期刊
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
卷 30, 期 1, 页码 77-96出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0142331207076374
关键词
genetic algorithm; maximum power point tracking; photovoltaic power generation; radial basis function neural network
The paper presents a new method to achieve maximum power point tracking (MPPT) for practical grid-connected PV panels. The method employs the radial basis function neural network (RBFNN) to predict the PV plant's maximum power points corresponding to different weather conditions. The RBFNN model can be trained on-line, autonomously, using a simplified genetic algorithm (SGA). The method has been verified by modelling the MPP points for practical PV panels located in Southampton and Leeds, respectively The Leeds model has been used to control a prototype PV-grid-connected power generation system. The on-line RBFNN training scheme is discussed in detail and experimental results are presented which compared favourably with the conventional perturbation and observation (P&O) technique.
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