Using the Instantaneous Power of a Free Acceleration Test for Squirrel-Cage Motor Parameters Estimation

Kojooyan-Jafari, Hengameh; Monjo, Lluís; Córcoles, F.; Pedra, J.

doi:10.1109/tec.2015.2399697

Cited by 23 publications

(6 citation statements)

References 31 publications

(33 reference statements)

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“…In [64], the authors proposed the usage of the instantaneous power of a free acceleration test for IM double-cage motor parameter estimation, in which, the authors compared the obtained results with the corresponding values of measured and manufacturer data presented in Table 14. Further, we have used the proposed algorithm, machine manufacturer data, and the following function.…”

Section: Simulation Results For Machinementioning

confidence: 99%

Parameter Estimation of Induction Machine Single-Cage and Double-Cage Models Using a Hybrid Simulated Annealing–Evaporation Rate Water Cycle Algorithm

et al. 2020

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This paper presents the usage of the hybrid simulated annealing—evaporation rate water cycle algorithm (SA-ERWCA) for induction machine equivalent circuit parameter estimation. The proposed algorithm is applied to nameplate data, measured data found in the literature, and data measured experimentally on a laboratory three-phase induction machine operating as an induction motor and as an induction generator. Furthermore, the proposed method is applied to both single-cage and double-cage equivalent circuit models. The accuracy and applicability of the proposed SA-ERWCA are intensively investigated, comparing the machine output characteristics determined by using SA-ERWCA parameters with corresponding characteristics obtained by using parameters determined using known methods from the literature. Also, the comparison of the SA-ERWCA with classic ERWCA and other algorithms used in the literature for induction machine parameter estimation is presented. The obtained results show that the proposed algorithm is a very effective and accurate method for induction machine parameter estimation. Furthermore, it is shown that the SA-ERWCA has the best convergence characteristics compared to other algorithms for induction machine parameter estimation in the literature.

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Section: Simulation Results For Machinementioning

confidence: 99%

Parameter Estimation of Induction Machine Single-Cage and Double-Cage Models Using a Hybrid Simulated Annealing–Evaporation Rate Water Cycle Algorithm

et al. 2020

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“…The most traditional method for IM starting is the DOL method [8,9]. However, the DOL method is much more beneficial in parameters estimation of IMs [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…The first perspective is that the IM starting process can benefit parameter determination either with a direct startup [8,9] or acceleration [12,13] or acceleration-deceleration tests [14]. Many research works were dedicated to estimating motor parameters under different conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Babau et al in [14] proposed a complete parameter identification procedure of large IMs from no-load acceleration-deceleration tests. Kojooyan et al in [12] have used the instantaneous power of a free acceleration test for squirrel-cage IM parameters estimation, andĆalasan et al in [16] proposed a hybrid simulated annealing evaporation-rate water cycle algorithm [17] for parameter estimation of single-cage and double-cage IMs models.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Exact Analytical Solution Based on Kloss Equation towards Accurate Speed-Time Characteristics Modeling of Induction Machines during No-Load Direct Startups

et al. 2021

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The acceleration time of induction machines (IMs) is essential for proper protection-relay settings of the machine to prevent voltage sags in local power areas. In this paper, mathematical modeling of IMs’ speed-time characteristics during no-load direct startup has been presented. Unlike the approaches presented in the literature, the proposed approach includes the bearing losses, in which two expressions of the speed-time characteristics of IMs during no-load direct startup are derived. The first expression was derived based on the Kloss equation used for representing the torque, and the second expression was derived based on the torque expression determined from the Thevenin equivalent circuit of the machine. The derived expressions’ accuracy was validated using laboratory measurement and computer simulation approaches. The results obtained show a good agreement between the measured and simulated speed-time characteristics of two IMs. Finally, the proposed formulations can provide a simple analytical base to enable accurate IM modeling.

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“…Regarding the squirrel-cage model of induction motors presented in Ref. [23], new parameters are determined by estimating the mechanical speed and instantaneous electrical power using a free acceleration test. Authors have recently proposed a two-step method to detect the characteristics of an induction motor using a nonlinear method that incorporates magnetic satiation [24].…”

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confidence: 99%

Feasibility of Six Metaheuristic Solutions for Estimating Induction Motor Reactance

Gör

2024

Mathematics

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Industry is the primary application for induction machines. As such, it is essential to calculate the induction devices’ electrical properties accurately. With DC testing, no-load rotor tests, and locked rotor tests, one may empirically evaluate the electrical variables of induction motors. These tests are expensive and difficult to conduct, however. The information supplied by machine makers can also be used to accurately approximate the equivalent variables of the circuits in induction machines. This article has successfully predicted motor reactance (Xm) for both double- and single-cage models using artificial neural networks (ANN). Although ANNs have been investigated in the literature, the ANN structures were trained to use unmemorized training. Besides ANN, six other approaches have been suggested to address this issue: heap-based optimization (HBO), leagues championship algorithm (LCA), multi-verse optimization (MVO), osprey optimization algorithm (OOA), cuckoo optimization algorithm (COA), and sooty tern optimization algorithm (STOA). The efficaciousness of the suggested approaches was compared with each another. Regarding the obtained outcomes, the suggested MVO- multi-layer perceptron (MLP) technique performed better than the other five methods regarding reactance prediction, with R2 of 0.99598 and 0.9962, and RMSE of 20.31492 and 20.80626 in the testing and training phases, respectively. For the projected model, the suggested ANNs have produced great results. The novelty lies in the mentioned methods’ ability to tackle the complexities and challenges associated with induction motor reactance optimization, providing innovative approaches to finding optimal or near-optimal solutions. As researchers continue to explore and refine these techniques, their impact on motor design and efficiency will likely grow, driving advancements in electrical engineering.

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Using the Instantaneous Power of a Free Acceleration Test for Squirrel-Cage Motor Parameters Estimation

Cited by 23 publications

References 31 publications

Parameter Estimation of Induction Machine Single-Cage and Double-Cage Models Using a Hybrid Simulated Annealing–Evaporation Rate Water Cycle Algorithm

Parameter Estimation of Induction Machine Single-Cage and Double-Cage Models Using a Hybrid Simulated Annealing–Evaporation Rate Water Cycle Algorithm

A Novel Exact Analytical Solution Based on Kloss Equation towards Accurate Speed-Time Characteristics Modeling of Induction Machines during No-Load Direct Startups

Feasibility of Six Metaheuristic Solutions for Estimating Induction Motor Reactance

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