Optimization of Observer Feedback Gains for Stable Sensorless IM Drives at Very Low Frequencies: A Comparative Study between GA and PSO

Mohamed S. Zaky, Shaaban M. Shaaban, Tamer Fetouh, Haitham Z. Azazi and Yehya I. Mesalam
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Mohamed S. Zaky: Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 1321, Saudi Arabia
Shaaban M. Shaaban: Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 1321, Saudi Arabia
Tamer Fetouh: Department of Electrical Engineering, College of Engineering, Menoufia University, Shebin El-Kom 32511, Egypt
Haitham Z. Azazi: Department of Electrical Engineering, College of Engineering, Menoufia University, Shebin El-Kom 32511, Egypt
Yehya I. Mesalam: Department of Industrial Engineering, College of Engineering, Zagazig University, Zagazig 44519, Egypt

Mathematics, 2022, vol. 10, issue 10, 1-20

Abstract: Instability of an adaptive flux observer (AFO) in the regenerating mode at low frequencies is a great challenge of sensorless induction motor (SIM) drives. Zero observer feedback gains (OFGs) in the regenerating mode at low frequencies are the main reasons for moving the dominant zero of the speed estimators to the unstable region. OFGs should be appropriately selected to transfer the unstable dominant zero to the stable region. In this paper, genetic algorithm (GA) and particle swarm optimization (PSO) techniques were used to design the OFGs for a stable observer. A fair comparison of the dominant zero location between the two approaches using the optimized OFGs is presented under parameter deviation. Analytical results and the design procedure of the OFGs using the two approaches are presented under deviations of stator resistance and mutual inductance to guarantee a stable dominant zero in the regenerating mode of IM. The dominant zeros obtained by PSO had a superior location to that obtained by GA for both stator resistance and mutual inductance deviations. It was observed that one of the gains had an almost constant value over a wide range of parameter deviations. However, the value of the other gain was dependent on the deviation of machine parameters. The advantage of using PSO over GA is that the relation between the gain and parameter deviation can be represented by a deterministic and mostly linear relationship. Simulation and experimental work of the SIM drive are presented and evaluated under the optimized OFGs.

Keywords: sensorless control; genetic algorithms; particle swarm optimization; state feedback; induction motors (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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