CN105825289A - Prediction method for wind power time sequence - Google Patents
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Abstract
The invention relates to a prediction method for a wind power time sequence. The method comprises the steps of obtaining the historical statistical data of the wind power in a wind power plant, processing abnormal historical data to obtain a wind power time sequence, conducting the wavelet decomposition on the wind power time sequence to decompose the wind power time sequence into a low-frequency component and a high-frequency component, conducting the stationary test and the stationary treatment on the wind power time sequence of the high-frequency component, conducting the order and parameter fitting for the ARMA model of the wind power time sequence of the high-frequency component, converting the ARMA model into a Kalman filtering model and making a rolling forward prediction to obtain a high-frequency prediction result; conducting the slope rolling forward extrapolation prediction on the wind power time sequence of the low-frequency component to obtain a low-frequency prediction result; and finally obtaining a final prediction result through the wavelet reconstruction.
Description
Technical field
The invention belongs to power system new energy field, especially relate to a kind of method utilizing Combined model forecast wind power time series.
Background technology
In recent years, lack of energy and environmental pollution states are day by day serious, and regenerative resource gradually receives global concern.Wind energy, solar energy are inexhaustible clean energy resourcies, and wind generating technology has started large-scale application as technology the most ripe in generation of electricity by new energy.
But, undulatory property, randomness and the intermittence having due to wind speed itself, and wind speed is affected by multiple physical factors such as temperature, humidity, air pressure, the state of exerting oneself of wind generator system can be affected by serious, presents passage in time and the state that constantly fluctuates, change, cannot accurately predict.So, after large-scale wind power generating set accesses electrical network, the stability of operation of power networks and reliability can be brought challenge greatly.Therefore, the power sending wind speed or wind-power electricity generation is accurately predicted, is to ensure that one of key factor of power network safety operation under high wind-powered electricity generation permeability, has highly important Research Significance.
Summary of the invention
In sum, the method for more Accurate Prediction wind power of necessary offer a kind of short time, effectively reduces and abandons wind, improve the utilization rate of wind power resources.
The present invention relates to the Forecasting Methodology of a kind of wind power time series, comprise the following steps: obtain the historical statistical data of wind energy turbine set wind power, and history abnormal data is processed, obtain wind power time series；Wind power time series is carried out wavelet decomposition, is decomposed into low frequency component and high fdrequency component；The wind power time series of high fdrequency component is carried out stationary test and tranquilization processes；The wind power time series of high fdrequency component is carried out determination and the parameter fitting of the exponent number of arma modeling；Arma modeling be converted to Kalman filter model and do rolling forward prediction, obtaining high frequency and predict the outcome；The wind power time series of low frequency component is carried out slope and rolls outside forecast forward, obtain low frequency and predict the outcome；And the high frequency obtained predicted the outcome and low frequency predicts the outcome and obtained final predicting the outcome by wavelet reconstruction.
Relative to prior art, Kalman filter model is combined by wind power prediction model of the present invention with arma modeling, adds unidirectional negative feedback links in Discrete stochastic systems, is conducive to improving the precision of prediction of arma modeling；Additionally, by utilizing wavelet analysis method that original series is decomposed into different components, feature according to different components selects Forecasting Methodology prediction targetedly, is finally finally predicted the outcome by wavelet reconstruction and can effectively improve the precision of model prediction, reduces error.
Accompanying drawing explanation
The flow chart of the Forecasting Methodology of the wind power time series that Fig. 1 provides for the embodiment of the present invention.
The FB(flow block) of the Forecasting Methodology of the combined method wind power time series that Fig. 2 provides for the present invention.
The Kalman filtering Discrete stochastic systems figure that Fig. 3 provides for the present invention.
The Kalman filtering that Fig. 4 provides for the present invention optimize after the block diagram of Discrete Linear Random System.
Detailed description of the invention
Below according to Figure of description and in conjunction with specific embodiments technical scheme is stated the most in detail.
