Skip to main content
SpringerLink
Log in

Fuzzy logic-based induction motor protection system

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The protection is very important to detect abnormal motor running conditions such as over current, over voltage, overload, over temperature, and so on. When a failure is sensed by the protection system, a time delay should be specified to trip the motor. In the classical systems, motors are stopped with the time delay, which is adjusted constantly without considering the fault level. This paper presents a fuzzy logic-based protection system covering six different fault parameters for induction motors. This paper focuses on a new time-delay calculation for stopping induction motor and improves the overall detection performance. The time delay is computed by fuzzy logic method according to various fault parameters when one of the failures occurs on the motor. This system is successfully tested in real-time faults on the motor, and it shows that it provides sensitive protection by fuzzy rules.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. http://library.abb.com/global/scot/scot209.nsf/veritydisplay/ad423934ae0700f2c1256f48003a6fe1/File/2CDC131013B0201.pdf. Accessed 30 July 2011

  2. Arslan M, Çunkaş M, Sağ T (2011) Determination of induction motor parameters with differential evolution algorithm. Neural Comput Appl. doi:10.1007/s00521-011-0612-8

  3. Siddique A, Yadava GD, Singh B (2005) A review of stator fault monitoring techniques of induction motors. IEEE Trans Energy Convers 20(1):106–114

    Article  Google Scholar 

  4. Bellini A, Fillippetti F, Tassoni C, Capolino G (2008) Advances in diagnostic techniques for induction machines. IEEE Trans Energy Convers 55(12):4109–4126

    Google Scholar 

  5. Özgönenel O, Yalçın T (2011) A complete motor protection algorithm based on PCA and ANN: a real time study. Turk J Elect Eng Comput Sci 19(3):317–334

    Google Scholar 

  6. Zarei J, Poshtan J (2007) Bearing fault detection using wavelet packet transform of induction motor stator current. Tribol Int 40(5):763–769

    Article  Google Scholar 

  7. Benbouzid M, Vieira M, Theys C (1999) Induction motors’ faults detection and localization using stator current advanced signal processing techniques. IEEE Trans Power Electron 14(1):14–22

    Article  Google Scholar 

  8. Blödt M, Chabert M, Regnier J, Faucher J (2006) Mechanical load fault detection in induction motors by stator current time–frequency analysis. IEEE Trans Ind Appl 42(6):1454–1463

    Article  Google Scholar 

  9. Ayhan B, Chow M, Song M (2005) Multiple signature processing-based fault detection schemes for broken rotor bar in induction motors. IEEE Trans Energy Convers 20(2):336–343

    Article  Google Scholar 

  10. Rajagopalan S, Restrepo J, Aller J, Habetler T, Harley R (2008) Nonstationary motor fault detection using recent quadratic time–frequency representations. IEEE Trans Ind Appl 44(3):735–744

    Article  Google Scholar 

  11. Kowalski CT, Orlowska-Kowalska T (2003) Neural networks application for induction motor faults diagnosis. Math Comput Simul 63(3–5):435–448

    Article  MathSciNet  MATH  Google Scholar 

  12. Su H, Chong KT, Kumar RR (2011) Vibration signal analysis for electrical fault detection of induction machine using neural networks. Neural Comput Appl 20(2):183–194

    Article  Google Scholar 

  13. Kim K, Parlos AG (2002) Model-based fault diagnosis of induction motors using non-stationary signal segmentation. Mech Syst Signal Process 16(2–3):223–253

    Article  Google Scholar 

  14. Martins JF, Pires VF, Pires AJ (2007) Unsupervised neural-network-based algorithm for an on-line diagnosis of three-phase induction motor stator fault. IEEE Trans Ind Electron 54(1):259–264

    Article  Google Scholar 

  15. Gao XZ, Ovaska SJ (2001) Soft computing methods in motor fault diagnosis. Appl Soft Comput 1(1):73–81

    Article  Google Scholar 

  16. Arabacı H, Bilgin O (2010) Automatic detection and classification of rotor cage faults in squirrel cage induction motor. Neural Comput Appl 19:713–723

    Article  Google Scholar 

  17. Çolak İ, Çelik H, Sefa İ, Demirbaş Ş (2005) On line protection systems for induction motors. Energy Convers Manag 46(17):2773–2786

    Article  Google Scholar 

  18. Çolak İ, Bayindir R, Bektaş A, Sefa İ, Bal G (2007) Protection of induction motor using PLC. International Conference on Power Engineering, Energy and Electrical Drives (POWERENG), pp 96–99, Portugal

  19. Bayindir R, Sefa İ (2007) Novel approach based on microcontroller to online protection of induction motors. Energy Convers Manag 48(3):850–856

    Article  Google Scholar 

  20. Bayindir R, Sefa İ, Çolak İ, Bektas A (2008) Fault detection and protection of induction motors using sensors. IEEE Trans Energy Convers 23(3):734–741

    Article  Google Scholar 

  21. Çunkaş M, Akkaya R, Öztürk A (2000) Protection of AC motors by means of microcontrollers. 10th Mediterranean Electrotechnical Conference (MELECON), vol 3, pp 1093–1096

  22. Uyar O (2010) Design and implementation of fuzzy logic based protection system for induction motors. MS thesis, Selcuk University, Turkey

  23. Pillay P, Manyage M (2001) Definitions of voltage unbalance. IEEE Power Eng Rev 21(5):10–12

    Article  Google Scholar 

  24. Ross TJ (2004) Fuzzy logic with engineering applications. Wiley, London

    MATH  Google Scholar 

  25. Uyar O, Çunkaş M (2011) Design of fuzzy logic based motor protection system. 6th International Advanced Technologies Symposium (IATS’11), 16–18 May 2011, Turkey

  26. Zhang P, Du Y, Habetler TG, Lu B (2011) A survey of condition monitoring and protection methods for medium-voltage induction motors. IEEE Trans Ind Appl 47(1):34–46

    Article  Google Scholar 

  27. Sin ML, Erugrul N (2003) Induction machine on-line condition monitoring and fault diagnosis—a survey. AUPEC2003, Australasian Universities Power Engineering Conference, Christchurch, New Zealand

Download references

Acknowledgment

This work was supported by Scientific Research Project Coordination of Selcuk University under Project with 09101047 project number.

Author information

Authors and Affiliations

  1. Techical Education Faculty, Electronic and Computer Education, Selçuk University, 42003, Konya, Turkey

    Okan Uyar

  2. Technology Faculty, Electric and Electronic Engineering, Selçuk University, 42003, Konya, Turkey

    Mehmet Çunkaş

Authors
  1. Okan Uyar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mehmet Çunkaş
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehmet Çunkaş.

Appendices

Appendix 1: Abbreviations

 

Abbr.

Description

Abbr.

Description

OC

Over current

VS

Very short

OV

Over voltage

S

Short

T

Temperature

N

Normal

CU

Current unbalance

LN

Long

VU

Voltage unbalance

VL

Very long

LV

Under voltage

OCL

Over current low

L

Low

OVM

Over voltage medium

M

Medium

TH

Temperature high

H

High

  

Appendix 2

Appendix 3

Rights and permissions

Reprints and permissions

About this article

Cite this article

Uyar, O., Çunkaş, M. Fuzzy logic-based induction motor protection system. Neural Comput & Applic 23, 31–40 (2013). https://doi.org/10.1007/s00521-012-0862-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-012-0862-0

Keywords

Search

Navigation