Journal
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
Volume 30, Issue 1, Pages 77-96Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0142331207076374
Keywords
genetic algorithm; maximum power point tracking; photovoltaic power generation; radial basis function neural network
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available