open access publication

Article, 2024

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

IET Science Measurement & Technology, ISSN 1751-8830, 1751-8822, 10.1049/smt2.12187

Contributors

Laib, Abderrzak [1] Chelabi, Mohamed 0000-0001-9592-4668 [2] Terriche, Yacine 0000-0002-9829-4638 [3] Melit, Mohammed 0000-0003-1917-2024 [2] Boudjefdjouf, Hamza 0000-0003-2120-6406 [4] Ahmed, Hafiz 0000-0001-8952-4190 (Corresponding author) [5] Chedjara, Zakaria [6]

Affiliations

  1. [1] Department of Electrical Engineering Faculty of technology, University of M'sila, M'sila, Algeria
    2. [NORA names: Algeria; Africa];
  2. [2] University of Jijel
    3. [NORA names: Algeria; Africa];
  3. [3] Ørsted Wind Power A/S, Fredericia, Denmark
    4. [NORA names: Denmark; Europe, EU; Nordic; OECD];
  4. [4] Department of Electrical Engineering, University of Freres Mentouri Constantine, Constantine, Algeria
    5. [NORA names: Algeria; Africa];
  5. [5] University of Sheffield
    6. [NORA names: United Kingdom; Europe, Non-EU; OECD];

Abstract

Abstract 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.

Keywords

Abstract, PSO-LSSVM, RLCG, accurate identification, algorithm, answers, application of time domain reflectometry, applications, approach, assessment, capacitance, city, communication, communication infrastructure, complex, complex wired networks, conductivity, consumers, damage, diagnosis, domain, efficient methodology, electricity, electricity infrastructure, error, experimental results, fault, fault diagnosis, finite difference time domain, grid, identification, improper installation, induction, infrastructure, installation, integration, inverse problem, machine, manufacturing, manufacturing errors, methodology, model, network, objective, optimization, parameters, particle swarm optimization, particles, plants, power, power grid, power plants, power system, power transmission, practice, prevent damage, real-world systems, reflectometry, reliability, research, resistance, results, risk, squares support vector machine, support vector machine, swarm optimization, system, system integration, system reliability, time domain, time domain reflectometry, transmission, urban power systems, vector machine, wire, wired networks

Data Provider: Digital Science

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