The UHF method for measurement of partial discharge in oil-impregnated insulation

Chen, Qingguo; Gong, Xixiu; Gao, Wensheng; Li, Fuqi; Tan, Kexiong

doi:10.1109/icpadm.2003.1218448

Cited by 2 publications

(1 citation statement)

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“…Therefore, it is safer and more efficient to use dedicated monitoring systems, specially designed for online and real-time detection and monitoring of PD in substations. Alternative techniques [36][37][38] have been developed to decrease the hardware requirements of the monitoring system using the PD radiometric signal envelope. However, research in this field is still in its early stages, and there is a need to develop a system that can reduce the sampling requirements while retaining important information from the PD signal.…”

Section: Introductionmentioning

confidence: 99%

Identification of Partial Discharge Sources by Feature Extraction from a Signal Conditioning System

Carvalho,

da Costa,

Nobrega

et al. 2024

Sensors

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This paper addresses the critical challenge of detecting, separating, and classifying partial discharges in substations. It proposes two solutions: the first involves developing a signal conditioning system to reduce the sampling requirements for PD detection and increase the signal-to-noise ratio. The second approach uses machine learning techniques to separate and classify PD based on features extracted from the conditioned signal. Three clustering algorithms (K-means, Gaussian Mixture Model (GMM), and Mean-shift) and the Support Vector Machine (SVM) method were used for signal separation and classification. The proposed system effectively reduced high-frequency components up to 50 MHz, improved the signal-to-noise ratio, and effectively separated different sources of partial discharges without losing relevant information. An accuracy of up to 93% was achieved in classifying the partial discharge sources. The successful implementation of the signal conditioning system and the machine learning-based signal separation approach opens avenues for more economical, scalable, and reliable PD monitoring systems.

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Section: Introductionmentioning

confidence: 99%