Advanced particle swarm optimization for parameter identification of three-phase DFIM

Three-phase double-feed induction motors (DFIMs) have important applications such as producing the variable speed with constant frequency in industry, so, parameter identification of these motors has particular importance. Classic methods can be used for parameter identification of DFIMs, but using these methods needs to linearization and simplification of the model. This linearization leads to decrease the precision of parameter identification while random search methods such as evolutionary strategy (ES) and advanced particle swarm optimization (APSO) don´t require the linearization. Therefore, in this research, after describing the mathematical model of three-phase DFIM by equations of state, parameters of model are identified using APSO algorithm. Comparing between identified parameters by proposed method and evolutionary strategy (ES) shows that estimated parameters by APSO algorithm can simulate the behavior of three-phase DFIM more precise than another method (ES).