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International Journal of Photoenergy
Volume 2013, Article ID 938162, 8 pages
Research Article

Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis

Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan

Received 14 August 2012; Accepted 16 November 2012

Academic Editor: Mahmoud M. El-Nahass

Copyright © 2013 Neng-Sheng Pai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Solar energy heliostat fields comprise numerous sun tracking platforms. As a result, fault detection is a highly challenging problem. Accordingly, the present study proposes a cerebellar model arithmetic computer (CMAC) neutral network for automatically diagnosing faults within the heliostat field in accordance with the rotational speed, vibration, and temperature characteristics of the individual heliostat transmission systems. As compared with radial basis function (RBF) neural network and back propagation (BP) neural network in the heliostat field fault diagnosis, the experimental results show that the proposed neural network has a low training time, good robustness, and a reliable diagnostic performance. As a result, it provides an ideal solution for fault diagnosis in modern, large-scale heliostat fields.