Enhanced Clustering of DC Partial Discharge Pulses Using Multi-Level Wavelet Decomposition and Principal Component Analysis

Sung-Ho Yoon, Ik-Su Kwon, Jin-Seok Lim, Byung-Bae Park, Seung-Won Lee and Hae-Jong Kim ()
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Sung-Ho Yoon: Power Cable Research Center, Korea Electrotechnology Research Institute, Changwon-si 51543, Gyeongsangnam-do, Republic of Korea
Ik-Su Kwon: Power Cable Research Center, Korea Electrotechnology Research Institute, Changwon-si 51543, Gyeongsangnam-do, Republic of Korea
Jin-Seok Lim: Power Cable Research Center, Korea Electrotechnology Research Institute, Changwon-si 51543, Gyeongsangnam-do, Republic of Korea
Byung-Bae Park: Power Cable Research Center, Korea Electrotechnology Research Institute, Changwon-si 51543, Gyeongsangnam-do, Republic of Korea
Seung-Won Lee: Power Cable Research Center, Korea Electrotechnology Research Institute, Changwon-si 51543, Gyeongsangnam-do, Republic of Korea
Hae-Jong Kim: Power Cable Research Center, Korea Electrotechnology Research Institute, Changwon-si 51543, Gyeongsangnam-do, Republic of Korea

Energies, 2025, vol. 18, issue 18, 1-17

Abstract: Partial discharge (PD) is a critical indicator of insulation degradation in high-voltage DC systems, necessitating accurate diagnosis to ensure long-term reliability. Conventional AC-based diagnostic methods, such as phase-resolved partial discharge analysis (PRPDA), are ineffective under DC conditions, emphasizing the need for waveform-based analysis. This study presents a novel clustering framework for DC PD pulses, leveraging multi-level wavelet decomposition and statistical feature extraction. Each signal is decomposed into multiple frequency bands, and 70 distinctive waveform features are extracted from each pulse. To mitigate feature redundancy and enhance clustering performance, principal component analysis (PCA) is employed for dimensionality reduction. Experimental data were obtained from multiple defect types and measurement distances using a 22.9 kV cross-linked polyethylene (XLPE) cable system. The proposed method significantly outperformed conventional time-frequency (T-F) mapping techniques, particularly in scenarios involving signal attenuation and mixed noise. Propagation-induced distortion was effectively addressed through multi-resolution analysis. In addition, field noise sources such as HVDC converter switching transients and fluorescent lamp emissions were included to assess robustness. The results confirmed the framework’s capability to distinguish between multiple PD types and noise sources, even in challenging environments. Furthermore, optimal mother wavelet selection and correlation-based feature analysis contributed to improved clustering resolution. This framework supports robust PD classification in practical HVDC diagnostics. The framework can contribute to the development of real-time autonomous monitoring systems for HVDC infrastructure. Future research will explore incorporating temporal deep learning architectures for automated PD-type recognition based on clustered data.

Keywords: cable diagnostics; clustering; DC partial discharge; principal component analysis; wavelet decomposition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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