• S. Upadhyaya

In this paper, Second generation Wavelet Transform (SGWT) has been implemented along with the traditional discrete Wavelet Transform (DWT) for 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 Multilayer perceptron (MLP) and Hidden Markov Models (HMMs). For localisation of power quality disturbances, the signals have been analysed upto four decomposition levels whereas for classification the signals are decomposed for seven decomposition levels. In order to represent in realistic environment, these proposed techniques are tested with the three phase voltage signals captured from transmission line panels. Further, to aid these PQ disturbance detection, different types of real time transmission line fault signals are also characterized with these aforementioned approaches.

Comparison between ANN and HMMs Techniques for analysis of Real Time Power Quality Disturbance Signals incorporating SGWT features

"Comparison between ANN and HMMs Techniques for analysis of Real Time Power Quality Disturbance Signals incorporating SGWT features", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 5, page no.636-648, May-2018, Available :http://www.jetir.org/papers/JETIR1805527.pdf

"Comparison between ANN and HMMs Techniques for analysis of Real Time Power Quality Disturbance Signals incorporating SGWT features", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 5, page no. pp636-648, May-2018, Available at : http://www.jetir.org/papers/JETIR1805527.pdf