Identification of the induction motor parameters at standstill using soft computing methods

Abstract

PurposeThe paper sets out to deal with the off‐line identification of induction motor (IM) parameters at standstill. Determination of values of the IM parameters is essential in sensorless drives with regard to accuracy and quality of the control system.Design/methodology/approachThe presented identification method is based on the reconstruction of stator current response to the forced stator voltage step change; thus the cost function is defined in the classical form of the mean squared error between the computed and experimental data. The identification via evolutionary algorithms (EAs) is presented. The considered problem is continuous and thus a continuous version of EA is suggested as more suitable.FindingsThis approach has been shown to have several advantages over classical optimisation methods like the ability to cope with ill‐behaved problem domains exhibiting attributes such as: discontinuity, time‐variance, randomness, and, what is particularly important in this application, the ability to cope with the signals disturbed by noises. Owing to this ability the EAs could be implemented directly for the identification of IM parameters not only in simulations but also in the industrial applications for the motor control, though the electrical signals acquired from real motor and used as input data in the identification procedures are to a large extent disturbed by the electrical noises.Originality/valueTwo versions of the suggested approach are compared: the EA with hard selection and with soft selection. Both algorithms were tested in a simulation and experimental set‐up using digital signal processor for control and signal processing of the voltage inverter‐fed IM drive. Satisfactory results were obtained for the identification procedure based on the selected EA.

Application of Evolutionary Algorithms with Adaptive Mutation to the Identification of Induction Motor Parameters at Standstill

“…The performance of the EA of such kind applied to the IM equivalent circuit parametric identification at standstill has been investigated in advance and the results of those investigations are presented in [11]. The EA, which preformed best in that task has been chosen as the basic algorithm, to which the adaptive mutation scheme was then introduced.…”

“…In spite of the fact that EA is a relatively fast computational algorithm, it is still desirable to reduce the processing time in the view of the industrial implementation of the IM parameters identification procedure. Since the basic EA is an algorithm with greedy selection scheme (yet sufficient in case of the considered problem as the previous investigations revealed [11]) and is working with small population of individuals, the processing time mainly depends on the mutation operator. It is obvious that for such a type of an algorithm large mutation value is advantageous when the population is far from the optimum and it should decrease when the population approaches the vicinity of the optimum.…”

“…Few authors proposed evolutionary algorithms (EA) for the identification [1], [2], [6], [9]- [11]. In the most cases simple genetic algorithm (GA) was applied [1], [2], [6], [10], [11].…”

“…In other words a small value in Hamming distance (syntactic information) does not always imply a small variation in their fitness interpretation (semantic information). The performance of the simple EA operating in binary-coded and real-coded domain was investigated and compared in [11]. The test showed that solving the problem in binary-coded domain instead of the realcoded one is particularly unfavorable in case of the considered problem of the induction motor parameters' identification.…”

An Improved Particle Swarm Optimization for Induction Motor Parameter Determination

“…The evolutionary algorithm [10], genetic algorithm [11] - [15], adaptive GA [16], artificial neural network (ANN) [17] [18] and differential evolution [19] have been used for parameter determination of induction motor.…”