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Maximum power point tracking of a photovoltaic energy system using neural fuzzy techniques
Li CH(李春华); Zhu XJ(朱新坚); Sui S(隋升); Hu WQ(胡万起)
2009
Source PublicationJournal of Shanghai University(English Edition)
Issue01Pages:29-36
AbstractIn order to improve the output efficiency of a photovoltaic(PV) energy system,the real-time maximum power point(MPP) of the PV array should be tracked closely. The non-linear and time-variant characteristics of the photovoltaic array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP as in traditional control strategies. A neural fuzzy controller(NFC) in conjunction with the reasoning capability of fuzzy logical systems and the learning capability of neural networks is proposed to track the MPP in this paper. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the NFC. With a derived learning algorithm,the parameters of the NFC are updated adaptively. Experimental results show that,compared with the fuzzy logic control algorithm,the proposed control algorithm provides much better tracking performance.
KeywordPhotovoltaic Array Boost Converter Maximum Power Point Tracking(Mppt) Neural Fuzzy Controller(Nfc) Radial Basis Function Neural Networks(Rbfnn)
Document Type期刊论文
Version出版稿
Identifierhttp://ir.ipe.ac.cn/handle/122111/6992
Collection研究所(批量导入)
Recommended Citation
GB/T 7714
Li CH,Zhu XJ,Sui S,et al. Maximum power point tracking of a photovoltaic energy system using neural fuzzy techniques[J]. Journal of Shanghai University(English Edition),2009(01):29-36.
APA Li CH,Zhu XJ,Sui S,&Hu WQ.(2009).Maximum power point tracking of a photovoltaic energy system using neural fuzzy techniques.Journal of Shanghai University(English Edition)(01),29-36.
MLA Li CH,et al."Maximum power point tracking of a photovoltaic energy system using neural fuzzy techniques".Journal of Shanghai University(English Edition) .01(2009):29-36.
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