Soft Computing

, Volume 23, Issue 21, pp 11217–11226 | Cite as

Differential evolution applied to line-connected induction motors stator fault identification

  • Jacqueline Jordan GuedesEmail author
  • Marcelo Favoretto Castoldi
  • Alessandro Goedtel
  • Cristiano Marcos Agulhari
  • Danilo Sipoli Sanches
Methodologies and Application


The three-phase induction motor is the main machine used for electromechanical energy conversion, due to its consolidated construction characteristics. As a consequence of its great importance and industrial application, researches in the fault identification area are constantly conducted to reduce the maintenance rate and the losses, during the productive process, caused by undesirable downtime. In this sense, this work proposes an alternative methodology, based on the differential evolution algorithm, to identify stator short-circuit fault in induction motors connected directly to the electrical grid, using voltage and current signals in time domain. The differential evolution algorithm is used to estimate the electrical parameters of the induction motor, based on the model of the equivalent electrical circuit. Stator fault is identified by calculating the variation of the estimated magnetizing inductance of the motor under no fault condition. The proposed method is validated through experimental tests on 1 HP and 2 HP motors under conditions of load torque variation and unbalanced voltages.


Three-phase induction motor Stator fault Differential evolution Parameter estimation 



Author Alessandro Goedtel has received research Grants from National Council for Scientific and Technological Development—CNPq (Processes 474290/2008-5, 473576/2011-2, 552269/2011-5, 307220/2016-8) and Araucária Foundation of Support to the Scientific and Technological Development of Paraná (Process 06/56093-3).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Jacqueline Jordan Guedes
    • 1
    Email author
  • Marcelo Favoretto Castoldi
    • 1
  • Alessandro Goedtel
    • 1
  • Cristiano Marcos Agulhari
    • 1
  • Danilo Sipoli Sanches
    • 1
  1. 1.Federal University of Technology of ParanaCornelio ProcopioBrazil

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