Automated Classification of Power Quality Disturbances using Hilbert Huang Transform and RBF Networks
T. Jayasree1, D. Sam Harrison2, T. Sree Rangaraja3
1T. Jayasree, Sr.Lecturer, Govt. Polytechnic College, Nagercoil, Kanyakumari Dist, Tamilnadu, India.
2D. Sam Harrison, Assistant Professor, C.S.I. Institute of Technology, Kanya Kumari, Tamilnadu, India.
3T. Sree Rangaraja, Associate Professor, Anna university of Technology, Tiruchirappalli, Tamilnadu, India.
Manuscript received on October 06, 2011. | Revised Manuscript received on October 22, 2011. | Manuscript published on November 05, 2011. | PP: 217-223 | Volume-1 Issue-5, November 2011. | Retrieval Number: E0197101511/2011©BEIESP
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© The Authors. Published By: 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: This paper presents Radial Basis Function Neural Network based approach for automatic Power Quality (PQ) disturbance classification. The input features of the Neural Network are extracted using Hilbert Huang Transform (HHT) and they are given as input to the Radial Basis Function Neural network. The data required to develop the network are generated by creating various faults in a test system. The proposed method requires less number of features and less memory space without losing its original property.
Keywords: PQ, HHT