CN104504466A - Wind power plant power prediction method considering atmospheric disturbance effect - Google Patents
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Abstract
The invention relates to a wind power plant power prediction method considering an atmospheric disturbance effect. The method comprises the following steps of regarding an atmosphere nonlinear Lorenz system as a disturbance model, defining atmosphere nonlinear disturbance variables to eliminate nonlinear factors contained in original air speed data, and optimizing the input of a power prediction model, so that the wind power prediction with higher accuracy can be realized. According to the wind power plant power prediction method considering the atmospheric disturbance effect, on the basis of a conventional power prediction method, a power prediction disturbance formula is utilized to carry out nonlinear disturbance correction on a wind speed prediction result, and the influence of some nonlinear disturbance factors existing in original wind speed on the prediction result is eliminated, so that the aim of accurately predicting wind power is achieved. The experiment result shows that according to the method, the prediction accuracy of wind energy can be greatly improved, and the development of wind power industry is greatly promoted.
Description
Technical Field
The invention relates to a method capable of accurately predicting wind speed of a wind power plant, and belongs to the technical field of power generation.
Background
The development and utilization of new energy are important ways for solving the problems of global energy shortage, ecological environment deterioration and the like. Wind energy is a clean renewable energy source and has abundant resources and wide distribution. The wind power generation can realize large-scale effective utilization of wind energy resources. According to the statistical data of the global wind energy society, by 2013, the global accumulated wind power installed capacity reaches 318,117MW, which is increased by more than 5 times compared with the statistical data before 10 years. During the operation of a wind farm, wind is one of the most critical meteorological factors influencing power change, and the random fluctuation and intermittency of wind energy cause the wind power generated by the wind energy to have similar instability. With the continuous increase of wind power penetration power, the wind power integration brings risks to the stability and safety of a power system, and normal production and life of people are further influenced. Therefore, research and development of high-precision wind speed and power prediction technology is urgent to develop wind energy resources.
The existing wind power prediction models can be divided into physical models, statistical models, artificial intelligence and hybrid models according to different modeling methods. The physical model improves the resolution of numerical weather forecast by using some physical variables and geographic factors, and is suitable for long-term wind speed prediction. The statistical model is a relation between model input and model output established based on a large amount of historical data, and generally comprises a continuous method model, a time series model, a Kalman filtering model and the like. Artificial intelligence is a wind energy prediction technology widely used at present, and generally includes methods such as a Wavelet Neural Network (WNN), an error back propagation neural network (BP), a Radial Basis Function (RBF), a Support Vector Machine (SVM), Fuzzy Logic (FL), and the like. Due to the different limitations of single prediction models, combined models of multiple prediction methods have been proposed and applied more and more in recent years.
Most of the existing wind power prediction technologies improve the prediction accuracy by improving various numerical algorithms, and no prediction method considers the influence of the nonlinear characteristics of an atmospheric system on a power prediction result, so that higher prediction accuracy cannot be obtained.
Disclosure of Invention
The invention aims to provide a wind power plant power prediction method considering atmospheric disturbance effect aiming at the defects of the prior art so as to improve the prediction precision of wind energy.
The problem of the invention is realized by the following technical scheme:
a wind power plant power prediction method considering atmospheric disturbance effect comprises the steps of regarding an atmospheric nonlinear Lorenz system as a disturbance model, defining an atmospheric nonlinear disturbance variable to eliminate nonlinear factors contained in original wind speed data, and optimizing input of a power prediction model to achieve wind power prediction with higher precision, wherein the method comprises the following steps:
a. acquiring wind speed and power data of a wind power plant at a set frequency, equally dividing the acquired data into a training set and a test set, respectively training three prediction models, namely a Wavelet Neural Network (WNN), an error back propagation network (BP) and a Support Vector Machine (SVM), by using the data of the training set, and predicting and verifying the data of the test set;
b. giving initial value conditions and parameter values, and numerically solving a Lorenz equation;
c. taking the mode length of each point in the atmospheric nonlinear system phase space as a comprehensive disturbance variable L of three disturbance variables in the Lorenz system, wherein the atmospheric nonlinear disturbance variable L is given by the following formula:
system for representing atmospheric nonlinear disturbancesA motion state at a time;
d. respectively predicting the wind speed by utilizing a Wavelet Neural Network (WNN), an error back propagation network (BP) and a Support Vector Machine (SVM), and respectively recording initial prediction results of the three prediction models as；
e. Defining a power prediction disturbance formula:
wherein,in order to be the result of the preliminary wind speed prediction,for the correction value of the wind speed prediction result,is the coefficient of perturbation.
