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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

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.

Details

Title
Online Efficiency Optimization and Speed Sensorless Control of Single-Phase Induction Motors
Author
Golsorkhi, Mohammad S 1   VIAFID ORCID Logo  ; Binandeh, Hadi 2 ; Savaghebi, Mehdi 3   VIAFID ORCID Logo 

 Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 8415683, Iran; [email protected] 
 Department of Electrical Engineering, Amirkabir University of Technology, Tehran 1591634311, Iran; [email protected] 
 Electrical Engineering Section, Department of Mechanical and Electrical Engineering, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark 
First page
8863
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
20763417
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2580960585
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.