Investigation of the transformer winding high-frequency parameters identification using particle swarm optimisation method

• Published in: IET electric power applications Vol. 10; no. 9; pp. 923 - 931
• Main Authors: Chanane, Abdallah, Bouchhida, Ouahid, Houassine, Hamza
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.11.2016
• Subjects: air core; Diagnostic systems; genetic algorithm; genetic algorithms; high‐frequency parameter identification; high‐frequency transformer winding; intelligent method; Optimization; Parameter identification; Parameters; particle swarm optimisation; particle swarm optimisation method; PSO method; Robustness; Swarm intelligence; transfer function; transfer functions; transformer windings; Transformers; Winding; transfer function; genetic algorithm; transformer windings; particle swarm optimisation; intelligent method; high-frequency transformer winding; PSO method; particle swarm optimisation method; transfer functions; genetic algorithms; high-frequency parameter identification; air core
• ISSN: 1751-8660; 1751-8679; 1751-8679
• DOI: 10.1049/iet-epa.2016.0074

Intelligent methods are used more and more nowadays, in various fields, whose main purpose is to find better solutions to problems that are considered difficult. Identifying the parameters of transformer winding is a crucial problem. In this study, the authors shall use the ‘particle swarm optimisation’ (PSO) method to identify the parameters of high-frequency transformer winding, for diagnostic purposes. They validate the approach by comparing their results with the experimental ones. Two cases study of windings with air core were considered. Not only the effectiveness and accuracy of the proposed method is verified, the PSO method is compared with another methods referred to as transfer function and genetic algorithm. The results are in total agreement and show the reliability and the robustness of the PSO method.