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

Abstract

This article 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 neighborhoods) 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 analyzed 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.