Maximum power point tracking of a photovoltaic energy system using neural fuzzy techniques
文献类型：期刊论文
| 作者 | Li CH(李春华) ; Zhu XJ(朱新坚) ; Sui S(隋升) ; Hu WQ(胡万起) |
| 刊名 | Journal of Shanghai University(English Edition)
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| 出版日期 | 2009 |
| 期号 | 01页码:29-36 |
| 关键词 | photovoltaic array boost converter maximum power point tracking(MPPT) neural fuzzy controller(NFC) radial basis function neural networks(RBFNN) |
| 中文摘要 | In 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. |
| 公开日期 | 2013-12-24 |
| 版本 | 出版稿 |
| 源URL | [http://ir.ipe.ac.cn/handle/122111/6992]  |
| 专题 | 过程工程研究所_研究所（批量导入） |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：过程工程研究所
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