Diagnosis of wiring networks using Particle Swarm Optimization and Genetic Algorithms

• Published in: Computers & electrical engineering Vol. 40; no. 7; pp. 2236 - 2245
• Main Authors: Smail, M.K., Bouchekara, H.R.E.H., Pichon, L., Boudjefdjouf, H., Mehasni, R.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2014; Elsevier
• Subjects: Computer simulation; Diagnosis; Engineering Sciences; Finite Difference Time Domain method; Genetic Algorithm; Genetic algorithms; Mathematical models; Networks; Particle Swarm Optimization; Reflectometry; Swarm intelligence; Time Domain Reflectometry; Wiring; Wiring diagnosis; Wiring diagnosis; Time Domain Reflectometry; Finite Difference Time Domain method; Particle Swarm Optimization; Genetic Algorithm
• ISSN: 0045-7906; 1879-0755
• DOI: 10.1016/j.compeleceng.2014.07.002

The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for wiring network diagnosis allowing the detection, localization and characterization of faults. Two complementary steps are addressed. In the first step the direct problem is modeled using RLCG circuit parameters. Then the Finite Difference Time Domain method is used to solve the telegrapher’s equations. This model provides a simple and accurate method to simulate Time Domain Reflectometry responses. In the second step the optimization methods are combined with the wire propagation model to solve the inverse problem and to deduce physical information’s about defects from the reflectometry response. Several configurations are studied in order to demonstrate the applicability of each approach. Further, in order to validate the obtained results for both inversion techniques, they are compared with experimental measurements.