D, forecasting the wind speed in the step d according to a power forecasting disturbance formulaRespectively carrying out nonlinear interference correction, and respectively recording the corrected prediction results；
f. Predicting the speed in steps d and eAs an input to the WNN model,as an input to the BP model, there is,and respectively obtaining three groups of corresponding power prediction results as the input of the SVM model.
g. And specifying an error index and carrying out error analysis on each prediction result.
According to the wind power plant power prediction method considering the atmospheric disturbance effect, the initial value condition is given when the Lorenz equation is solved numericallyThe parameter is taken as。
According to the wind power plant power prediction method considering the atmospheric disturbance effect, error indexes are designated, error analysis is carried out on each prediction result, the error indexes comprise Mean Absolute Error (MAE), Mean Square Error (MSE) and absolute percentage error (MAPE), and the calculation formulas are as follows:
，
，
；
whereinAndrespectively representThe observed and predicted values of wind speed or power at the moment,representing the number of predicted samples;
recording wind speed sample data in a prediction period(ii) a Respectively predict the valuesSum sample valueSubstituting the three error indexes to calculate error results of WNN, BP and SVM models; in the same way, the predicted values are respectively calculatedSum sample valueAnd substituting the formula to obtain error results of the LSWNN, LSBP and LSSVM models. Specific error statistics are shown in table 1.
F, three groups of different power prediction results can be obtained in the power prediction process in the step f and are respectively recorded asAnd power sample data in the prediction period is recorded asWill predict the valueSum sample valueAnd correspondingly substituting the three error formulas to obtain an error result of the power prediction. The specific error statistics are shown in table 2.
On the basis of a conventional power prediction method, the nonlinear disturbance correction is carried out on the wind speed prediction result by using a power prediction disturbance formula, so that the influence of certain nonlinear disturbance factors existing in the original wind speed on the prediction result is eliminated, and the aim of accurately predicting the wind power is fulfilled. Experimental results prove that the method can greatly improve the prediction precision of wind energy and has great promotion effect on the development of the wind power industry.
Drawings
The invention will be further explained with reference to the drawings.
FIG. 1 is 4028 wind speed data on Sotavento wind farm 2014 month 2, abscissaRepresenting the observation time, and the ordinate represents the wind speed data;
FIG. 2 is an atmospheric nonlinear disturbance quantity distribution curve defined by equation (1);
FIG. 3 is a wind speed prediction curve for the WNN model and the LSWNN model, with the predicted time on the abscissa and the wind speed value on the ordinate;
FIG. 4 is a wind speed prediction curve of a BP model and an LSBP model, the abscissa is prediction time, and the ordinate is a wind speed value;
FIG. 5 is a wind speed prediction curve of an SVM model and an LSSVM model, the abscissa is prediction time, and the ordinate is a wind speed value;
FIG. 6 is a sequence of wind speedsA power prediction result of the WNN model as an input quantity;
FIG. 7 is a sequence of wind speedsA power prediction result of the BP model as an input quantity;
FIG. 8 is a sequence of wind speedsA power prediction result of the SVM model as an input quantity;
FIG. 9 is a distribution of the atmospheric nonlinear disturbance variable imposed in the prediction model LSWNN (the disturbance coefficient is 0.0253);
FIG. 10 is a graph of the distribution of the atmospheric nonlinear disturbance variable imposed in the prediction model LSBP (disturbance coefficient-0.0384);
FIG. 11 is a distribution of atmospheric nonlinear disturbance variables applied in the prediction model LSSVM (disturbance coefficient-0.0131);
fig. 12 is a flow chart of the present invention.
The symbols in the text are: l, an atmospheric nonlinear disturbance variable,atmospheric nonlinear disturbance systemThe state of motion at the moment in time,a wind speed prediction result of a Wavelet Neural Network (WNN),error of the first and secondWind speed prediction results to the propagation network (BP),a wind speed prediction result of a Support Vector Machine (SVM),a correction value of a wind speed prediction result of a Wavelet Neural Network (WNN),a correction value of the wind speed prediction result of the error back propagation network (BP),a corrected value of a wind speed prediction result of a Support Vector Machine (SVM),and a preliminary wind speed prediction result is obtained,corrected values of wind speed prediction results, MAE, mean absolute error, MSE, mean square error, MAPE, absolute percentage error,、an observed value of the wind speed or power at the moment,、the predicted value of the wind speed or power at the moment,and a disturbance coefficient of the magnetic field,representing the number of predicted samples.
