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GA-RBF neural network based maximum power point tracking for grid-connected photovoltaic systems

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3 Author(s)
L. Zhang ; Sch. of Electron. & Electr. Eng., Leeds Univ., UK ; Yunfei Bai ; A. Al-Amoudi

This paper presents a novel GA-RBFNN (genetic algorithm trained radial basis function neural network)-based model to carry out the maximum power point tracking (MPPT) for grid-connected photovoltaic (PV) power generation control systems. The hidden layer of the neural network is self-organised by the GA-based RBF growing algorithm. The trained GA-RBFNN-based MPP model is then employed to predict the maximum power points of a PV array using measured environmental data. The simulation results are compared with the conventional P&O method, and the current/voltage waveforms of the PV panel are presented and discussed.

Published in:

Power Electronics, Machines and Drives, 2002. International Conference on (Conf. Publ. No. 487)

Date of Conference:

4-7 June 2002