Digital detection and fuzzy classification of partial discharge signals

TLDR

The paper addresses digital acquisition, classification, and stochastic analysis of random pulse signals generated by partial discharge phenomena. The study develops a high‑sampling‑rate measuring system for digital acquisition of PD‑pulse signals that yields enough pulses for stochastic analysis. A fuzzy‑classifier‑based separation and classification method is applied to the acquired PD‑pulse shape signals, with efficiency assessed through pulse‑height and phase‑distribution analysis, and the instrumentation is used to record PD data from mica‑insulated stator bars and coils with simulated defects. Fuzzy classification clusters signals with homogeneous stochastic features, enables identification of PD sources when multiple sources are active, and provides an efficient noise‑rejection tool.

Abstract

This paper deals with digital acquisition, classification and analysis of the stochastic features of random pulse signals generated by partial discharge (PD) phenomena. Focus is made on a new measuring system for the digital acquisition of PD-pulse signals, which operates at a sampling rate high enough to avoid the frequency aliasing, but that provides an amount of PD pulses which enables PD stochastic analysis. A separation and classification method, based on a fuzzy classifier, is developed for the analysis of the acquired PD-pulse shape signals. The result of the fuzzy classification is a cluster of signals homogeneous in terms of stochastic features of PD pulses. The classification efficiency is evaluated resorting to the PD-pulse height and phase distributions analysis. The instrumentation, and the associated classification methodology, are applied to measure and analyze PD data recorded for mica-insulated stator bars and coils, where typical defects, occurring during normal operations, were simulated. It is shown that the proposed procedure enables PD-source identification to solve the identification problems which arise, in particular, when different sources of PD are simultaneously active. In addition fuzzy classification provides an efficient noise-rejection tool.