Detailed Description
The invention provides a new idea and a new view angle of a wind power plant power prediction method. Considering the influence of nonlinear factors in an atmospheric system on wind speed change, taking the atmospheric nonlinear Lorenz system as a disturbance model, defining an atmospheric nonlinear disturbance variable to eliminate the nonlinear factors contained in the original wind speed, and optimizing the input of a power prediction model so as to realize higher-precision power prediction. The present invention will be described in detail with reference to the following examples:
the method comprises the following steps: in the embodiment, wind speed and power data are recorded every 10 minutes from 2 month 1 day to 2 month 28 days of a Sotavento wind farm 2014 in california, the sample capacity of the wind speed and power data is 4028 (the total sample capacity is 4032, four missing values exist in original data due to anemometry towers or wind conditions and the like, the four missing values are discarded and rearranged into 4028 data), the data are divided into a training set and a test set, the data of the training set are used for respectively training three prediction models, namely a Wavelet Neural Network (WNN), an error back propagation network (BP) and a Support Vector Machine (SVM), and the data of the test set are predicted and verified;
step two: initial conditionsValue of parameterUnder the condition, a Lorenz equation is numerically solved to obtain a group of proper atmospheric disturbance sequences;
step three: the concept of the atmospheric nonlinear disturbance variable is presented and its expression is given:
taking the mode length of each point in the atmospheric nonlinear system phase space as a comprehensive disturbance variable L of three disturbance variables in the Lorenz system, wherein the atmospheric nonlinear disturbance variable L is given by the following formula:
（1）
system for representing atmospheric nonlinear disturbancesA motion state at a time;
step four: respectively predicting the 2-month wind speed in 2014 by using a conventional prediction model Wavelet Neural Network (WNN), an error back propagation network (BP) and a Support Vector Machine (SVM), and respectively recording the prediction results as；
Step five: defining a power prediction disturbance formula:
（2）
wherein,in order to be the result of the preliminary wind speed prediction,for the correction value of the wind speed prediction result,in order to be able to make the coefficients of the perturbations,
forecasting results of wind speed in step four according to power forecasting disturbance formulaRespectively carrying out nonlinear interference correction, and respectively recording the corrected prediction results；
Step six: predicting the wind speedAs an input to the WNN model,as an input to the BP model, there is,respectively obtaining three groups of corresponding power prediction results as the input of an SVM model so as to compare the prediction level of each model with the accuracy of the prediction results;
step seven: and (3) giving a proper error index, comparing and analyzing the wind speed and power prediction results of each model:
the error indexes comprise Mean Absolute Error (MAE), Mean Square Error (MSE) and absolute percent error (MAPE), and the calculation formulas are respectively as follows:
（3）
（4）
（5）
whereinAndrespectively representThe observed and predicted values of wind speed or power at the moment,representing the number of predicted samples.
Analysis of Experimental results
The method provided by the invention is subjected to example verification through wind speed and power data of a Sotavento wind power station in California in 2 months 2014, and main experimental results of the method are shown in the attached drawings. It is to be understood that the following experimental analysis is exemplary only, and is not intended to limit the method to a particular application.
Firstly, a wind speed distribution curve of a Sotavento wind power plant 2014 for 2 months is shown in FIG. 1, and it can be seen that the fluctuation of the wind speed is large, and the average wind speed reaches 12 m/s. The calculation is carried out by means of the numerical solution of the atmosphere nonlinear equation and according to the atmosphere nonlinear disturbance quantity formula defined by the formula (1). As shown in fig. 2, the distribution of the atmospheric nonlinear disturbance amount exhibits a random fluctuation.
Secondly, the 4028 wind speed data in month 2 are divided into a training set and a testing set, WNN, BP and SVM models are trained respectively, and the data of the testing set are predicted and verified. The predicted results are shown in FIGS. 3-5.
And thirdly, providing disturbance models LSWNN, LSBP and LSSVM corresponding to each model according to the prediction result, and eliminating nonlinear factors in WNN, BP and SVM model wind speed prediction data by using the disturbance models respectively to realize more accurate fitting of actual wind speed data. FIGS. 9-11 show the disturbance coefficients and the atmospheric nonlinear disturbance quantities in the respective disturbance model correction equations, which are obtained from the preliminary prediction resultsAnd several groups of optimal results are obtained by repeatedly training the sample data.
And finally, predicting the power of the predicted time period by using the wind speed prediction result. The power prediction model adopts WNN, BP and SVM models, and the input quantities are respectivelyvs ，vs Andvs ,. The power prediction results are shown in fig. 6-8. As can be seen from FIGS. 6-8, the wind speed sequence is utilizedThe fitting effect of the result of power prediction on the actual power distribution curve is far better than that of the actual power distribution curve by utilizing the wind speed sequenceResults of performing power prediction. The above-described wind speed and power prediction errors are calculated from the errors defined by equations (3) to (5), respectively, and the results are shown in tables 1 and 2, respectively.
