Enhanced Method for Localization of Partial Discharges in Oil-Filled Transformers Using Acoustic Emission Signals

Enhanced Method for Localization of Partial Discharges in Oil-Filled Transformers Using Acoustic Emission Signals

Accurate measurement of partial discharges (PD) in power transformers is crucial for fault identification and effective maintenance planning. Acoustic emission (AE) sensing, less susceptible to external electromagnetic interference, offers non-invasive PD detection. This paper investigates the relat...

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Bibliographic Details
Main Authors: Yasutomo Otake, Kunihiko Tajiri
Format: Article
Language:English
Published: The Prognostics and Health Management Society 2024-10-01
Series:International Journal of Prognostics and Health Management
Subjects:
power transformer
partial discharge
electrical insulation
acoustic emission
tdoa
sound source localization
Online Access:https://papers.phmsociety.org/index.php/ijphm/article/view/3852
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Summary:Accurate measurement of partial discharges (PD) in power transformers is crucial for fault identification and effective maintenance planning. Acoustic emission (AE) sensing, less susceptible to external electromagnetic interference, offers non-invasive PD detection. This paper investigates the relationship between AE detection intensity, PD source type, and distance using AE sensors. Corona discharges exhibited the strongest AE wave intensities, followed by creepage discharges and PD in bubbles. AE wave intensity varies significantly based on discharge propagation speed, medium, and discharge space volume. The study experimentally compared three Time Difference of Arrival (TDOA) calculation methods for localization: energy criterion, Generalized Cross-Correlation (GCC), and GCC with Phase Transformation (PHAT). The energy criterion excelled in distributed sensor setups, while GCC-PHAT was superior in centralized configurations. GCC-PHAT, effectively suppressing noise and reflections, consistently outperformed standard GCC in accuracy, even at low discharge intensities. These findings promise improved precision and effectiveness in power transformer maintenance diagnostics.
ISSN:2153-2648

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