicated；
P is obtained after handling Δ Pex,
By PexAs the input of PI controller, the output of PI controller obtains P divided by unit quantityadd；By Padd+Pset(j) as every
The output setting value of platform unit, wherein Pset(j) active optimization to be set as being predicted based on wind regime feature with control strategy is intelligently divided
P obtained when with strategyset(j)。
2. the integrated optimization control method of wind power plant active output and unit fatigue according to claim 1, feature exist
In: in S1, wind generation set control strategy controls the active output of Wind turbines according to active regulating command, and design is based on equivalent wind
Speed estimation the novel active regulation and control system of Wind turbines come realize the Wind turbines under different set revolving speed it is active adjusting refer to
The tracking of order.
3. the integrated optimization control method of wind power plant active output and unit fatigue according to claim 1, feature exist
In: in S2, machine group parts include turbines vane, wheel hub, yaw and the big component of pylon four, to three directions of the four big component
The DEL data set of axle power square Mx, My and Mz carry out skewness and kurtosis computing index and calculated, which includes n
Data.
4. the complex optimal controlled strategy side of wind power plant active output and unit fatigue according to any one of claim 1-3
Method, it is characterised in that: in S9, P is solved by particle swarm intelligence algorithmset(j):
Population size is N,
Position of i-th of particle in search space indicates are as follows: xi=(xi1, xi2..., xiD),
Its flying speed is expressed as: vi=(vi1, vi2..., viD),
T+1 for when, i-th of particle renewal speed and position:
vid(t+1)=wvid(t)+c1r1(pid(t)-xid(t))+c2r2(pgd(t)-xid(t)) (9a)
xid(t+1)=xid(t)+vid(t) (9b)
Wherein, i=1,2 ..., N；D=1,2 ..., D, D are Wind turbines quantity, Pid(t) it is had been to for i-th of particle
Optimum position, Pgd(t) optimum position searched at present for entire population, w is inertia weight coefficient；C1 and c2 is acceleration
Coefficient, r1 and r2 are the random numbers on (0,1)；
PsetIt (j) is particle position xid, corresponding fitness is
5. the integrated optimization control method of wind power plant active output and unit fatigue according to claim 4, feature exist
In: the process of particle swarm intelligence algorithm are as follows:
1) x, is initializedidAnd vid, and constrain in formula (6) range；
2), joint type (4) and (5), calculate the P of each particleid(t)；
3) P of population, is calculatedgd(t)；
4), x is updated according to formula (9)idAnd vid, constrain in the range of formula (6)；
5), the number of iterations t=t+1；
If 6) reach iteration upper limit value, optimal solution is provided；If not up to iteration upper limit value, returns 2) and continue cycling through stream
Journey, until reaching iteration upper limit value.
6. the integrated optimization control method of wind power plant active output and unit fatigue according to claim 1, feature exist
In: it is flat based on autoregression integral sliding when control strategy is set as the active allocation strategy based on wind regime pattern measurement in S9
Equal algorithm (ARIMA) and the hybrid algorithm of Kalman filter (KF) predict the input variable in input combination.
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| CN110535174B (en) * | 2019-07-23 | 2023-03-10 | 电子科技大学 | Active power control method considering fatigue load distribution and productivity of wind power plant |
| CN111262257B (en) * | 2020-02-26 | 2023-05-16 | 沈阳工业大学 | Wind turbine frequency modulation method considering fatigue load of turbine |
| CN112234616B (en) * | 2020-10-13 | 2023-01-06 | 青岛百恒新能源技术有限公司 | Wind power plant active power optimal distribution control method |
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