Parameter Estimation of Isotropic PMSMs Based on Multiple Steady-State Measurements Collected During Regular Operations

This article proposes a novel method to estimate the parameters of isotropic PMSMs which uses only steady-state measurements of load conditions commonly available during the regular operations of off-the-shelf industrial drives. Differently from existing online and offline approaches, the proposed method is designed considering real-world scenarios where ad-hoc tests, additional sensors and the implementation of custom software procedures, such as signal injection, are highly discouraged. The rotor flux linkage, the stator resistance and inductance are estimated with the aid of Adaline neural networks using two operating conditions of the motor. Considering parameter variations according to the actual operating conditions as well as the influence of the inverter nonlinearity and actuation delay, the estimation errors are minimized by proper selecting these two optimal conditions. The accuracy of the proposed method is validated by simulation and experimental studies considering scenarios with different number of motor operating conditions.