A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks  

作　　者：Chun-hua LI Xin-jian ZHU Guang-yi CAO Wan-qi HU Sheng SUI Ming-ruo HU 

机构地区：[1]Fuel Cell Research Institute, Shanghai Jiao Tong University, Shanghai 200240. China [2]Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100080, China

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》2009年第2期263-270,共8页浙江大学学报（英文版）A辑（应用物理与工程）

基　　金：Project (No. 20576071) supported by the National Natural Science Foundation of China

摘　　要：To 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 filzzy logic control algorithm.To 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.

关 键 词：Photovoltaic array Maximum power point tracking （MPPT） Fuzzy neural network controller （FNNC） Radial basis function neural network （RBFNN） 

分 类 号：TK01[动力工程及工程热物理] TP2[自动化与计算机技术—检测技术与自动化装置]