Referring to Fig. 1, the built-up pattern that utilizes that the present invention provides carries out the Forecasting Methodology of wind power time series, comprises the following steps:
Step S10, obtains the historical statistical data of wind energy turbine set wind power, and processes history abnormal data, obtain wind power time series；
Step S20, carries out wavelet decomposition to wind power time series, is decomposed into low frequency component and high fdrequency component；
Step S30, carries out stationary test to the wind power time series of high fdrequency component and tranquilization processes；
Step S40, carries out determination and the parameter fitting of the exponent number of arma modeling to the wind power time series of high fdrequency component；
Step S50, is converted to arma modeling Kalman filter model and does rolling forward prediction, obtaining high frequency and predict the outcome；
Step S60, carries out slope to the wind power time series of low frequency component and rolls outside forecast forward, obtain low frequency and predict the outcome；
Step S70, predicts the outcome to the high frequency obtained in step S50 and step S60 and low frequency predicts the outcome and carries out wavelet reconstruction and obtain final predicting the outcome.
In step slo, described wind power time series can obtain to the historical statistical data of wind energy turbine set wind power in one-year age by collecting the first half, can select as required.Further, since wind field equipment, manual operation and wind speed itself are easily subject to the reasons such as the feature of physical factor impact, therefore need historical data is carried out pretreatment, to remove the impact of special situation.Such as, having saltus step the most suddenly in historical data is the phenomenon of zero point, and this is probably by artificially rationing the power supply or running into what accident caused, for the data of similar this situation, directly removes after judging in a program.It is appreciated that the acquisition methods of described wind power time series can need to select according to computational accuracy.
In step S20, described low frequency air quantity and high frequency air quantity can be obtained by described wind power time series is done wavelet transformation.After a signal is done wavelet transformation, generally can be broken down into approximation signal, i.e. low frequency signal and detail signal, i.e. high-frequency signal.Wherein, approximation signal has reacted the trend part of primary signal, and detail signal has then reacted the wave portion of primary signal, plateau.Therefore, after described wind power sequence is carried out wavelet transformation, the sequence of non-stationary can be decomposed into trend component and stable wave component, data decomposition can be come by wavelet transformation, different methods is used to be predicted targetedly according to respective feature different components, to obtain the result of higher precision.
When using wavelet transformation, selecting different basic functions ψ (t) can obtain different results, conventional wavelet basis function includes Haar wavelet basis function, db2 wavelet basis function, db4 wavelet basis function etc..In the present embodiment, use db4 wavelet basis function.Utilizing wavelet decomposition to process described wind power time series, if being broken down into dried layer, the criterion that concrete stopping is decomposed is: until the low frequency trend component of current layer number just can characterize the tendency information of original series.The concrete number of plies decomposed can be to utilize wavelet decomposition 5-6 time, i.e. obtains 6-7 subsequence (5-6 layer high-frequency data, one layer of low frequency tendency information).
In step s 30, described wind power time series needs to carry out stationary test and tranquilization processes.Owing to using ARMA method in the present embodiment, and the stationarity to data of setting up of arma modeling has required.
The definition of described " stationarity " is: to time series { rt, to arbitrary integer m, if rtAnd rt-mCovariance, rtAverage all do not change over, then claim time series { rtMeet weakly stationary.Specifically, if time series { rtIt is weakly stationary, need to meet following condition:
(1) expectation does not changes over time；
(2) variance does not changes over time；
(3) covariance of any two time point does not changes over time.
In order to judge whether described wind power time series has stationarity, it is possible to use autocorrelation coefficient method, i.e. sequence is done autocorrelation analysis, obtain autocorrelation coefficient curve, observe the decrease speed of autocorrelation coefficient curve, if curve declines rapidly, then show that original data sequence is stable；Be otherwise non-smoothly.
