Stochastic Spheric Navigator Algorithm for High-Precision Parameter Estimation in Three-Phase Induction Motors Using Torque Data

Three-phase induction motors account for nearly two-thirds of industrial electricity consumption, making accurate parameter identification essential for efficiency optimization, predictive maintenance, and digital twin calibration. This paper introduces the stochastic spheric navigator algorithm (SSNA) for estimating the equivalent circuit parameters (stator and rotor resistances, leakage reactances, and magnetizing reactance) of induction motors by minimizing the normalized squared error between manufacturer-provided torque characteristics (starting, peak, and full-load) and their analytical counterparts derived from the steady-state Thévenin model. The SSNA employs an adaptive spherical search mechanism with a decaying radius schedule that progressively narrows the exploration neighborhood, enabling a balanced transition from global exploration to local refinement. Validated on 5 hp and 25 hp motors against the genetic algorithm (GA), particle swarm optimizer (PSO), hybrid GA-PSO, and sine–cosine algorithm (SCA), the SSNA demonstrates distinct advantages. For the 5 hp motor, it achieves the lowest errors in maximum torque (1.34×10−4%) and full-load torque (5.08×10−4%). For the previously unreported 25 hp motor, the SSNA yields an objective function value of 4.68×10−12—six orders of magnitude lower than the SCA—and reduces magnetizing reactance estimation error from 46.55% (SCA) to 16.18%. Statistical analysis over 100 independent runs reveals that the SSNA uniquely combines the lowest minimum (best) value, the lowest maximum (worst) value, and the lowest standard deviation, demonstrating superior accuracy, reliability, and consistency. These results position the SSNA as a highly competitive optimization framework for induction motor parameter identification, with particular suitability for applications demanding high precision and robust performance.