An effective identification of the induction machine parameters using a classic genetic algorithm, NSGA II and θ-NSGA III

Abstract

To remain competitive, the manufacturing industry is using computer processing power to innovate, develop and optimize new cost-efficient production strategies. This is the reason why optimization of automation systems is deployed to improve productivity, quality and robustness of the production. The different existing goals of optimization as the control machine, management of the power consumption, design of electrical installation and prediction of motor faults lead to the necessity of estimating the induction machine parameters (the stator and rotor resistances, the stator and rotor inductances and the magnetizing inductance). To these ends, researchers and companies are investigating efficient methods to identify these parameters. In this paper, we propose an effective method for the induction machine parameters identification based on the new -NSGA III genetic algorithm. A comparison between a classic single objective genetic algorithm (GA) and two well-known multiobjectives GAs (NSGA II and -NSGA III) is performed. Our results show that the multi-objective GA -NSGA III provides a better estimation of parameters than the classic single objective GA and the multi-objective GA NSGA II.

Heuristic algorithm-based estimation of rotor resistance of an induction machine by slot parameters with experimental verification

“…In both Eqs. (1) and (2) two unknown values, air gap flux density and pole pitch factor, can be assumed as 0.75 T and 0.729, which both satisfy induction machine design limitations. On the other hand, total flux and E 1 , the voltage value at the stator windings voltage drop, which is about 2% for this machine, must match with the winding number…”

“…The correct calculation of the torque and speed and determination of equivalent circuit parameters accurately ensures the analysis of the motor in the right way. Since the fluxes on α and β axes are difficult to determine theoretically based on voltage and current in real-time applications, artificial neural networks [1], genetic algorithm (GA) [2][3][4][5], or particle swarm optimization (PSO) [6][7][8] and algorithm comparison (GA-PSO, GA-modified GA, PSO-modified PSO, GA-cuckoo alg., etc.) [9][10][11][12][13][14] can be used.…”

Many-objective Optimization of IPM and Induction Motors for Automotive Application

“…A widely used evolutionary multi-objective algorithm is the Non-dominated Sorting Genetic Algorithm II (NSGA-II) [10]. NSGA-II is based on an elitist principle, highlights non-dominated solutions and attempts to maintain the appropriate diversity by using a crowding distance operator [11], [12].…”