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Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks
Joint Authors
Talebi, Nasser
Darabi, Ahmad
Sadrnia, Mohammad Ali
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-03-11
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Occurrence of faults in wind energy conversion systems (WECSs) is inevitable.
In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required.
Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems.
One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior.
By comparing the outputs of the real system and neural model, incidence of the faults can be identified.
In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs.
The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators.
Robustness of the FDS is achieved by employing an adaptive threshold.
Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.
American Psychological Association (APA)
Talebi, Nasser& Sadrnia, Mohammad Ali& Darabi, Ahmad. 2014. Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks. Computational Intelligence and Neuroscience،Vol. 2014, no. 2014, pp.1-13.
https://search.emarefa.net/detail/BIM-482464
Modern Language Association (MLA)
Talebi, Nasser…[et al.]. Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks. Computational Intelligence and Neuroscience No. 2014 (2014), pp.1-13.
https://search.emarefa.net/detail/BIM-482464
American Medical Association (AMA)
Talebi, Nasser& Sadrnia, Mohammad Ali& Darabi, Ahmad. Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks. Computational Intelligence and Neuroscience. 2014. Vol. 2014, no. 2014, pp.1-13.
https://search.emarefa.net/detail/BIM-482464
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-482464