Enhanced modelling of the transformer winding high frequency parameters identification from measured frequency response analysis

• Published in: IET generation, transmission & distribution Vol. 13; no. 8; pp. 1339 - 1345
• Main Authors: Chanane, Abdallah, Houassine, Hamza, Bouchhida, Ouahid
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 23.04.2019
• Subjects: air core; equivalent circuit; frequency response; frequency response analysis; genetic algorithm method; genetic algorithms; high frequency parameters identification; luxurious time‐efficient computation method; modernised methods; particle swarm optimisation; physical behaviour; PSO‐SA; Research Article; search problems; simulated annealing; simulated annealing method; transformer winding; transformer windings; high frequency parameters identification; frequency response analysis; simulated annealing; simulated annealing method; particle swarm optimisation; genetic algorithms; transformer winding; physical behaviour; air core; transformer windings; modernised methods; equivalent circuit; genetic algorithm method; PSO-SA; luxurious time-efficient computation method; search problems; frequency response
• ISSN: 1751-8687; 1751-8695; 1751-8695
• DOI: 10.1049/iet-gtd.2018.5514

One way to represent the physical behaviour of the transformer winding under high frequencies is to construct an equivalent circuit using modernised methods. This study represents a luxurious and time-efficient computation method based on particle swarm optimisation with simulated annealing (PSO-SA) method to construct an equivalent circuit of a transformer winding, from its frequency response analysis. The proposed method uses a simulated annealing method as a local search tool to improve the convergence and the characteristics of particle swarm optimisation in fast calculation. Furthermore, a comparative study of PSO-SA, crow search algorithm and genetic algorithm method is made. Different case studies of a transformer winding with an air core are considered. According to the results, the PSO-SA has higher efficiency and faster convergence.