If the assay of described wind power sequence is non-stationary series, now need to carry out tranquilization process, by difference method, described wind power non-stationary original series can be converted to stationary sequence.Difference refers to that the data of original series adjacent time point are done difference obtains the process of new sequence, repeats that original series does the sequence obtained after difference n times and is referred to as N jump sub-sequence.Stable sequence is obtained after described wind power time series can be done 1-2 difference processing.
In step s 40, the wind Power x Time after described tranquilization processes needs the matching of determination and the parameter carrying out the exponent number of arma modeling.
ARMA time series models, are again autoregressive moving-average model, are to be combined, by autoregression model (AR model) and moving average model (MA model), the model produced.
Described ARMA (p, q) model can have a form of formula (1):
The exponent number of AR part during wherein p represents model, q represents the exponent number of MA part, γtAnd rt-iRepresent current and the data of history, φ respectivelyiFor constant term, for rt-iCoefficient, i.e. AR part coefficient, atAnd at-iRepresent the residual error of the data of corresponding time point, θ respectivelyiFor constant term, it is at-iCoefficient, i.e. MA part coefficient.When p or q is 0, arma modeling returns to the state of MA or AR model respectively.
Next judge the exponent number of the model of matching, i.e. determine p, q value.The method of judgment models exponent number can be AIC criterion, i.e. Akaike information criterion.Specific practice is the exponent number scope of the most general setting models, the AIC value that then in traversal computer capacity, the model of all exponent numbers is corresponding, and the exponent number of the corresponding model that AIC value is minimum is required.When model parameter estimation mode difference, the calculating standard of AIC value is different, mainly includes following two formulas:
During maximal possibility estimation: AIC=(n-d) log σ2+2(p+q+2)(2)
During least-squares estimation: AIC=(n-d) log σ2+(p+q+1)logn(3)
Wherein n is sample number, σ2For regression criterion quadratic sum, d, p, q are parameter.
After model order determines, coefficient number unknown in model and structure just have determined that, then determine the coefficient of these the unknowns according to historical data.In the present embodiment, determine that the method for unknowm coefficient, for solving system of linear equations, uses least square method.After solving all coefficients, complete the foundation of arma modeling.
In step s 50, the forecast model of described high fdrequency component uses usual linear discrete Kalman filtering system comprise state equation and measure equation, respectively as shown in (4), (5).
Z (k+1)=H (k+1) X (k+1)+v (k+1) (5)
In two formulas, X (k) represents state vector, and ω (k) represents interference vector, and Z (k) represents observation vector, and v (k) represents noise vector,Γ (k+1, k), H (k+1) be constant coefficient matrix.The Discrete stochastic systems determined by (4), (5) formula can represent with Fig. 3.
Further, in order to obtain optimized Kalman prediction model, it is possible to use mathematical induction and orthogonality theorem are derived below equation by (4), (5) two formulas:
K=P (k+1 | k) HT[HP(k+1|k)HT+R]-1(7)
P (k+1 | k+1)=[I-KH] P (k+1 | k) (9)
In formulaRepresent the best estimate in k moment, K represents Kalman gain matrix, P (k+1 | k) represents the error co-variance matrix of the Single-step Prediction from k to the k+1 moment, P (k+1 | k+1) represents the error co-variance matrix of the filter forecasting in k+1 moment, Q is the covariance matrix of ω (k), R is the covariance matrix of v (k+1), and I is unit matrix.Formula (6)～(9) are followed successively by optimal filter respectively and estimate equation, optimum gain matrix equation, Single-step Prediction error covariance equation and filter forecasting error covariance equation.By given correspondenceP (k | k), the initial value of R, Q, can be iterated, according to (6)～(9) four equations, the prediction that rolls forward.(5)～in (9)Γ (k+1, k), H (k+1) be constant coefficient matrix, can be drawn by historical data.
See also Fig. 4, for the block diagram of the Discrete Linear Random System after optimization.
