Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/233028

Title

Chebyshev Functional Link Neural Network-based modeling and experimental verification for photovoltaic arrays

Author(s)

Jiang, Lian Lian;  Maskell, Douglas L.;  Patra, Jagdish C.

Abstract

This paper presents a Chebyshev Functional Link Neural Network (CFLNN) based model for photovoltaic modules. There are two basic approaches to build a model - use an analytical modeling technique or use an Artificial Neural Network (ANN) based method. However, both the analytical modeling technique and the traditional Multilayer Perceptron (MLP) model have some disadvantages. For example, in the analytical model, the influence of irradiance and temperature on some parameters of the photovoltaic module, such as the parallel and series resistance and other uncertainty factors, are not taken into consideration. In the case of the multilayer neural network model, there is a large computational complexity in training the network and in its implementation. In order to overcome these advantages, we propose a CFLNN based model for solar modules. The proposed model not only reduces the complexity of the network due to the absence of hidden layers in the network configuration, but also shows better accuracy over the analytical modeling method. In the experimental section, the operating current predicted by CFLNN is compared with the outputs from other two modeling methods - MLP and the two-diode model. Finally, verification is performed using experimental datasets. The results show that the CFLNN modeling method provides better prediction of the output current compared to the analytical model and has a reduced computational complexity than the traditional MLP model.

Publication type

Conference paper

Research centre

Swinburne University of Technology. Faculty of Engineering and Industrial Sciences

Source

Proceedings of the 2012 Annual International Joint Conference on Neural Networks (IJCNN 2012), part of the 2012 IEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Queensland, Australia, 10-15 June 2012

Publication year

2012

Keyword(s)

Chebyshev Functional Link Neural Network;  Multilayer neural networks;  Photovoltaic arrays;  Two-diode model

Publisher

IEEE

ISSN

2161-4393 (series ISSN)

ISBN

9781467314886, 1467314889

Publisher URL

http://dx.doi.org/10.1109/IJCNN.2012.6252615

Copyright

Copyright © 2012.

Peer reviewed