Rotor Temperature Estimation Strategy for Induction Motors Based on Thermal Conductance Correction

Abstract

Estimating rotor temperature is essential for precise torque control and over-temperature warning in induction motors (IMs). Therefore, a rotor temperature estimation strategy is proposed in this study. First, a three-order lumped-parameter temperature network is developed to observe the rotor temperature online, and the parameters of the thermal network are identified offline by a genetic algorithm. However, the actual air-gap thermal conductance and end-space thermal conductance vary with operating conditions, and it would be unfavorable for temperature estimation if the thermal conductance is kept constant during operation. To address this issue, an adaptive law for air-gap thermal conductance and a temperature error feedback compensation matrix were designed using the Lyapunov stability theory. The advantage of the rotor temperature estimation strategy is that the error feedback of the stator temperature can be used to realize the correction of the air-gap thermal conductance. The temperature experiments show that the rotor temperature estimation strategy has higher temperature estimation accuracy than the conventional lumped-parameter temperature network. Meanwhile, torque experiments demonstrate that the torque control accuracy of the indirect magnetic field directional control is significantly improved.