A comparative study on parameters estimation of squirrel cage induction motors using neural networks with unmemorized training

Çetin, Onursal; Dalcalı, Adem; Temurtaş, Feyzullah

doi:10.1016/j.jestch.2020.03.011

Cited by 26 publications

(18 citation statements)

References 23 publications

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“…Consequently, as shown in Table 4, the magnitude of the errors for the IM electrical parameters estimated with N-MRAS_e are lowest than those achieved with LSE_e. Table 4 shows that parameters estimate accuracy is within other works ranges [21][22][23][24]. A further improvement would be applying a PE voltage signal that compensates for the inverter's impact described in [27,28].…”

Section: Experimental Tests Performedmentioning

confidence: 74%

“…Based on Figure 10 results, the algorithm gives Table 2 results. It uses the following steady-state error expression as a standard practice reported in several IM estimation studies [21][22][23][24].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In recent years, researchers have analyzed some techniques for parameter estimation of IM, considering some particular cases and the application of different mathematical algorithms. A neural network-based method, trained with manufacturer data, estimates single and double-cage IM electrical parameters in [21] with up to 12% accuracy for a 22 kW IM. In [22], a deep-Q-learning algorithm estimates the IM rotor resistance and mutual inductance, comparing with three other algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…These two studies [22,23] show a similar accuracy for IM of 15 kW and 1.5 kW, respectively. The works [21][22][23] are offline algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Normalized-Model Reference System for Parameter Estimation of Induction Motors

et al. 2022

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This manuscript proposes a short tuning march algorithm to estimate induction motors (IM) electrical and mechanical parameters. It has two main novel proposals. First, it starts by presenting a normalized-model reference adaptive system (N-MRAS) that extends a recently proposed normalized model reference adaptive controller for parameter estimation of higher-order nonlinear systems, adding filtering. Second, it proposes persistent exciting (PE) rules for the input amplitude. This N-MRAS normalizes the information vector and identification adaptive law gains for a more straightforward tuning method, avoiding trial and error. Later, two N-MRAS designs consider estimating IM electrical and mechanical parameters. Finally, the proposed algorithm considers starting with a V/f speed control strategy, applying a persistently exciting voltage and frequency, and applying the two designed N-MRAS. Test bench experiments validate the efficacy of the proposed algorithm for a 10 HP IM.

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Section: Experimental Tests Performedmentioning

confidence: 74%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…These two studies [22,23] show a similar accuracy for IM of 15 kW and 1.5 kW, respectively. The works [21][22][23] are offline algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Normalized-Model Reference System for Parameter Estimation of Induction Motors

et al. 2022

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“…So far, numerous online identification methods of IM parameters have been proposed in the literature. These include model-based methods [6], recursive least-square algorithms (RLS) [7][8][9], model reference adaptive systems (MRAS) [10][11][12][13][14][15][16], signal injection (SI) techniques [17][18][19], state observers (SO) [20][21][22], and artificial intelligence (ANN) [23][24][25] methods. Typically, the greater the estimation accuracy and insensitivity to other machine parameters, the greater the algorithm complexity, which puts demands on hardware computational power and the experience of the implementation engineer.…”

Section: Introductionmentioning

confidence: 99%

Influence of Selected Non-Ideal Aspects on Active and Reactive Power MRAS for Stator and Rotor Resistance Estimation

2021

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Mathematical models of induction motor (IM) used in direct field-oriented control (DFOC) strategies are characterized by parametrization resulting from the IM equivalent circuit and model-type selection. The parameter inaccuracy causes DFOC detuning, which deteriorates the drive performance. Therefore, many methods for parameter adaptation were developed in the literature. One class of algorithms, popular due to their simplicity, includes estimators based on the model reference adaptive system (MRAS). Their main disadvantage is the dependence on other machines’ parameters. However, although typically not considered in the respective literature, there are other aspects that impair the performance of the MRAS estimators. These include, but are not limited to, the nonlinear phenomenon of iron losses, the effect of necessary discretization of the algorithms and selection of the sampling time, and the influence of the supply inverter nonlinear behavior. Therefore, this paper aims to study the effect of the above-mentioned negative aspects on the performance of selected MRAS estimators: active and reactive power MRAS for the stator and rotor resistance estimation. Furthermore, improved reduced-order models and MRAS estimators that consider the iron loss phenomenon are also presented to examine the iron loss influence. Another merit of this paper is that it shows clearly and in one place how DFOC, with the included effect of iron losses and inverter nonlinearities, can be modeled using simulation tools. The modeling of the IM and DFOC takes place in MATLAB/Simulink environment.

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