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

NengSheng Pai | Zendy; HerTerng Yau | Zendy; Tzu-Hsiang Hung | Zendy; Chin-Pao Hung | Zendy

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

Author(s) -

NengSheng Pai,

HerTerng Yau,

Tzu-Hsiang Hung,

Chin-Pao Hung

Publication year - 2013

Publication title -

international journal of photoenergy

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.426

H-Index - 51

eISSN - 1687-529X

pISSN - 1110-662X

DOI - 10.1155/2013/938162

Subject(s) - heliostat , artificial neural network , computer science , robustness (evolution) , fault (geology) , control theory (sociology) , field (mathematics) , solar energy , artificial intelligence , engineering , mathematics , biochemistry , chemistry , control (management) , seismology , geology , pure mathematics , electrical engineering , gene

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

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