Classification of arc fault between broken conductor and high-impedance surface: an empirical mode decomposition and Stockwell transform-based approach

• Published in: IET generation, transmission & distribution transmission & distribution, 2020-11, Vol.14 (22), p.5277-5286
• Main Authors: Lala, Himadri, Karmakar, Subrata
• Format: Article
• Language: English
• Subjects: appropriate signal processing technique; arcs (electric); broken conductor; different arcing surfaces; empirical mode decomposition; fault diagnosis; feature extraction; HIAF; high‐impedance arc fault; high‐impedance surface; intrinsic mode functions; learning (artificial intelligence); machine learning algorithm; medium voltage distribution line; power distribution faults; power engineering computing; real‐time arc voltage signals; real‐time arcing voltage signals; Research Article; signal processing; Stockwell transform‐based approach; time‐frequency analysis; time‐frequency based analysis; transforms
• ISSN: 1751-8687; 1751-8695
• DOI: 10.1049/iet-gtd.2020.0340

The high-impedance arc fault (HIAF) is a result of the interaction between the current-carrying conductor and the high-impedance surface. This study presents a detailed and comparative time-frequency based analysis for the classification of different arcs, generated by the interaction of a broken conductor and different arcing surfaces. The real-time arcing voltage signals are considered as the basis of the whole time-frequency based analysis from a medium voltage distribution line. In this proposed approach, empirical mode decomposition (EMD) is used to study the real-time arc voltage signals of various surfaces. The intrinsic mode functions obtained by the application of the EMD technique is used as the input to various machine learning techniques, which later successfully classifies different HIAFs from their harmonic footprints. The Stockwell transform (ST) is additionally applied to the same test cases, and the classification of the arc is performed using similar learning algorithms to get a quantitative view of both outcomes of EMD and ST towards the selection of appropriate signal processing technique and machine learning algorithm for the same.