TY - GEN
T1 - Multiswitch Fault-detection for VSI fed Multiphase Motor Drive Based on Machine Learning
AU - Chikondra, Bheemaiah
AU - Gonuguntla, Venkateswarlu
AU - Al Zaabi, Omar
AU - Behera, Ranjan Kumar
AU - Veluvolu, Kalyana C.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Detecting early faults in an electric drive system in order to maintain reliability and uninterrupted post-fault operation is an extremely difficult task. In recent years, early fault detection has become one of the most important areas of cutting-edge research in the fields of electric vehicles, offshore-ship propulsion, air taxis, electric vertical take-off and landing, etc. In this paper, a generalized fault detection method for the five-phase induction motor drive is presented based on an optimized support vector machine (SVM) learning algorithm. Using the second low-frequency processing method, low-frequency signals were extracted from fault currents and employed for SVM training. This expedites fault detection and reduces memory allocation. The proposed fault detection algorithm has been validated through simulation in steady-state and dynamic loading conditions of the drive for a variety of fault scenarios.
AB - Detecting early faults in an electric drive system in order to maintain reliability and uninterrupted post-fault operation is an extremely difficult task. In recent years, early fault detection has become one of the most important areas of cutting-edge research in the fields of electric vehicles, offshore-ship propulsion, air taxis, electric vertical take-off and landing, etc. In this paper, a generalized fault detection method for the five-phase induction motor drive is presented based on an optimized support vector machine (SVM) learning algorithm. Using the second low-frequency processing method, low-frequency signals were extracted from fault currents and employed for SVM training. This expedites fault detection and reduces memory allocation. The proposed fault detection algorithm has been validated through simulation in steady-state and dynamic loading conditions of the drive for a variety of fault scenarios.
KW - Artificial intelligence
KW - fault-tolerance
KW - multiphase machines
KW - support vector machine
KW - voltage source inverter
UR - https://www.scopus.com/pages/publications/85152386624
U2 - 10.1109/PEDES56012.2022.10080697
DO - 10.1109/PEDES56012.2022.10080697
M3 - Conference contribution
AN - SCOPUS:85152386624
T3 - 10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022
BT - 10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2022
Y2 - 14 December 2022 through 17 December 2022
ER -