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Activity Number: 307 - Novel Approaches for Analyzing Dynamic Networks
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #307043 Presentation 1 Presentation 2
Title: Developing New Statistical Pattern Recognition and System Identification Techniques for Partial Discharge Analysis
Author(s): Pramoda Sachinthana Jayasinghe* and Mohammad Jafari Jozani and Behzad Kordi
Companies: University of Manitoba and University of Manitoba and University of Manitoba
Keywords: Statistical Learning; Partial Discharges; System Identification; Classification; Laguerre functions

Partial discharge (PD) in power transmission systems can be harmful to insulators, the equipment that they are connected to and in the long run, can also lead to more dangerous outcomes. Therefore, identifying partial discharges at an earlier stage is important. Our aim of this analysis is to develop a methodology to identify the system that caused a partial discharge.

In this study, we analyze the input and output signals to a partial discharge source in an experimental setting. We propose using an orthogonal expansion approach based on Laguerre functions to first approximate each signal. Then using properties of Laguerre polynomials and deconvolution theory, we were able to obtain a recursive formula that can be used to get a mathematical expression for the system.

The characteristics of the system then can be used for the purpose of classifying the signals to their respective sources.

Authors who are presenting talks have a * after their name.

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