Parameter Identification of Inverter-Fed Induction Motors: A Review

Abstract

Induction motor parameters are essential for high-performance control. However, motor parameters vary because of winding temperature rise, skin effect, and flux saturation. Mismatched parameters will consequently lead to motor performance degradation. To provide accurate motor parameters, in this paper, a comprehensive review of offline and online identification methods is presented. In the implementation of offline identification, either a DC voltage or single-phase AC voltage signal is injected to keep the induction motor standstill, and the corresponding identification algorithms are discussed in the paper. Moreover, the online parameter identification methods are illustrated, including the recursive least square, model reference adaptive system, DC and high-frequency AC voltage injection, and observer-based techniques, etc. Simulations on selected identification techniques applied to an example induction motor are presented to demonstrate their performance and exemplify the parameter identification methods.

Stochastic Search Technique with Variable Deterministic Constraints for the Estimation of Induction Motor Parameters

“…In general, optimization techniques differ in the method used to search the model parameters, that is, how a new set of parameters is selected in each iteration, and the resulting behavior then generates a smaller error compared to the error of the previous iteration. The search can be deterministic, which produce the same outputs invariably before the same entries without considering the existence of chance or the uncertainty principle, or stochastic (not deterministic), which are used in implementations, such as genetic algorithms [7,8], particle swarm optimization [9][10][11][12][13], local search [10], artificial neural networks [14], differential evolution algorithms [15], and other methods [16], demonstrating satisfactory results in terms of computational resources, execution time, and less error, compared to deterministic algorithms. It is worth mentioning that there are implementations where stochastic methods cannot be used, since their nature does not allow repetitiveness in the estimated data.…”

Neural Network-Based Model Reference Adaptive System for Torque Ripple Reduction in Sensorless Poly Phase Induction Motor Drive

“…The direct axis and quadrature axis rotor fluxes are given by the following Equations (23) and (24).…”

“…It is observed in both the simulation and hardware that the speed control of the induction motor is a challenging problem in the absence of a power electronics component [23,24]. In the proposed method, the induction motor with estimated parameters for sensor-less drive is analysed and compared with a conventional space vector modulation-based induction motor drive with a speed sensor.…”

Online and stable parameter estimation based on normalized brain emotional learning model (NBELM)

“…8 Although the RLS has an outstanding performance in time-varying problems, not only it does have stability problems but also its increasing computational complication is a burden on the system. 10 In the work of Thomsen et al, 11 the MRAS technique is also implemented on a doubly fed induction generator. 9 Another online approach is the model reference adaptive system (MRAS) technique.…”

“…Tang et al have made use of this method for estimating the parameters of a motor. 10 In the work of Thomsen et al, 11 the MRAS technique is also implemented on a doubly fed induction generator. In the paper, it is shown that the performance of the method is dependent on the test signal to excite all relevant eigenvalues of the system.…”