Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84239
Title: Chebyshev Functional Link Neural Network-based modeling and experimental verification for photovoltaic arrays
Authors: Jiang, Lian Lian.
Maskell, Douglas L.
Patra, Jagdish C.
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Jiang, L. L., Maskell, D. L., & Patra, J. C. (2012). Chebyshev Functional Link Neural Network-based modeling and experimental verification for photovoltaic arrays. The 2012 International Joint Conference on Neural Networks (IJCNN).
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.
URI: https://hdl.handle.net/10356/84239
http://hdl.handle.net/10220/12377
DOI: 10.1109/IJCNN.2012.6252615
Rights: © 2012 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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