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Particle Swarm Optimization with Varied Social Network for Reliable Parameter Estimation in Thermal Analysis of Electrical Machines

Lookup NU author(s): Dr Rafal Wrobel

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Abstract

IEEEThis paper presents a new variant of particle swarm optimization (PSO) algorithm, which was developed for a reliable parameter estimation in thermal analysis of electrical machines. The proposed algorithm uses a varied social network, where both number and size of the network (local neighbourhoods) are randomly adjusted during the optimization process. Such approach has been introduced here to assure improved diversity of the PSO and consequently a more reliable and robust search of the solution space. A case study parameter estimation for a reduced-order thermal-equivalent-circuit (TEC) of an electrical machine has been used to demonstrate effectiveness of the proposed method. The analysed black-box parameter estimation relies on the input and output data (demand data) from a short-transient finite-element-analysis (FEA) of a complete machine assembly. The proposed PSO variant has been benchmarked with a selection of the existing state-of-the-art PSO algorithms, which employ alternative social network schemes with the network parameters dynamically varied. The statistical data gathered from multiple runs of the PSO-based estimation suggests that the proposed new approach offers considerable improvements in terms of accuracy, efficiency, reliability and robustness as compared with the alternative PSO algorithms.


Publication metadata

Author(s): Wrobel R

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Magnetics

Year: 2022

Pages: Epub ahead of print

Online publication date: 01/04/2022

Acceptance date: 02/04/2018

ISSN (print): 0018-9464

ISSN (electronic): 1941-0069

Publisher: IEEE

URL: https://doi.org/10.1109/TMAG.2022.3164254

DOI: 10.1109/TMAG.2022.3164254


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