GPU-Accelerated Parallel Coevolutionary Algorithm for Parameters Identification and Temperature Monitoring in Permanent Magnet Synchronous Machines


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 2 levels and is developed primarily based on compute unified device design (CUDA). In G-PCIPSO, the antibodies (Abs) of upper 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 mostly sharing mechanism. Moreover, an immune vaccine-enhanced operator is proposed to steer the Pbests particles to unexplored areas. Optimized parallel implementations of G-PCIPSO algorithm is developed on GPU using CUDA, that considerably quickens the search process. Finally, the proposed algorithm is applied to multiple parameters identification and temperature monitoring of PMSM. It will track parameter variation and achieve temperature monitoring on-line effectively. Compared with a CPU-based serial execution, the computational efficiency is greatly enhanced by GPU-accelerated parallel computing technique.

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