TABLE 1
TABLE 2
All experimental results of the method strongly show that the introduction of the atmospheric nonlinear disturbance variable can realize more accurate power prediction, and the introduction of the atmospheric nonlinear disturbance model in the field of wind energy prediction research is feasible and has very important theoretical research value and practical guidance significance.
Claims (3)
1. A wind power plant power prediction method considering atmospheric disturbance effect is characterized in that an atmospheric nonlinear Lorenz system is taken as a disturbance model, an atmospheric nonlinear disturbance variable is defined to eliminate nonlinear factors contained in original wind speed data, and the input of a power prediction model is optimized, so that wind power prediction with higher precision is realized, and the method comprises the following steps:
a. acquiring wind speed and power data of a wind power plant at a set frequency, equally dividing the acquired data into a training set and a test set, respectively training three prediction models, namely a Wavelet Neural Network (WNN), an error back propagation network (BP) and a Support Vector Machine (SVM), by using the data of the training set, and predicting and verifying the data of the test set;
b. giving initial value conditions and parameter values, and numerically solving a Lorenz equation;
c. taking the mode length of each point in the atmospheric nonlinear system phase space as a comprehensive disturbance variable L of three disturbance variables in the Lorenz system, wherein the atmospheric nonlinear disturbance variable L is given by the following formula:
system for representing atmospheric nonlinear disturbancesA motion state at a time;
d. respectively predicting the wind speed by utilizing a Wavelet Neural Network (WNN), an error back propagation network (BP) and a Support Vector Machine (SVM), and respectively recording the prediction results of the three prediction models as VD1, VD2 and VD 3;
e. defining a power prediction disturbance formula:
wherein,in order to be the result of the preliminary wind speed prediction,for the correction value of the wind speed prediction result,in order to be able to make the coefficients of the perturbations,
d, forecasting the wind speed in the step d according to a power forecasting disturbance formulaRespectively carrying out nonlinear interference correction, and respectively recording the corrected prediction results；
f. Predicting the wind speedAs an input to the WNN model,as an input to the BP model, there is,respectively obtaining three groups of corresponding power prediction results as the input of an SVM model;
g. and specifying an error index and carrying out error analysis on each prediction result.
2. The method for predicting the power of the wind power plant by considering the atmospheric disturbance effect as claimed in claim 1, wherein the given initial value condition when the Lorenz equation is solved numerically isThe parameter is taken as。
3. A wind farm power prediction method considering atmospheric disturbance effect according to claim 2, characterized in that error indicators are specified and error analysis is performed on each prediction result, the error indicators comprise Mean Absolute Error (MAE), Mean Square Error (MSE) and absolute percent error (MAPE), and the calculation formulas are as follows:
；
wherein,andrespectively representThe observed and predicted values of wind speed or power at the moment,representing the number of predicted samples;
recording wind speed sample data in a prediction periodRespectively predict the valuesSum sample valueCarry in the three error indicatorsCalculating error results of the WNN, the BP and the SVM models; in the same way, the predicted values are respectively calculatedSum sample valueSubstituting a formula to obtain error results of LSWNN, LSBP and LSSVM models;
f, three groups of different power prediction results can be obtained in the power prediction process in the step f and are respectively recorded asAnd power sample data in the prediction period is recorded asWill predict the valueSum sample valueAnd correspondingly substituting the three error formulas to obtain an error result of the power prediction.
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| CN107358006A (en) * | 2017-07-25 | 2017-11-17 | 华北电力大学（保定） | A kind of Lorenz disturbance wind speed forecasting methods based on principal component analysis |
| CN107392379A (en) * | 2017-07-25 | 2017-11-24 | 华北电力大学（保定） | A kind of time series wind speed forecasting method based on Lorenz disturbances |
| CN108846508A (en) * | 2018-05-30 | 2018-11-20 | 华北电力大学（保定） | A kind of wind speed forecasting method and system based on atmospheric perturbation |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107358006A (en) * | 2017-07-25 | 2017-11-17 | 华北电力大学（保定） | A kind of Lorenz disturbance wind speed forecasting methods based on principal component analysis |
| CN107392379A (en) * | 2017-07-25 | 2017-11-24 | 华北电力大学（保定） | A kind of time series wind speed forecasting method based on Lorenz disturbances |
| CN107392379B (en) * | 2017-07-25 | 2021-06-11 | 华北电力大学（保定） | Lorenz disturbance-based time series wind speed prediction method |
| CN107358006B (en) * | 2017-07-25 | 2021-10-22 | 华北电力大学（保定） | Lorenz disturbance wind speed prediction method based on principal component analysis |
| CN108846508A (en) * | 2018-05-30 | 2018-11-20 | 华北电力大学（保定） | A kind of wind speed forecasting method and system based on atmospheric perturbation |
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