Detection and classification of combined real time power quality disturbance signals with Hidden Markov Models incorporating wavelet features

Authors

  • S. Upadhyaya  Department of Electrical Engineering, SUIIT, Burla, Sambalpur India

Keywords:

Abstract

In this paper, Maximum Overlapping Discrete Wavelet Transform (MODWT) has been implemented along with the traditional discrete Wavelet Transform (DWT) for the detection and localization of different types of power quality (PQ) disturbance signals. Selected features have been extracted from the detail coefficient of the variants of WT and then fed as inputs to the classifiers in order to characterize the signals. Moreover, a comparative assessment of the PQ signal carried out with different classifiers such as Multilayer perceptron (MLP) and Hidden Markov Models (HMMs). Moreover, in order to represent in realistic environment, these proposed techniques are tested with signals captured from transmission line panels. Further, to aid this PQ disturbance detection, different types of real time fault signals are also characterized with these aforementioned approaches.

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International Journal of Scientific Research in Science and Technology

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Published

2018-06-30

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Volume 4, Issue 8, May-June-2018

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Research Articles

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[1]
S. Upadhyaya, " Detection and classification of combined real time power quality disturbance signals with Hidden Markov Models incorporating wavelet features, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4, Issue 8, pp.85-101, May-June-2018.