In-Situ Efficiency Estimation of Induction Motors Using Whale Optimization Algorithm

This paper investigates the in-situ efficiency prediction of induction motors using four optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA), and red fox optimization algorithm (RFO). Experimental evaluations were conducted on three induction motors with power ratings of 22 kW, 30 kW, and 132 kW under varying load conditions (25%, 50%, 75%, and 100%). The performance of the algorithms is tested not only under full load conditions but also under partial load conditions. This is an important requirement, given that motors usually do not run at full load in real-world applications. The algorithms were assessed based on their convergence behavior, accuracy, and experimentally measured efficiency values. The results revealed that the performance of the algorithms varies depending on the motor power and load level. While WOA is more successful at medium and high loads, PSO stands out at low loads. While GA provides higher accuracy, especially at full load on an motor, the performance of RFO varies according to the load level. In general, the performance of WOA and RFO stands out to some extent. The study demonstrates the advantages of non-intrusive methods for motor efficiency prediction that eliminate the need for direct shaft power measurements. It also offers practical benefits in industrial applications, such as reducing downtime and improving energy management.

Cite this article as: M. Göztaş, M. Çunkaş, and M.A. Şahman, “In-situ efficiency estimation of induction motors using whale optimization algorithm,” Turk J Electr Power Energy Syst., Published online April 7, 2025. doi 10.5152/tepes.2025.25001.