Open AccessControl Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System
Author(s) -
Y. D. Song,
Qian Cao,
Xiaoqiang Du,
Hamid Reza Karimi
Publication year - 2013
Publication title -
journal of applied mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.307
H-Index - 43
eISSN - 1687-0042
pISSN - 1110-757X
DOI - 10.1155/2013/375840
Subject(s) - computer science , battery (electricity) , hybrid power , artificial neural network , matlab , wavelet transform , power (physics) , wind power , discrete wavelet transform , turbine , wavelet , voltage , control theory (sociology) , electrical engineering , control (management) , engineering , artificial intelligence , mechanical engineering , physics , quantum mechanics , operating system
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
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