Intelligent Starting Current-Based Fault Identification of an Induction Motor Operating under Various Power Quality Issues

Sakthivel Ganesan, Prince Winston David, Praveen Kumar Balachandran and Devakirubakaran Samithas
Additional contact information
Sakthivel Ganesan: Department of Mechatronics Engineering, Kamaraj College of Engineering and Technology, Madurai 625701, India
Prince Winston David: Department of Electrical & Electronics Engineering, Kamaraj College of Engineering and Technology, Madurai 625701, India
Praveen Kumar Balachandran: Department of Electrical & Electronics Engineering, Bharat Institute of Engineering and Technology, Hyderabad 501510, India
Devakirubakaran Samithas: Department of Electrical & Electronics Engineering, Sethu Institute of Technology, Madurai 626115, India

Energies, 2021, vol. 14, issue 2, 1-13

Abstract: Since most of our industries use induction motors, it is essential to develop condition monitoring systems. Nowadays, industries have power quality issues such as sag, swell, harmonics, and transients. Thus, a condition monitoring system should have the ability to detect various faults, even in the presence of power quality issues. Most of the fault diagnosis and condition monitoring methods proposed earlier misidentified the faults and caused the condition monitoring system to fail because of misclassification due to power quality. The proposed method uses power quality data along with starting current data to identify the broken rotor bar and bearing fault in induction motors. The discrete wavelet transform (DWT) is used to decompose the current waveform, and then different features such as mean, standard deviation, entropy, and norm are calculated. The neural network (NN) classifier is used for classifying the faults and for analyzing the classification accuracy for various cases. The classification accuracy is 96.7% while considering power quality issues, whereas in a typical case, it is 93.3%. The proposed methodology is suitable for hardware implementation, which merges mean, standard deviation, entropy, and norm with the consideration of power quality issues, and the trained NN proves stable in the detection of the rotor and bearing faults.

Keywords: discrete wavelet transform (DWT); power quality issues; induction motor; motor faults (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/2/304/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/2/304/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:2:p:304-:d:476599

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().