Smart Collaborative Performance-Induced Parameter Identification Algorithms for Synchronous Reluctance Machine Magnetic Model

In rail transit traction, due to the remarkable energy-saving and low-cost characteristics, synchronous reluctance motors (SynRM) may be a potential substitute for traditional AC motors. However, in parameter extraction of SynRM nonlinear magnetic model, accuracy robustness metaheuristic algorithm is restricted by excessive dependence on fitness evaluation. this paper, novel probability-driven smart collaborative performance (SCP) defined quantify comprehensive contribution candidate solution current population. With quantitative results SCP as feedback in-formation, an updating mechanism with improved evolutionary quality established. The allocation computing resources induced achieves good balance between exploration exploitation. Comprehensive experiment demonstrate better effectiveness SCP-induced algorithms proposed machine model. Accuracy are ranked first comparison result statistics other well-known algorithms.