A Cable Defect Assessment Method Based on a Mixed-Domain Multi-Feature Set of Overall Harmonic Signals

Ruidong Wang and Ruzheng Pan ()
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Ruidong Wang: School of International Education, Hebei University of Technology, Tianjin 300401, China
Ruzheng Pan: School of International Education, Hebei University of Technology, Tianjin 300401, China

Energies, 2024, vol. 18, issue 1, 1-15

Abstract: This paper presents a cable defect assessment method based on a mixed-domain multi-feature set derived from overall harmonic signals. Four typical defect types—thermal ageing, cable moisture, excessive bending, and insulation damage—were simulated under laboratory conditions. Grounding current tests and Variational Mode Decomposition (VMD) time series analysis were performed on the test samples to extract the overall harmonic sequences in the grounding current. Mixed-domain multi-feature set is then formed through feature extraction and validity analysis. To optimize the assessment performance, a Support Vector Machine (SVM) classifier optimized by the Sparrow Search Algorithm (SSA) was constructed. The results show that different defects lead to significantly differentiated harmonic distortions in the grounding currents, which has proved to be a reliable data basis for cable defect assessment. The proposed method refines the data information and achieves the most accurate recognition of cable defects, which may contribute to the reliable operation of power networks.

Keywords: power cable; harmonic sequence; mixed-domain feature extraction; defect recognition (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: 2024
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