2134/22596 Zhao-Hua Liu Zhao-Hua Liu Xiao-Hua Li Xiao-Hua Li Liang-Hong Wu Liang-Hong Wu Shao-Wu Zhou Shao-Wu Zhou Kan Liu Kan Liu GPU-accelerated parallel coevolutionary algorithm for parameters identification and temperature monitoring in permanent magnet synchronous machines Loughborough University 2016 Graphics processing units Sociology Parameter estimation Monitoring Temperature measurement Temperature sensors Mechanical Engineering not elsewhere classified 2016-09-28 08:59:51 Journal contribution https://repository.lboro.ac.uk/articles/journal_contribution/GPU-accelerated_parallel_coevolutionary_algorithm_for_parameters_identification_and_temperature_monitoring_in_permanent_magnet_synchronous_machines/9547100 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.