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

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

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Main Authors: Y. D. Song, Qian Cao, Xiaoqiang Du, Hamid Reza Karimi
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2013/375840
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spelling doaj-c7fabeffbc3b45f2b838cced4d09f9332020-11-24T21:05:33ZengHindawi LimitedJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/375840375840Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power SystemY. D. Song0Qian Cao1Xiaoqiang Du2Hamid Reza Karimi3School of Automation, Chongqing University, Chongqing 400044, ChinaSchool of Energy Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Energy Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaDepartment of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, NorwayThis 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.http://dx.doi.org/10.1155/2013/375840
collection DOAJ
language English
format Article
sources DOAJ
author Y. D. Song
Qian Cao
Xiaoqiang Du
Hamid Reza Karimi
spellingShingle Y. D. Song
Qian Cao
Xiaoqiang Du
Hamid Reza Karimi
Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System
Journal of Applied Mathematics
author_facet Y. D. Song
Qian Cao
Xiaoqiang Du
Hamid Reza Karimi
author_sort Y. D. Song
title Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System
title_short Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System
title_full Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System
title_fullStr Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System
title_full_unstemmed Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System
title_sort control strategy based on wavelet transform and neural network for hybrid power system
publisher Hindawi Limited
series Journal of Applied Mathematics
issn 1110-757X
1687-0042
publishDate 2013-01-01
description 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.
url http://dx.doi.org/10.1155/2013/375840
work_keys_str_mv AT ydsong controlstrategybasedonwavelettransformandneuralnetworkforhybridpowersystem
AT qiancao controlstrategybasedonwavelettransformandneuralnetworkforhybridpowersystem
AT xiaoqiangdu controlstrategybasedonwavelettransformandneuralnetworkforhybridpowersystem
AT hamidrezakarimi controlstrategybasedonwavelettransformandneuralnetworkforhybridpowersystem
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