Parameters estimation of three-phase induction motors using differential evolution

Guedes, Jacqueline Jordan; Castoldi, Marcelo Favoretto; Goedtel, Alessandro; Agulhari, Cristiano Marcos; Sanches, Danilo Sipoli

doi:10.1016/j.epsr.2017.08.033

Cited by 61 publications

(33 citation statements)

References 23 publications

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“…There are quite a few papers which deal with the induction machine starting process description, problems, usage, and realizations. [3][4][5][6][7][8][9][10][11][12][13][14][15]21,22 However, the speed calculation (ie, speed-time curve) during induction machine starting process is not analyzed in such papers. The speed-time curve during the induction machine start-up is analyzed in Aree.…”

Section: Introductionmentioning

confidence: 99%

Analytical solution for no‐load induction machine speed calculation during direct start‐up

Ćalasan

2018

Int Trans Electr Energ Syst

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Summary This paper deals with the investigation into mathematical modeling of induction machine speed‐time and time‐speed dependencies during no‐load direct start‐up. Namely, a precise analytical formula for induction machine speed calculation during no‐load direct start‐up is given and solved in the form of the Lambert W function. The results obtained by using developed analytical solution are compared with corresponding results obtained by using methods based on numerical time‐domain computation (NTDC) that have been realized in two different program packages. Furthermore, the results obtained by using developed analytical solution are compared with corresponding experimental results. The findings showed good agreement between the obtained results. All developed MATLAB programs for speed‐time and time‐speed curve determination are given in the paper. The Mathematica program developed for solving the Lambert W equation is also provided.

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Section: Introductionmentioning

confidence: 99%

Analytical solution for no‐load induction machine speed calculation during direct start‐up

Ćalasan

2018

Int Trans Electr Energ Syst

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“…The differential evolution algorithm requires the choice of the weighting factor, F, and crossover rate, Cr, which have not been standardized for this specific problem; therefore, the results may vary in other cases. Although an exploratory analysis was done to identify the factors, the results were not conclusive, so those referenced in [22], which are F = 0.8 and Cr = 0.5, were assumed. Finally, Figure 12 shows the torque and current curves with the best parameters selected with SSVDC for equal weights of the objective functions, and for equal weights in the operating zones, compared with the initial parameters (IPs).…”

Section: Results Of Ssvdc Techniquementioning

confidence: 99%

“…Some implementations of DE algorithms [22][23][24] have shown satisfactory results and reach the error assigned in a given number of iterations, taking into account the non-negative constraint for each of the parameters. In addition to this, it is concluded that they have good results at the rated operating points of the IM, but the result at the other operating points is unknown.…”

Section: Stochastic Search Technique With Variables Deterministic Conmentioning

confidence: 96%

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

2020

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This paper deals with the implementation of a new technique of stochastic search to find the best set of parameters in a mathematical model, applied to the single cage (SC) model of the induction motor (IM). The technique includes a new strategy to generate variable constraints of the domain, seven error functions, weight for the operating zones of the IM, and multi-objective functions. The results are validated with experimental data of the torque and current in an IM, and show better fitting to the experimental curves compared with the results of two different techniques, one deterministic and the other one stochastic. The results obtained allow us to conclude that the best set of parameters for the model depends on the weights assigned to the objective functions and to the operating zones.

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“…Thus, this paper proposes another alternative optimization approach for selecting the optimized mode number and the balancing parameter values for the VMD method by using a differential evolution algorithm (DEA), or VMDEA. DEA is a simple, effective design and coding optimization method that has been used in many fields of study, such as satellite images, facial recognition, and induction motors . In addition, DEA has been proved more effective compared with GA and PSO in solving most optimization problems because of its simple and compact construction, rapid convergence, and fewer control parameters .…”

Section: Introductionmentioning

confidence: 99%

Intelligent wind turbine gearbox diagnosis using VMDEA and ELM

et al. 2019

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Wind turbine gearbox diagnosis is a vital tool for maintaining wind turbine operation and safety. The gearbox vibration signal is invariably complex and variable, and useful information and features are difficulty of extraction. Recently, a new and adaptive signal decomposition method, known as variational mode decomposition (VMD), has been proposed, which helps to improve the efficiency and effectiveness of extracting features from gearbox vibration signals. However, the performance of the VMD method mainly depends on its input parameters, especially the mode number and balancing parameter (also called the quadratic penalty term). Hence, this paper proposes a selection method for an optimized VMD parameter using differential evolution algorithm (DEA), also called VMDEA. Firstly, the VMDEA is used to select optimized VMD input parameters for each of the vibration signals. Following this, VMD decomposes each vibration signal into sets of subsignals using the selected optimized parameter. Multidomain features are extracted from VMD reconstructed signals and are passed on to the extreme learning machine (ELM) for fault classification. This study can thus provide a good solution for determining an optimized VMD parameter for decomposing vibration signals and can also provide a more efficient and effective diagnostic approach to wind turbine gearbox maintenance.

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Parameters estimation of three-phase induction motors using differential evolution

Cited by 61 publications

References 23 publications

Analytical solution for no‐load induction machine speed calculation during direct start‐up

Analytical solution for no‐load induction machine speed calculation during direct start‐up

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

Intelligent wind turbine gearbox diagnosis using VMDEA and ELM

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