Title: Heterogeneous multi-subswarm particle swarm optimisation for numerical and parameter estimation of PMSM

Authors: Guohan Lin; Qin Wan

Addresses: Hunan Province Cooperative Innovation Center for Wind Power Equipment and Energy Conversion, Hunan Institute of Engineering, Hunan, Xiangtan 411104, China ' College of Electrical and Information, Hunan Institute of Engineering, Hunan, 411104, China

Abstract: A heterogeneous multi-subswarm coevolution particle swarm optimisation (HMSCPSO) is proposed for numerical optimisation and parameters identification of PMSM. To improve the algorithm's dynamic optimal performance, the HMSCPSO consists of one adaptive subswarm and several basic subswarms. During the iteration, the best individual in basic subswarm and adaptive subswarm are selected as candidate to construct the elite subswarm. Heterogeneous search strategy was adopted in basic subswarm and adaptive subswarm. The migration scheme is employed for the information exchange between subswarms. The adaptive inertia weight strategy can maintain a balance between exploration and exploitation to ensure the algorithm converges to stable point. To accelerate the convergence rate, immune clonal selection operator with wavelet mutation is applied to elite subswarm. The performance of the proposed algorithm is extensively evaluated on suite of numerical optimisation functions. The results demonstrate good performance of the HMSCPSO in solving numerical problems when compared with others recent variants PSO. The performance of HMSCPSO is further evaluated by its application to the parameters identification of PMSM. The experimental results show that the HMSCPSO can simultaneously identify stator resistance, dq axis inductances and the permanent magnet flux accurately.

Keywords: particle swarm optimisation; coevolution; heterogeneous search; parameter identification; permanent magnet synchronous motor.

DOI: 10.1504/IJWMC.2017.087347

International Journal of Wireless and Mobile Computing, 2017 Vol.13 No.1, pp.51 - 62

Received: 25 Nov 2016
Accepted: 15 Mar 2017
Published online: 13 Oct 2017 *

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