Maximum power point tracking of a photovoltaic energy system using neural fuzzy techniques |
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| Authors: | Chun-hua Li Xin-jian Zhu Sheng Sui Wan-qi Hu |
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| Institution: | 1. Fuel Cell Research Institute, Shanghai Jiaotong University, Shanghai 200240, P. R. China
2. Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100080, P. R. China |
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| Abstract: | 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. |
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| Keywords: | photovoltaic array boost converter maximum power point tracking (MPPT) neural fuzzy controller (NFC) radial basis function neural networks (RBFNN) |
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