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A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks
Li, Chun-hua1; Zhu, Xin-jian1; Cao, Guang-yi1; Hu, Wan-qi2; Sui, Sheng1; Hu, Ming-ruo1; Li, CH
2009-02-01
Source PublicationJOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A
ISSN1673-565X
Volume10Issue:2Pages:263-270
AbstractTo extract the maximum power from a photovoltaic (PV) energy system, the real-time maximum power point (MPP) of the PV array must be tracked closely. The non-linear and time-variant characteristics of the PV array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP for traditional control strategies. We propose a fuzzy neural network controller (FNNC), which combines the reasoning capability of fuzzy logical systems and the learning capability of neural networks, to track the MPP. With a derived learning algorithm, the parameters of the FNNC are updated adaptively. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the FNNC. Simulation results show that the proposed control algorithm provides much better tracking performance compared with the fuzzy logic control algorithm.
KeywordPhotovoltaic Array Maximum Power Point Tracking (Mppt) Fuzzy Neural Network Controller (Fnnc) Radial Basis Function Neural Network (Rbfnn)
SubtypeArticle
WOS HeadingsScience & Technology ; Technology ; Physical Sciences
DOI10.1631/jzus.A0820128
Indexed BySCI
Language英语
WOS KeywordBOOST CONVERTER ; CONTROLLER ; PERFORMANCE ; VOLTAGE ; DRIVE
WOS Research AreaEngineering ; Science & Technology - Other Topics ; Physics
WOS SubjectEngineering, Multidisciplinary ; Multidisciplinary Sciences ; Physics, Applied
WOS IDWOS:000263300800014
Citation statistics
Cited Times:14[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Version出版稿
Identifierhttp://ir.ipe.ac.cn/handle/122111/6752
Collection多相复杂系统国家重点实验室
Corresponding AuthorLi, CH
Affiliation1.Shanghai Jiao Tong Univ, Fuel Cell Res Inst, Shanghai 200240, Peoples R China
2.Chinese Acad Sci, Inst Proc Engn, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Li, Chun-hua,Zhu, Xin-jian,Cao, Guang-yi,et al. A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks[J]. JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A,2009,10(2):263-270.
APA Li, Chun-hua.,Zhu, Xin-jian.,Cao, Guang-yi.,Hu, Wan-qi.,Sui, Sheng.,...&Li, CH.(2009).A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks.JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A,10(2),263-270.
MLA Li, Chun-hua,et al."A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks".JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A 10.2(2009):263-270.
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