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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
|
| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2013/938162 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849387257542213632 |
|---|---|
| author | Neng-Sheng Pai Her-Terng Yau Tzu-Hsiang Hung Chin-Pao Hung |
| author_facet | Neng-Sheng Pai Her-Terng Yau Tzu-Hsiang Hung Chin-Pao Hung |
| author_sort | Neng-Sheng Pai |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-35ccfcdc84df4d07b7a781581a85f832 |
| institution | Kabale University |
| issn | 1110-662X 1687-529X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-35ccfcdc84df4d07b7a781581a85f8322025-08-20T03:55:16ZengWileyInternational 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 |
| 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 |
| title | 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_short | 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 |
| 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 |