To described Kalman filtering optimization forecast model, it is determined by 3 constant coefficient matrixesГ (k+1, k), H (k+1), and 4 initial valuesP (k | k), R, Q, can be iterated the prediction rolled forward.
For wind power time series is predicted, arma modeling itself has model parameter to determine easily, the advantage of method maturation specification.In contrast, when Kalman filter model is individually predicted, the determination of model parameter is relatively difficult, but has an advantage in that, there is degenerative process in model, it is possible to real-time is adjusted optimal estimation value.Therefore, arma modeling and Kalman filter model just define excellent complementary relationship, both combinations are predicted, beneficially improve the precision predicted the outcome.
As specific embodiment, both concrete associated methods following (to illustrate as a example by ARMA (2,3)):
(1) model form first writing out ARMA (2,3) is:
X (t)=φ0+φ1X(t-1)+φ2X(t-2)+θ1a(t-1)+θ2a(t-2)+θ3a(t-3)+a(t)(10)
In order to be the matrix in Kalman filter model and the form of vector by the parameters in (10) formula and transformation of data, now do with down conversion:
(2) after carrying out the conversion in (1), according to the calculated relationship of (10), in the case of ignoring residual error a (t), there is a following relation:
(5-16) state equation during formula is Kalman filter model, foolproof can see coefficient matrix from (11) formula hereinΓ (k+1, value k).
(3) determinedГ (k+1, after value k), it is considered to measure equation, from Middle taking-up X (t) is exactly optimal direct observation plus the most uncared-for residual error a (t), therefore takes v (k+1)=a (t) and obtains (5-17) formula:
The most also the value of H-matrix has been determined that.
Arrive this, in Kalman filter model all of coefficient matrix all it has been determined that, it is only necessary to select suitable initial value according to practical situation, can bring equation (6) into～(9) are iterated the prediction that rolls forward, obtain a result.
In small wave converting method described in step S20, wind power time series high fdrequency component and low frequency component are resolved into.Its high frequency components (wave component) the most steadily, is suitable for ARMA_Kalman model.Again by above-mentioned iteration rolling forecast, the result that high fdrequency component is predicted can be drawn.
In step S60, the low frequency component (trend component) that described wind power time series is obtained after wavelet transformation.To described low frequency trend component, in a suitable short-term time scale, it is all the regular change dull according to almost fixing slope, therefore for the prediction of this part of component, can directly use and predict according to the method for slope extrapolated back, owing to short term predicted data has similar tendency information with measured data, therefore by the trend component in short-term forecast curve, flex point can be judged during extrapolation, prevent the error of prediction.
In step S70, by the method for wavelet reconstruction, predicting the outcome of the high fdrequency component described in step S50 and the predicting the outcome of low frequency component described in step S60 comprehensively being obtained final predicting the outcome, concrete grammar is as follows:
In order to each data component reduction that decomposition is come, the method that wavelet reconstruction can be used.High fdrequency component described in S50 being predicted the outcome and the low frequency component described in step S60 predicts the outcome and carries out wavelet reconstruction, can obtain last predicting the outcome, can use Mallat algorithm, this algorithm is that wavelet transformation technical field is known.
Kalman filter model is combined by wind power prediction model of the present invention with arma modeling, adds unidirectional negative feedback links in Discrete stochastic systems, is conducive to improving the precision of prediction of arma modeling；Arma modeling model parameter calculation is simple, can provide parameter information for Kalman filter model efficiently again.Original series is decomposed into different components by utilizing wavelet analysis method by this method, feature according to different components selects Forecasting Methodology prediction targetedly, last finally being predicted the outcome by wavelet reconstruction can effectively improve the precision of model prediction, reduce error, than conventional method more accurately comprehensively.
It addition, those skilled in the art also can make other change in spirit of the present invention, all should be included in scope of the present invention.
