Estimation of Electrical Parameters of the Double-Cage Model of Induction Motors Using Manufacturer Data and Genetic Algorithm

Perin, Matheus; Silveira, Gabriel Behling Da; Pereira, L.F.A.; Haffner, Sérgio; Almansa, Dhamens M. S.

doi:10.1109/iecon49645.2022.9968412

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…The most commonly utilized equivalent circuit model of the squirrelcage induction motor is the constant parameter equivalent circuit model with a single-cage rotor structure [11]. Contrary to popular belief, obtaining equivalent circuit parameters is complex, and it is even more challenging to find the typical amounts of the double-cage induction motor variables [42,43]. Since the single-cage equivalent circuit method does not adequately present induction motors, the double-cage equivalent circuit method should be utilized.…”

Section: Structure and Equivalent Circuit Models Of Squirrel-cage Ind...mentioning

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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Section: Structure and Equivalent Circuit Models Of Squirrel-cage Ind...mentioning

confidence: 99%

Feasibility of Six Metaheuristic Solutions for Estimating Induction Motor Reactance

Gör

2024

Mathematics

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“…parameters, which can be obtained from diverse methods [10], such as offline algorithms [11,12], offline tests [13,14], and self-commissioning tests [15,16].…”

Section: Of 20mentioning

confidence: 99%

Robust Combined Adaptive Passivity-Based Control for Induction Motors

Travieso-Torres,

Ricaldi-Morales,

Aguila-Camacho

2024

Preprint

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The need for industrial and commercial machinery to maintain high torque while accurately following a variable angular speed is increasing. To meet this demand, induction motors (IMs) are commonly used with variable speed drives (VSDs) that employ a field-oriented control (FOC) scheme. Over the last thirty years, IMs have been replacing independent connection direct current motors due to their cost-effectiveness, reduced maintenance needs, and increased efficiency. However, IMs and VSDs exhibit nonlinear behavior, uncertainties, and disturbances. This paper proposes a robust combined adaptive passivity-based control (CAPBC) for this class of nonlinear systems that applies to angular rotor speed and stator current regulation inside an FOC scheme for IMs´ VSDs. It uses general Lyapunov-based design energy functions and adaptive laws with σ-modification to assure robustness after combining control and monitoring variables. Lyapunov’s second method and Barbalat Lemma prove that the control and identification error tends to be zero over time. Moreover, comparative experimental results with standard Proportional Integer controller (PIC) and direct APBC, show the proposed CAPBC effectiveness and robustness under normal and changing conditions

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Estimation of the parameters for multi-cage models of induction motors using manufacturer data and PSO

Perin,

Pereira,

Silveira

et al. 2023

Electr Eng

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Estimation of Electrical Parameters of the Double-Cage Model of Induction Motors Using Manufacturer Data and Genetic Algorithm

Cited by 3 publications

References 7 publications

Feasibility of Six Metaheuristic Solutions for Estimating Induction Motor Reactance

Feasibility of Six Metaheuristic Solutions for Estimating Induction Motor Reactance

Robust Combined Adaptive Passivity-Based Control for Induction Motors

Estimation of the parameters for multi-cage models of induction motors using manufacturer data and PSO

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