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GPU-accelerated parallel coevolutionary algorithm for parameters identification and temperature monitoring in permanent magnet synchronous machines
journal contributionposted on 28.09.2016, 08:59 by Zhao-Hua Liu, Xiao-Hua Li, Liang-Hong Wu, Shao-Wu Zhou, Kan Liu
A hierarchical fast parallel co-evolutionary immune particle swarm optimization (PSO) algorithm, accelerated by graphics processing unit (GPU) technique (G-PCIPSO), is proposed for multiparameter identification and temperature monitoring of permanent magnet synchronous machines (PMSM). It is composed of two levels and is developed based on compute unified device architecture (CUDA). In G-PCIPSO, the antibodies (Abs) of higher level memory are selected from the lower level swarms and improved by immune clonal-selection operator. The search information exchanges between swarms using the memory-based sharing mechanism. Moreover, an immune vaccine-enhanced operator is proposed to lead the Pbests particles to unexplored areas. Optimized parallel implementations of G-PCIPSO algorithm is developed on GPU using CUDA, which significantly speeds up the search process. Finally, the proposed algorithm is applied to multiple parameters identification and temperature monitoring of PMSM. It can track parameter variation and achieve temperature monitoring online effectively. Compared with a CPU-based serial execution, the computational efficiency is greatly enhanced by GPU-accelerated parallel computing technique.
This work was supported in part by the China Postdoctoral Science Foundation funded project under Grant 2013M540628 and Grant 2014T70767, in part by the National Natural Science Foundation of China under Grant 61174140 and Grant 51374107, and in part by the Hunan Provincial Natural Science Foundation of China under Grant 13JJ8014 and Grant 14JJ3107. Paper no. TII-14-0700.
- Mechanical, Electrical and Manufacturing Engineering