CN108711885A - One kind cooperateing with method of estimation for field of wind-force state - Google Patents
One kind cooperateing with method of estimation for field of wind-force state Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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- 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
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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
- Y02E10/76—Power conversion electric or electronic aspects
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Abstract
The invention belongs to wind power plant state estimation fields.Larger feature can be fluctuated by air speed influence for wind power plant branch electric system, devised strong tracking square root volume Kalman filter, improved the precision of state estimation；And further directed to wind power plant distributed frame, it is proposed that the collaboration based on strong tracking volume Square root Kalman filtering is parallel and order status algorithm for estimating.Under wind power plant uncertain environment, volume Kalman filtering can better estimation effect, to demonstrate the validity of algorithm.
Description
The invention belongs to state estimation field in wind power plant, it is related to a kind of distributed method of state estimation in wind power plant.
Invention content
Compared with conventional Power Generation Mode (thermoelectricity, water power, nuclear power etc.), wind-power electricity generation have fluctuation, it is not exclusively predictive,
The features such as low controllability, brings challenge to electric system, especially in terms of active power and reactive power scheduling controlling.
In wind power plant by example show voltage and it is idle between relationship.
It is more for wind power plant distribution regulation and control (control) method.Than the control for being more typically namely based on multiple agent thought
Method regards each wind turbine as an independent intelligent body；It is grouped the control method of thought based on wind turbine, will have identical
The wind turbine of ability and operating mode is divided into one group, one distributed control of every group of design.No matter which kind of distributed control mode, all needs
A kind of estimator of response is wanted to carry out the effective acquisition and analysis of state.
(1) wind power plant membership spatial model
The active power output and wind of wind power plant have much relations, usually it is active can fluctuate with the wind, trend grid-connected circuit with
And change on transmission line of electricity comparatively fast, the grid-connected of some large-scale wind power fields can generate one to grid voltage quality and voltage stability
Fixed influence.It follows that its active power has fluctuation, intermittent feature, reactive power and wind power plant in wind power plant
Wind turbine type used in interior is related, while also and its control system has certain relationship.
In typical electric system, it is equivalent after model namely multiple branch circuit single machine equivalent model grid entry point low-pressure side
Voltage UlowAnd the voltage U in each wind turbine collection electric linejeqExpression formula
Wind turbines set end voltage is not only related with the output power of itself, also related with the output power of other units,
It intercouples between each unit of wind power plant together, when the output power change of wind power plant Fans, with " string " or even entire
The set end voltage of other units will all be affected in wind power plant, and only influence degree is different, and Holt ' s two parameters are utilized to this
Method establishes the state space of wind turbine：
State equation：
It by analysis and calculates, can obtain, state equation is：
Wherein For system noise, η ≠
i。
Observational equation：
Analysis through oversampling circuit, can obtain its observational equation is：
WhereinFor system noise.
Idle control is carried out to wind power plant, in order to ensure the quality of voltage of entire wind power plant, if can be to wind-powered electricity generation
Field carries out high-precision state estimation to wind power plant rapidly after carrying out idle control, is of great significance, while each understanding
After the state of branch, more efficient, accurate power scheduling can be also carried out.
(2) strong tracking square root volume Kalman filtering
For some branch of wind power plant, since the wind turbine quantity of a branch road generally will not be too many；Wind in wind power plant
Have：The features such as uncertain, discontinuous, mutability, the variation of wind influences very big, membership to wind power plant branch wind turbine
It can have greatly changed, need to find relatively suitable state estimation algorithm thus on the basis of chapter 3, strong tracking is calculated
Method there is good signal trace ability, the algorithm also to have stronger robustness, use strong tracking thus electric system
Volume Kalman filtering algorithm to carry out state estimation to the same branch single machine equivalent model of wind power plant.
The characteristics of for wind power plant branch equivalent model, using strong tracking square root volume Kalman filtering
(Strong Tracking Square Root Cubature Kalman Filter, STFSRCKF) algorithm, it is assumed that known system
It unites in the state estimation at k momentWith the square root coefficient S of estimate covariance battle arrayk, algorithm principle is as follows：
1. system initialization：
2. the time updates：Assuming that the posteriority state estimation at k-1 moment isCorresponding covariance matrix is Pk-1|k-1,
Ask system the k moment state estimation.
