Single phase induction motors (SPIM) are widely used in residential and commercial applications. Enhancement of efficiency of SPIMs can lead to huge energy savings. This paper presents a novel mechanical sensorless control method for SPIM drives. In this method, a machine learning algorithm is used to estimate the slip based on the ratio of main and auxiliary winding currents. To enhance the efficiency, the terminal voltage is reduced under light load conditions. The optimal operating voltage is implicitly obtained by equating the ratio of main and auxiliary winding currents to its optimum value. This optimal operating point is first calculated based on the frequency from a lookup table and then updated by using gradient descent algorithm. This way, the optimal operating point is realized despite motor parameter variations. The proposed scheme is suitable for low-power applications where working at different speeds and load torques is demanded, such as ventilation systems and various household appliances. Simulation results are presented to verify the efficacy of the proposed method.
An Improved Position-Sensorless Control Method at Low Speed for PMSM Based on High-Frequency Signal Injection into a Rotating Reference Frame
