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

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

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 i...

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Main Authors: Neng-Sheng Pai, Her-Terng Yau, Tzu-Hsiang Hung, Chin-Pao Hung
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:International Journal of Photoenergy
Online Access:http://dx.doi.org/10.1155/2013/938162
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spelling doaj-35ccfcdc84df4d07b7a781581a85f8322020-11-24T22:01:48ZengHindawi LimitedInternational Journal of Photoenergy1110-662X1687-529X2013-01-01201310.1155/2013/938162938162Application of CMAC Neural Network to Solar Energy Heliostat Field Fault DiagnosisNeng-Sheng Pai0Her-Terng Yau1Tzu-Hsiang Hung2Chin-Pao Hung3Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, TaiwanDepartment of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, TaiwanDepartment of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, TaiwanDepartment of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, TaiwanSolar 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.http://dx.doi.org/10.1155/2013/938162
collection DOAJ
language English
format Article
sources DOAJ
author Neng-Sheng Pai
Her-Terng Yau
Tzu-Hsiang Hung
Chin-Pao Hung
spellingShingle Neng-Sheng Pai
Her-Terng Yau
Tzu-Hsiang Hung
Chin-Pao Hung
Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis
International Journal of Photoenergy
author_facet Neng-Sheng Pai
Her-Terng Yau
Tzu-Hsiang Hung
Chin-Pao Hung
author_sort Neng-Sheng Pai
title Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis
title_short Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis
title_full Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis
title_fullStr Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis
title_full_unstemmed Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis
title_sort application of cmac neural network to solar energy heliostat field fault diagnosis
publisher Hindawi Limited
series International Journal of Photoenergy
issn 1110-662X
1687-529X
publishDate 2013-01-01
description 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.
url http://dx.doi.org/10.1155/2013/938162
work_keys_str_mv AT nengshengpai applicationofcmacneuralnetworktosolarenergyheliostatfieldfaultdiagnosis
AT herterngyau applicationofcmacneuralnetworktosolarenergyheliostatfieldfaultdiagnosis
AT tzuhsianghung applicationofcmacneuralnetworktosolarenergyheliostatfieldfaultdiagnosis
AT chinpaohung applicationofcmacneuralnetworktosolarenergyheliostatfieldfaultdiagnosis
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