Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis
Joint Authors
Hung, Chin-Pao
Yau, Her-Terng
Hung, Tzu-Hsiang
Pai, Neng-Sheng
Source
International Journal of Photoenergy
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-01-01
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Pai, Neng-Sheng& Yau, Her-Terng& Hung, Tzu-Hsiang& Hung, Chin-Pao. 2013. Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis. International Journal of Photoenergy،Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-509726
Modern Language Association (MLA)
Pai, Neng-Sheng…[et al.]. Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis. International Journal of Photoenergy No. 2013 (2013), pp.1-8.
https://search.emarefa.net/detail/BIM-509726
American Medical Association (AMA)
Pai, Neng-Sheng& Yau, Her-Terng& Hung, Tzu-Hsiang& Hung, Chin-Pao. Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis. International Journal of Photoenergy. 2013. Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-509726
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-509726