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.