Abstract
An inter-turn fault is one of the most difficult failures to detect Depending on the motor protection, the motor may continue to run but, soon or later, the heating in the short-circuited turns will cause severe failures. In this paper, the stator inter-turn faults are detected by observing the Concordia patterns and the behavior of the effective current values (RMS). These fault indicators have a specific behavior corresponding to each fault and can be used detect, locate and evaluate faults. Also two strategies are proposed to use multilayer feedforward neural networks (FFNN) in the fault detection, and both strategies are sketched and the best configuration is used. The network output can be interpreted, using a table, as a diagnosis report of the machine health. In addition, the use of two neural networks at the same time is proposed to improve the prediction accuracy.
| Original language | English |
|---|---|
| Title of host publication | 5th International Conference-Workshop Compatibility in Power Electronics, CPE 2007 |
| DOIs | |
| Publication status | Published - 2007 |
| Event | 5th International Conference-Workshop Compatibility in Power Electronics, CPE 2007 - Gdansk, Poland Duration: May 29 2007 → Jun 1 2007 |
Other
| Other | 5th International Conference-Workshop Compatibility in Power Electronics, CPE 2007 |
|---|---|
| Country | Poland |
| City | Gdansk |
| Period | 5/29/07 → 6/1/07 |
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Keywords
- Fault detection
- Inter-turns
- Neural networks
- Park's current vector
- RMS behavior
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering
Cite this
Stator inter-turn faults detection and localization using stator currents and concordia patterns - Neural network applications. / El-Arabawy, Ibrahim F.; Masoud, Mahmoud I.; El-Kader Mokhtari, Abd.
5th International Conference-Workshop Compatibility in Power Electronics, CPE 2007. 2007. 4296552.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Stator inter-turn faults detection and localization using stator currents and concordia patterns - Neural network applications
AU - El-Arabawy, Ibrahim F.
AU - Masoud, Mahmoud I.
AU - El-Kader Mokhtari, Abd
PY - 2007
Y1 - 2007
N2 - An inter-turn fault is one of the most difficult failures to detect Depending on the motor protection, the motor may continue to run but, soon or later, the heating in the short-circuited turns will cause severe failures. In this paper, the stator inter-turn faults are detected by observing the Concordia patterns and the behavior of the effective current values (RMS). These fault indicators have a specific behavior corresponding to each fault and can be used detect, locate and evaluate faults. Also two strategies are proposed to use multilayer feedforward neural networks (FFNN) in the fault detection, and both strategies are sketched and the best configuration is used. The network output can be interpreted, using a table, as a diagnosis report of the machine health. In addition, the use of two neural networks at the same time is proposed to improve the prediction accuracy.
AB - An inter-turn fault is one of the most difficult failures to detect Depending on the motor protection, the motor may continue to run but, soon or later, the heating in the short-circuited turns will cause severe failures. In this paper, the stator inter-turn faults are detected by observing the Concordia patterns and the behavior of the effective current values (RMS). These fault indicators have a specific behavior corresponding to each fault and can be used detect, locate and evaluate faults. Also two strategies are proposed to use multilayer feedforward neural networks (FFNN) in the fault detection, and both strategies are sketched and the best configuration is used. The network output can be interpreted, using a table, as a diagnosis report of the machine health. In addition, the use of two neural networks at the same time is proposed to improve the prediction accuracy.
KW - Fault detection
KW - Inter-turns
KW - Neural networks
KW - Park's current vector
KW - RMS behavior
UR - http://www.scopus.com/inward/record.url?scp=47149100968&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=47149100968&partnerID=8YFLogxK
U2 - 10.1109/CPE.2007.4296552
DO - 10.1109/CPE.2007.4296552
M3 - Conference contribution
AN - SCOPUS:47149100968
SN - 142441055X
SN - 9781424410552
BT - 5th International Conference-Workshop Compatibility in Power Electronics, CPE 2007
ER -