Calculate volume point：
Calculate the volume point transmitted by system equation：
X* i,k|k-1=f (Xi,k-1) (7)
Status predication value：
The square root of status predication error covariance matrix：
Wherein：Qk-1=SQ,k-1ST Q,k-1, SQ,k-1Indicate Qk-1Square root coefficient, weighted center matrix is：
Measure prediction：
Utilize Sk|k-1Calculate volume point：
It is transmitted by measurement equation：Zi,k|k-1=h (Xi,k|k-1)
The predicted value and its covariance square root of observed quantity：
Wherein：Pk=SR,kST R,k, SR,kIndicate RkSquare root coefficient, weighted center matrix is：
Cross-covariance：
New breath covariance matrix：
Pz,k|k-1=Pzz,k|k-1-Rk (18)
State updates：
Strong tracking is mainly reflected in by adjusting status predication error in real time the improvement of square root volume Kalman filtering
Covariance matrix and corresponding gain battle array reach, wherein λkFinding process it is as follows：
Nk=Vk-HkQk-1[Pk|k-1]-1Pxz,k|k-1-Rk (19)
Mk=Pz,k|k-1+Nk-Vk (20)
VkFor the covariance matrix of the residual sequence of reality output：
Wherein：0.95≤ρ≤0.995 is fading factor.
State updates and the square root coefficient of corresponding covariance matrix is：
Sk|k=Chol (s [KkSR,k,Xk|k-KkZk|K-1]) (24)
STFSRCKF algorithms calculate the Posterior Mean and covariance of state using the higher SRCKF of approximation accuracy, avoid pair
The calculating of Jacobian matrix, while the square root of matrix is updated in calculating process every time, it avoids due to calculating error and making an uproar
Variance matrix negative definite caused by the factors such as acoustical signal maintains STF to the uncertain robustness of system model, there is filtering to stablize
Property ' the signal trace ability of algorithm is also enhanced to a certain extent.
For the multiple agent estimation problem of wind field, algorithm above be based on model appropriate be also it is feasible, can
Will cooperate with parallel estimation method and estimation of the order method cooperateed with to be generalized to the situation that multiple agent is estimated.
(3) parallel estimation method is cooperateed with
The relationship between reactive power in voltage and wind power plant is analyzed, the relationship of the two is close, and analyzes branch shape
State Distributed fusion problem.Realize that the key point of distributions estimation is to ensure that the state in each estimator is consistent
Property.Due to having coupled relation between membership, the state estimation of any branch will necessarily be by the shadow of other all branches
It rings.Therefore each membership estimation first has to obtain the effective status of other branches.
There are very strong coupled relations, the output power of Wind turbines not only to affect itself between each unit of wind power plant
Set end voltage and the set end voltage for influencing this wind power plant other units and the voltage at wind farm grid-connected point.Due to wind power plant
In the voltage of each Wind turbines have certain relationship, if incremental, based on the state that certain units are relatively accurate
Estimated value carries out state estimation to other Wind turbines, then good effect can be obtained in terms of estimated accuracy.
(4) estimation of the order method is cooperateed with
For adaptation sequence Power Regulation process, wind power plant order status is estimated according to the sequence of branch Power Regulation, and with previous branch
The basis that the state estimation on road is estimated as latter branch state, in this way can be in the case where allowing time dispatching cycle, weight
Multiple repeatedly cycle estimation, the consistency of the state estimation of each branch of collateral security propose order status algorithm for estimating thus.In order to
More intuitively estimation of the order principle is stated out, is made that sequential algorithm estimation principle figure,
Description of the drawings
Fig. 1 is that Wind turbines generating set cooperates with parallel state algorithm for estimating flow chart.
Fig. 2 is that Wind turbines generating set cooperates with order status algorithm for estimating flow chart.
Specific implementation mode
This patent is using cooperateing with the detailed process of parallel algorithm for estimating as follows, as shown in Figure 1：
For branch i, cooperate with the detailed process of parallel algorithm for estimating as follows：
Step1：Given threshold δi> 0；
Step2：K-1 moment, the state of all branches are obtained by SCADA system at the k moment
Step3：It takesBased on, the 1st estimation is carried out, membership is obtained
Step4：It willIt is sent to SCADA system, and obtains the state of other branches from SCADA system
Step5：It takesBased on, the 2nd estimation is carried out, membership is obtained
Step6：It willIt is sent to SCADA system, and obtains the state of other branches from SCADA system
Step7：IfThen stop estimating, output estimation valueOtherwise continue to execute down；
......
Step3*(τ-1)+2：It takesBased on, the τ times estimation is carried out, membership is obtained
Step3*(τ-1)+3：It willIt is sent to SCADA system, and obtains the state of other branches from SCADA system
Step3*(τ-1)+4：IfThen stop estimating, output estimation valueOtherwise Step3* τ are executed
+2；
......。
This patent is as follows using the detailed process of collaboration estimation of the order algorithm, as shown in Figure 2：
Assuming that estimation sequence is branch 1, branch 2 ... branch l, and set the threshold value given threshold δ of each circuiti> 0, if
Branch i is determined in Shi Jianzhouqi [k,k+1], completing the mark that the κ times is estimated isComplete estimation mark be
In scheduling instance k：
1st estimation
For branch 1：
Step1：K-1 moment, the state of all branches are obtained by SCADA system
Step2：It takesBased on, the 1st estimation is carried out, membership is obtained
Step3：It willIt is sent to SCADA system, it will1 is set to,It is set to 0；
......
