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

Authors(1) :-S. Upadhyaya

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.
S. Upadhyaya
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Publication Details
  Published in : Volume 4 | Issue 8 | May-June 2018
  Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 85-101
Manuscript Number : IJSRST184825
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
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), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 4, Issue 8, pp.85-101, May-June-2018
URL : http://ijsrst.com/IJSRST184825