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

作者：Li, Chun hua^*; Zhu, Xin jian; Cao, Guang yi; Hu, Wan qi; Sui, Sheng; Hu, Ming ruo

来源：Journal of Zhejiang University-Science A(Applied Physics & Engineering), 2009, 10(2): 263-270.

DOI：10.1631/jzus.A0820128

• Photovoltaic array
• Maximum power point tracking (MPPT)
• Fuzzy neural network controller (FNNC)
• Radial basis function neural network (RBFNN)

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.

• 出版日期2009-2
• 单位中国科学院; 上海交通大学