By Topic

Application of radial basis function networks for solar-array modelling and maximum power-point prediction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

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

A neural network-based approach for solar array modelling is presented. The logic hidden unit of the proposed network consists of a set of nonlinear radial basis functions (RBFs) which are connected directly to the input vector. The links between hidden and output units are linear. The model can be trained using a random set of data collected from a real photovoltaic (PV) plant. The training procedures are fast and the accuracy of the trained models is comparable with that of the conventional model. The principle and training procedures of the RBF-network modelling when applied to emulate the I-V characteristics of PV arrays are discussed. Simulation results of the trained RBF networks for modelling a PV array and predicting the maximum power points of a real PV panel are presented

Published in:

Generation, Transmission and Distribution, IEE Proceedings-  (Volume:147 ,  Issue: 5 )