Title: Hybrid machine intelligence-based improved classifiers for multiple power signal disturbances

Authors: Ranjeeta Bisoi; J.B.V. Reddy

Addresses: Multidisciplinary Research Cell, Siksha 'O' Anusandhan University, Bhubaneswar-751030, Odisha, India ' Department of Science and Technology, Technology Bhavan, New Mehrauli Road, New Delhi 110016, India

Abstract: This work presents a new technique for the identification of multiple power quality disturbances in industrial power systems. This new technique is developed based on change detection filter (CDF) and machine intelligence techniques like the local linear radial basis function neural network (LLRBFNN) and decision tree (DT). The proposed change detection filter has been designed in one cycle back fashion for the multiple non-stationary transient power signal disturbances for localisation, and feature extraction. The extracted features are fed as inputs to either a LLRBFNN or a decision tree-based classifier (DTC) for characterisation of the power quality events. The performance of LLRBFNN and DTC are evaluated and compared with local linear wavelet neural network (LLWNN), wavelet neural network (WNN), radial basis function neural network (RBFNN), etc., to highlight the superior performance of the proposed classifiers.

Keywords: radial basis function neural networks; RBFNN; power disturbance signals; local linear WNNs; wavelet neural networks; LLWNN; feature extraction; change detection filter; CDF; average filter; decision tree; hybrid machine intelligence; classifiers.

DOI: 10.1504/IJPEC.2014.065494

International Journal of Power and Energy Conversion, 2014 Vol.5 No.4, pp.375 - 401

Received: 09 Apr 2013
Accepted: 04 Jun 2013

Published online: 28 Oct 2014 *

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