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<p>The weakness or defect in electrical insulations used in High Voltage operations always leads to generation of partial discharges (PD). Partial discharges (PD) are a common by product of insulation defects in high-voltage systems. Detecting PD is a crucial aspect of power system condition monitoring. The nature of PD signals varies depending on their source, and large substations often have multiple PD sources. Ultra-high frequency (UHF) sensors offer a cost-effective and safe method for PD detection. Multiple sensors can be mounted around a substation, capturing a mixed PD signal. The Separating individual PD signals from this mix is challenging. The Techniques like the Gaussian mixture model (GMM) and Self-Organizing Feature Map (SOFM) have shown promise in this task. GMM uses time and frequency domain features, while SOFM employs continuous wavelet transform (CWT) time-frequency features. This study compares the effectiveness of these techniques for PD detection and localization using both laboratory and field experiments.</p>

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Comparative Performance of Blind Source Separation Techniques for Partial Discharge Detection in Electrical Substations

  • 期刊论文

  • 发表日期：

    2025年5月6日

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  • Dipak Kumar Mishra,
  • Ramesh Kumar,
  • Manish Kumar Singla,
  • Mohammed H. Alsharif,
  • Zong Woo Geem

摘要

The weakness or defect in electrical insulations used in High Voltage operations always leads to generation of partial discharges (PD). Partial discharges (PD) are a common by product of insulation defects in high-voltage systems. Detecting PD is a crucial aspect of power system condition monitoring. The nature of PD signals varies depending on their source, and large substations often have multiple PD sources. Ultra-high frequency (UHF) sensors offer a cost-effective and safe method for PD detection. Multiple sensors can be mounted around a substation, capturing a mixed PD signal. The Separating individual PD signals from this mix is challenging. The Techniques like the Gaussian mixture model (GMM) and Self-Organizing Feature Map (SOFM) have shown promise in this task. GMM uses time and frequency domain features, while SOFM employs continuous wavelet transform (CWT) time-frequency features. This study compares the effectiveness of these techniques for PD detection and localization using both laboratory and field experiments.