For branch i：
Step1：Newest state, the state of all branches are obtained by SCADA system at the k moment
Step2：It takesBased on, the 1st estimation is carried out, membership is obtained
Step3：It willIt is sent to SCADA system；
......
The κ times estimation
For branch 1：
JudgeThen tuning controller assigns estimation task,Then not under
Up to estimation.
Step1：Newest state, the state of all branches are obtained by SCADA system
Step2：It takesBased on, the κ times estimation is carried out, membership is obtained
Step3：It willIt is sent to SCADA system, and willIt is sent to tuning controller；
Step4：IfThen stop estimating, output estimation valueAnd it willIt is sent to association's control
Adjust device；
......
For branch i：
JudgeThen tuning controller assigns estimation task,It does not assign then
Estimation；
Step1：Newest state, the state of all branches are obtained by SCADA system
Step2：It takesBased on, the κ times estimation is carried out, membership is obtained
Step3：It willIt is sent to SCADA system, and willIt is sent to tuning controller；
Step4：IfThen stop estimating, output estimation valueAnd it willIt is sent to coordination control
Device processed；
......。
Claims (5)
1. a kind of cooperateing with method of estimation about the state of unit in wind power plant and branch, it is characterised in that：
(1) state-space model of unit and branch in wind power plant is established；
(2) Parallel Scheduling situation in wind power plant is adapted to, algorithm for estimating is cooperateed with using the states in parallel of unit in wind power plant and branch；
(3) sequence Parallel Scheduling situation in wind power plant is adapted to, is cooperateed with and is estimated using the states in parallel of unit in wind power plant and branch
Algorithm.
2. to adapt to the state of unit and branch collaboration estimation in wind power plant, by the current collection connections structure feature of wind power plant and
Holt ' s two-parameter exponential exponential smoothings, it is proposed that corresponding state-space model.
3. for the consistency for ensureing between wind power plant inner blower state and membership, and adapting to the feelings of current power primary distribution
Condition, since there are very strong coupled relations, the output power of Wind turbines not only to affect itself between each unit of wind power plant
Set end voltage and the set end voltage for influencing this wind power plant other units and the voltage at wind farm grid-connected point, therefore using association
With parallel estimation strategy.
4. the case where to adapt to power order-assigned, using collaboration estimation of the order strategy, according to the sequence of branch Power Regulation to wind-powered electricity generation
Field sequence state estimation, and the basis estimated using the state estimation of previous branch as latter branch state, can adjust in this way
In the case of spending cycle time permission, repeatedly cycle estimation, the consistency of the state estimation of each branch of collateral security.
It is single using strong tracking square root volume Kalman filtering 5. easily being influenced by environmental catastrophe to adapt to state in wind power plant
The method for estimating state of machine or branch.
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| CN201810569937.9A CN108711885A (en) | 2018-06-05 | 2018-06-05 | One kind cooperateing with method of estimation for field of wind-force state |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102680762A (en) * | 2011-10-11 | 2012-09-19 | 国电联合动力技术有限公司 | Unscented-Kalman-filter-based wind farm generator terminal voltage measuring method and application thereof |
| CN107332289A (en) * | 2017-09-05 | 2017-11-07 | 清华大学 | A kind of variable-speed wind-power unit participates in system frequency modulation method |
| CN107565553A (en) * | 2017-09-19 | 2018-01-09 | 贵州大学 | A kind of power distribution network robust dynamic state estimator method based on UKF |
| CN107979112A (en) * | 2017-11-30 | 2018-05-01 | 全球能源互联网研究院有限公司 | A kind of blower control method, system, terminal and readable storage medium storing program for executing |
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2018
- 2018-06-05 CN CN201810569937.9A patent/CN108711885A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102680762A (en) * | 2011-10-11 | 2012-09-19 | 国电联合动力技术有限公司 | Unscented-Kalman-filter-based wind farm generator terminal voltage measuring method and application thereof |
| CN107332289A (en) * | 2017-09-05 | 2017-11-07 | 清华大学 | A kind of variable-speed wind-power unit participates in system frequency modulation method |
| CN107565553A (en) * | 2017-09-19 | 2018-01-09 | 贵州大学 | A kind of power distribution network robust dynamic state estimator method based on UKF |
| CN107979112A (en) * | 2017-11-30 | 2018-05-01 | 全球能源互联网研究院有限公司 | A kind of blower control method, system, terminal and readable storage medium storing program for executing |
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Application publication date: 20181026 |