SVM and RVM Fault Model for Wind Energy Conversion Systm
Rekha S.N1, P. Aruna Jeyanthy2, D. Devaraj3
1Rekha S. N, Department of Electrical and Electronics Engineering, Sapthagiri college of Engineering, Bangalore (Karnataka), India.
2P. Aruna Jeyanthy, Department of Electrical and Electronics Engineering, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
3D. Devaraj, Department of Electrical and Electronics Engineering, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
Manuscript received on 29 November 2019 | Revised Manuscript received on 18 December 2019 | Manuscript Published on 31 December 2019 | PP: 366-371 | Volume-8 Issue-4S2 December 2019 | Retrieval Number: D10821284S219/2019©BEIESP | DOI: 10.35940/ijrte.D1082.1284S219
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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 paper present the fault identification technique for wind Energy conversion system based on SVM (support vector machines) and RVM. A Relevance Vector Machine based fault detection technique and support vector machine fault detection technique with the Benchmark Model of the WECS is carried out with Multi class classification… The proposed implementation would carry out simulation which would consider multiple faults occurring simultaneously with a comparison study of both techniques can be achieved. The algorithm is carried out and the results are found to be satisfactory. The results in MATLAB shows that effective memory usage of each technique.
Keywords: Relevance Vector Machine, Support Vector Machine, Wind Fault Benchmark Model, Wind Generation Fault Diagnosis.
Scope of the Article: RF Energy Harvesting