Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features

Abstract

Electromagnetic Interference (EMI) is a technique for capturing Partial Discharge (PD) signals in High-Voltage (HV) power plant apparatus. EMI signals can be non-stationary which makes their analysis difficult, particularly for pattern recognition applications. This paper elaborates upon a previously developed software condition-monitoring model for improved EMI events classification based on time-frequency signal decomposition and entropy features. The idea of the proposed method is to map multiple discharge source signals captured by EMI and labelled by experts, including PD, from the time domain to a feature space, which aids in the interpretation of subsequent fault information. Here, instead of using only one permutation entropy measure, a more robust measure, called Dispersion Entropy (DE), is added to the feature vector. Multi-Class Support Vector Machine (MCSVM) methods are utilized for classification of the different discharge sources. Results show an improved classification accuracy compared to previously proposed methods. This yields to a successful development of an expert’s knowledge-based intelligent system. Since this method is demonstrated to be successful with real field data, it brings the benefit of possible real-world application for EMI condition monitoring.

Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features

“…Lower classification accuracy is observed in site 1. However, it is clear that an improvement in accuracy is achieved for each case, compared to [2]. An improvement of 8% is achieved for site 1 and 3 and an improvement of 14% and 4% is achieved for sites 2 and the common condition case respectively.…”

“…The downsides of an expert's analysis are the high costs, human time and impracticability for continuous monitoring. In [2] the authors introduced, for the first time, an automatic and continuous condition monitoring solution, based on a pattern recognition approach. The developed model is seen as a transfer of expert knowledge to a software model.…”

“…In this paper, we improve the performance of previous EMI discharge sources classification [2,[12][13][14][15], which aims to distinguish between a number of events including PD, Corona (C), Noise (N), Process Noise (PN) etc. Our previous method involves a state-of-the-art signal decomposition technique combined with an entropy measurement technique.…”

Separation of Magnetotelluric Signals Based on Refined Composite Multiscale Dispersion Entropy and Orthogonal Matching Pursuit

“…Dispersion entropy (DE) is proposed by Rostaghi M and Azami H in 2016, which is a nonlinear dynamics method to characterize the complexity and irregularity of the time series (Rostaghi and Azami 2016;Mitiche et al 2018).…”

Classification of Multiple Electromagnetic Interference Events in High-Voltage Power Plant

“…This paper aims to develop a software system that captures and utilises expert knowledge on EMI condition monitoring. This type of approach for EMI condition monitoring is relatively new and only a few publications exist at present [4]. Our approach exploits machine learning techniques which involve feature extraction and classification of EMI field data that represents the different discharges.…”