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Journal of Applied Mathematics
Volume 2013 (2013), Article ID 375840, 8 pages
Research Article

Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System

1School of Automation, Chongqing University, Chongqing 400044, China
2School of Energy Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
3Department of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway

Received 31 July 2013; Accepted 26 September 2013

Academic Editor: Tao Zou

Copyright © 2013 Y. D. Song et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper deals with an energy management of a hybrid power generation system. The proposed control strategy for the energy management is based on the combination of wavelet transform and neural network arithmetic. The hybrid system in this paper consists of an emulated wind turbine generator, PV panels, DC and AC loads, lithium ion battery, and super capacitor, which are all connected on a DC bus with unified DC voltage. The control strategy is responsible for compensating the difference between the generated power from the wind and solar generators and the demanded power by the loads. Wavelet transform decomposes the power difference into smoothed component and fast fluctuated component. In consideration of battery protection, the neural network is introduced to calculate the reference power of battery. Super capacitor (SC) is controlled to regulate the DC bus voltage. The model of the hybrid system is developed in detail under Matlab/Simulink software environment.