Neural Network Controlled Primitive Fault Analysis and Monitoring of Wind Turbine Gear Box
B. Raja Mohamed Rabi1, Kanimozhi Kannabiran2
1B. Raja Mohamed Rabi, Department of Mechanical Engineering, Sethu Institute of Technology, Kariapatti, Tamil Nadu, India.
2K.Kanimozhi, Department of Electrical and Electronics Engineering, Sethu Institute of Technology, Kariapatti, Tamil Nadu, India.
Manuscript received on 01 March 2019 | Revised Manuscript received on 05 March 2019 | Manuscript published on 30 July 2019 | PP: 764-768 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2404078219/19©BEIESP | DOI: 10.35940/ijrte.B2404.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The problem considered in this paper is minimization of operational and maintenance costs of Wind Energy Conversion Systems (WECS). A continuous condition monitoring system is to be designed for reducing these costs. Hence preliminary identification of the degeneration of the generator health, facilitating a proactive response, minimizing downtime, and maximizing productivity is made possible. The inaccessibility of Wind generators situated at heights of 30m or more height also creates problem in condition monitoring and fault diagnosis. This opens up the research on condition monitoring and fault diagnosis in WECS (blades, drive trains, and generators). Therefore different type of faults, their generated signatures, and their diagnostic schemes are discussed in this paper. The paper aims in validating the application of neural networks for the analysis of wind turbine data, so that possible future failures may be predicted and rectified earlier.
Index Terms: Condition Monitoring, Drive Train, Generator, Primitive Fault Diagnosis, Wind Energy Conversion Systems.
Scope of the Article: Analysis of Algorithms and Computational Complexity