Claims (8)
1. a Forecasting Methodology for wind power time series, comprises the following steps:
Obtain the historical statistical data of wind energy turbine set wind power, and history abnormal data is processed, obtain wind power time series；
Wind power time series is carried out wavelet decomposition, is decomposed into low frequency component and high fdrequency component；
The wind power time series of high fdrequency component is carried out stationary test and tranquilization processes；
The wind power time series of high fdrequency component is carried out determination and the parameter fitting of the exponent number of arma modeling；
Arma modeling be converted to Kalman filter model and do rolling forward prediction, obtaining high frequency and predict the outcome；
The wind power time series of low frequency component is carried out slope and rolls outside forecast forward, obtain low frequency and predict the outcome；And
The high frequency obtained is predicted the outcome and low frequency predicts the outcome and obtained final predicting the outcome by wavelet reconstruction.
2. the Forecasting Methodology of wind power time series as claimed in claim 1, it is characterised in that the wavelet basis function that wind power time series carries out wavelet decomposition employing is the one in Haar wavelet basis function, db2 wavelet basis function, db4 wavelet basis function.
3. the Forecasting Methodology of wind power time series as claimed in claim 1, it is characterised in that described arma modeling be expressed as ARMA (p, q), form is:
The exponent number of AR part during wherein p represents model, q represents the exponent number of MA part, rtAnd rt-iRepresent the current and data of history respectively,For constant term, for rt-iCoefficient, i.e. AR part coefficient, atAnd at-iRepresent the residual error of the data of corresponding time point, θ respectivelyiFor constant term, it is at-iCoefficient, i.e. MA part coefficient；When p or q is 0, arma modeling returns to the state of MA or AR model respectively.
4. the Forecasting Methodology of wind power time series as claimed in claim 3, it is characterized in that, Akaike information criterion AIC is utilized to determine p, q value, the general exponent number scope of first setting models, then the AIC value that in traversal computer capacity, the model of all exponent numbers is corresponding, the exponent number of the corresponding model that AIC value is minimum is required.
5. the Forecasting Methodology of wind power time series as claimed in claim 4, it is characterised in that the calculating standard of AIC value includes following two formulas:
During maximal possibility estimation: AIC=(n-d) log σ2+2(p+q+2)；
During least-squares estimation:
AIC=(n-d) log σ2+(p+q+1)logn；
Wherein n is sample number, σ2For regression criterion quadratic sum, d, p, q are parameter.
6. the Forecasting Methodology of wind power time series as claimed in claim 1, it is characterised in that described high frequency predicts the outcome and uses usual linear discrete Kalman filtering system, comprises state equation and measures equation:
Z (k+1)=H (k+1) X (k+1)+v (k+1)；
In two formulas, X (k) represents state vector, and ω (k) represents interference vector, and Z (k) represents observation vector, and v (k) represents noise vector,
Г (k+1, k), H (k+1) be constant coefficient matrix.
7. the Forecasting Methodology of wind power time series as claimed in claim 6, it is characterised in that utilize mathematical induction and orthogonality theorem to derive state equation and measurement equation:
K=P (k+1 | k) HT[HP(k+1|k)HT+R]-1；
P(k|1|k|1)-[IKH]P(k|1|k)；
In formulaRepresent the best estimate in k moment, K represents Kalman gain matrix, P (k+1 | k) represents the error co-variance matrix of the Single-step Prediction from k to the k+1 moment, P (k+1 | k+1) represents the error co-variance matrix of the filter forecasting in k+1 moment, Q is the covariance matrix of ω (k), R is the covariance matrix of v (k+1), and I is unit matrix.
8. the Forecasting Methodology of wind power time series as claimed in claim 1, it is characterised in that use Mallat algorithm described high fdrequency component to be predicted the outcome and walks predicting the outcome of low frequency component and carry out wavelet reconstruction.
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| CN117803946A (en) * | 2024-02-29 | 2024-04-02 | 山西漳电科学技术研究院(有限公司) | Safety control system and method for low-calorific-value coal burning boiler |
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