Wind power prediction based on variational mode decomposition multi-frequency combinations

Gang Zhang,Lei Zhang,Jiangbin Zhang,Hongchi Liu,Xia Hua,Ye Yan,Chen Wu,Yongqing Wang

doi:10.1007/s40565-018-0471-8

Gang Zhang, Lei Zhang + Show 6 more

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Because of the uncertainty and randomness of wind speed, wind power has characteristics such as nonlinearity and multiple frequencies. Accurate prediction of wind power is one effective means of improving wind power integration. Because the traditional single model cannot fully characterize the fluctuating characteristics of wind power, scholars have attempted to build other prediction models based on empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD) to tackle this problem. However, the prediction accuracy of these models is affected by modal aliasing and illusive components. Aimed at these defects, this paper proposes a multi-frequency combination prediction model based on variational mode decomposition (VMD). We use a back propagation neural network (BPNN), autoregressive moving average (ARMA) model, and least squares support vector machine (LS-SVM) to predict high, intermediate, and low frequency components, respectively. Based on the predicted values of each component, the BPNN is applied to combine them into a final wind power prediction value. Finally, the prediction performance of the single prediction models (ARMA, BPNN, LS-SVM) and the decomposition prediction models (EMD and EEMD) are used to compare with the proposed VMD model according to the evaluation indices such as average absolute error, mean square error, and root mean square error to validate its feasibility and accuracy. The results show that the prediction accuracy of the proposed VMD model is higher.

Highlights

A power system is a complex dynamic system
The autoregressive moving average (ARMA) is used to predict the intermediate frequency while least squares support vector machine (LS-SVM) predicts low frequency components; we used the back propagation neural network (BPNN) to integrate the predicted components
We found that the error of the single prediction model is larger than the multi-frequent combination prediction

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Introduction

A power system is a complex dynamic system. The power grid must maintain a balance between power generation, transmission, and usage. The output power of a wind farm is volatile and intermittent; further, large-scale access to wind power makes it difficult to establish effective generation plans. The continuous method is a relatively basic prediction method wherein measured values of wind power at the latest point are directly applied as prediction values of the time point [2,3,4,5]. This method is simple and suitable for prediction within 3-6 hour time periods, but the prediction accuracy is poor over lengthier amounts of time. The unsteadiness and nonlinearity of wind power necessitates differential processing which will lead to poor accuracy of low-order model predictions, and high-order prediction model parameters are not easy to estimate [23, 24]

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