Toggle Main Menu Toggle Search

Open Access padlockePrints

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

Lookup NU author(s): Dr Rafal Wrobel



This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by IEEE, 2022.

For re-use rights please refer to the publisher's terms and conditions.


This paper presents a 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 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 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: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 23rd International Conference on the Computation of Electromagnetic Fields (COMPUMAG 2021)

Year of Conference: 2022

Online publication date: 16/01/2022

Acceptance date: 26/09/2021

Date deposited: 17/03/2022

Publisher: IEEE