An improved squirrel search algorithm for parameter estimation of three-phase induction motors

Accurate motor parameters are critical for high-performance control and reliable fault diagnosis. This paper presents a new method for parameter estimation of three-phase induction motors (TIMs). Firstly, the two-phase rotating coordinate system is selected for motor modeling and the objective function is defined as minimizing the squared error between the actual and estimated stator currents. Then an improved squirrel search algorithm (ISSA) integrating adaptive sorting update and reproductive mechanisms is proposed to estimate the five parameters of TIMs. Finally, three cases are conducted to rigorously evaluate the performance of the ISSA, the conventional SSA, the particle swarm optimization (PSO), the whale optimization algorithm (WOA), and the extended Kalman-filter (EKF). The computational results indicate the superiority of the ISSA in convergence speed and estimation accuracy compared with other estimation algorithms. The proposed ISSA exhibits strong potential for adaptive motor control and condition monitoring, addressing both theoretical and practical challenges in parameter estimation.