기본 정보
연구 분야
프로젝트
발행물
구성원
article|
인용수 0
·2025
A Novel Classification Technique for Identifying Partial Discharge Sources in Rotating Machines
YongJoo Kim, Jin Lee
초록

A novel classification tool has been developed to analyze partial discharge patterns. This technique integrates features from the Peak Phase Dispersion Analysis (PPDA) Cross and the PPDA Cactus. It is intended to classify partial discharge patterns, including internal, slot, surface, and corona discharges. The PPDA Cross automatically generates key metrics, including the peak partial discharge (PD), the dispersion phase angle of the largest PD cluster, and the median phase angle of that cluster. On the other hand, the PPDA Cactus method clarifies the vertical characteristics of the phase-resolved partial discharge (PRPD) pattern in relation to the phase angle. This tool can simultaneously detect partial discharge signals from three-phase stator windings. A technique called three-phase signal discrimination (TPSD) has been introduced to improve measurement accuracy. This technique differentiates phase-related PD, external noise, and phase-to-phase PD, allowing PRPD data collection specific to each phase. Additionally, the instrument includes an innovative partial discharge sensor called the wide frequency band current transformer (WFBCT), which functions within a frequency range of 300 kHz to 60 MHz. This capability allows for acquiring PRPD patterns in voltage peak mode (VPM) and charge integration mode (CIM) under IEC60270.

키워드
Partial dischargeComputer scienceArtificial intelligenceEngineeringElectrical engineeringVoltage
타입
article
IF / 인용수
- / 0
게재 연도
2025