Enhanced complex wire fault diagnosis via integration of time domain reflectometry and particle swarm optimization with least square support vector machine

• Published in: IET science, measurement & technology Vol. 18; no. 8; pp. 417 - 428
• Main Authors: Laib, Abderrzak, Chelabi, Mohamed, Terriche, Yacine, Melit, Mohammed, Boudjefdjouf, Hamza, Ahmed, Hafiz, Chedjara, Zakaria
• Format: Journal Article
• Language: English
• Published: Wiley 01.10.2024
• Subjects: fault diagnosis; time‐domain reflectometry
• ISSN: 1751-8822; 1751-8830; 1751-8830
• DOI: 10.1049/smt2.12187

Urban power systems rely on intricate wire networks, known as the power grid, which form the essential electric infrastructure in cities. While these networks transmit electricity from power plants to consumers, they are vulnerable to faults caused by manufacturing errors and improper installation, posing risks to system integrity. Thus, accurate identification and assessment of these faults are crucial to prevent damage and maintain system reliability. The objective of this research is to present an innovative and efficient methodology for diagnosing complex wire networks through the application of time domain reflectometry (TDR) combined with the particle swarm optimization (PSO) and least squares support vector machine (LSSVM) algorithm. This research addresses the imperative need to accurately locate and assess breakage faults within wire networks, emphasizing their role in both power transmission and communication infrastructure. To model the TDR answer of a specific complex wire network, a forward model is established utilizing resistance, inductance, capacitance and conductance (RLCG) parameters and the finite difference time domain (FDTD) method. Subsequently, the PSO‐LSSVM approach is used to solve the inverse problem of localizing faults in complex wire networks. The experimental results validate the practicality of this approach in real‐world systems. Overview of the particle swarm optimization (PSO) method‐based tuning approach for the least square support vector machine (LSSVM) model developed in this work to diagnose faults in complex wire networks.