Abstract
It is documented that almost 98% of all voltage generated by electric utilities has up to 3% unbalance. Single phasing fault deserves special attention since phase loss is considered the worst case of unbalanced supply voltage. This paper focuses on unbalanced supply condition diagnosis and discrimination between an unbalance in the supply and phase loss fault. The discrimination will be based on the ratio of third harmonic to fundamental Fast Fourier Transform (FFT) magnitude components (RTHF-FFT) of the three-phase stator line currents and supply voltages under different load conditions and using artificial neural network (ANN). The proposed approach achieves high accuracy in detecting the unbalanced supply voltage condition in induction motor and identifying the level of severity of the fault. In addition, the proposed algorithm will discriminate between the effects of unbalanced supply voltage and those due to phase losses fault. The paper proposed a reliable approach for detection and diagnosis of unbalanced supply voltage condition. Possible loss of winding insulation under different percentages of unbalanced supply voltages will be predicted which could help preventing sudden failure of the motor during operation. The approach will be proved through experimental validation.
| Original language | English |
|---|---|
| Title of host publication | 7th IET International Conference on Power Electronics, Machines and Drives, PEMD 2014 |
| Publisher | Institution of Engineering and Technology |
| ISBN (Print) | 9781849198158 |
| Publication status | Published - 2014 |
| Event | 7th IET International Conference on Power Electronics, Machines and Drives, PEMD 2014 - Manchester, United Kingdom Duration: 8 Apr 2014 → 10 Apr 2014 |
Other
| Other | 7th IET International Conference on Power Electronics, Machines and Drives, PEMD 2014 |
|---|---|
| Country | United Kingdom |
| City | Manchester |
| Period | 8/4/14 → 10/4/14 |
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Keywords
- ANN
- Fault detection
- Induction motor
- Phase losses fault
- Unbalanced supply voltage
ASJC Scopus subject areas
- Electrical and Electronic Engineering
Cite this
ANN-based system for a discrimination between unbalanced supply voltage and phase lossin induction motors. / Khalil, Shady; Abu-Rub, Haitham; Iqbal, Atif.
7th IET International Conference on Power Electronics, Machines and Drives, PEMD 2014. Institution of Engineering and Technology, 2014.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - ANN-based system for a discrimination between unbalanced supply voltage and phase lossin induction motors
AU - Khalil, Shady
AU - Abu-Rub, Haitham
AU - Iqbal, Atif
PY - 2014
Y1 - 2014
N2 - It is documented that almost 98% of all voltage generated by electric utilities has up to 3% unbalance. Single phasing fault deserves special attention since phase loss is considered the worst case of unbalanced supply voltage. This paper focuses on unbalanced supply condition diagnosis and discrimination between an unbalance in the supply and phase loss fault. The discrimination will be based on the ratio of third harmonic to fundamental Fast Fourier Transform (FFT) magnitude components (RTHF-FFT) of the three-phase stator line currents and supply voltages under different load conditions and using artificial neural network (ANN). The proposed approach achieves high accuracy in detecting the unbalanced supply voltage condition in induction motor and identifying the level of severity of the fault. In addition, the proposed algorithm will discriminate between the effects of unbalanced supply voltage and those due to phase losses fault. The paper proposed a reliable approach for detection and diagnosis of unbalanced supply voltage condition. Possible loss of winding insulation under different percentages of unbalanced supply voltages will be predicted which could help preventing sudden failure of the motor during operation. The approach will be proved through experimental validation.
AB - It is documented that almost 98% of all voltage generated by electric utilities has up to 3% unbalance. Single phasing fault deserves special attention since phase loss is considered the worst case of unbalanced supply voltage. This paper focuses on unbalanced supply condition diagnosis and discrimination between an unbalance in the supply and phase loss fault. The discrimination will be based on the ratio of third harmonic to fundamental Fast Fourier Transform (FFT) magnitude components (RTHF-FFT) of the three-phase stator line currents and supply voltages under different load conditions and using artificial neural network (ANN). The proposed approach achieves high accuracy in detecting the unbalanced supply voltage condition in induction motor and identifying the level of severity of the fault. In addition, the proposed algorithm will discriminate between the effects of unbalanced supply voltage and those due to phase losses fault. The paper proposed a reliable approach for detection and diagnosis of unbalanced supply voltage condition. Possible loss of winding insulation under different percentages of unbalanced supply voltages will be predicted which could help preventing sudden failure of the motor during operation. The approach will be proved through experimental validation.
KW - ANN
KW - Fault detection
KW - Induction motor
KW - Phase losses fault
KW - Unbalanced supply voltage
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UR - http://www.scopus.com/inward/citedby.url?scp=84907410894&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84907410894
SN - 9781849198158
BT - 7th IET International Conference on Power Electronics, Machines and Drives, PEMD 2014
PB - Institution of Engineering and Technology
ER -