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Efficient Fault Detection for Open Circuit Faults in HANPC Inverters Using Artificial Neural Network for Motor Drive Applications
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Publication Year
2023-01-01
Journal
2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023
Keyword
Fault detectionHNPCOpen circuitPMSM
Mesh Keyword
Active neutral point clampedCommon faultsFaults detectionHNPCMotor drive applicationsNeutral-point clamped invertersOpen-circuit faultOpen-circuitsPMSMSwitching devices
All Science Classification Codes (ASJC)
Electrical and Electronic EngineeringMechanical EngineeringComputational Mechanics
Abstract
Open circuit faults are regarded as common faults that affect switching devices of the power converters. The early detection of these faults is a vital task in order to protect the other switching devices and the associated appliances such as motor drive systems. However, the conventional methods require different hardware devices to detect the variation in the current and voltage waveforms. Therefore, in this paper a creative method that use artificial intelligence by performing artificial neural network (ANN) to detect and identify the faulty devices effectively and precisely without the need to adapt additional hardware or modifying the original configuration of the inverter topology. The proposed ANN is trained using offline data and then tested and verified by applying it to hybrid active neutral point clamped (HANPC) topology. This topology is used to drive interior permanent magnet synchronous motor (IPMSM). The proposed method is verified by simulation results.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36983
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85174604897&origin=inward
DOI
https://doi.org/10.1109/sled57582.2023.10261371
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10261339
Type
Conference
Funding
work was supported by the Korea Institute ofEnergy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20206910100160 and No. 20225500000110).
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Lee, Kyo-Beum이교범
Department of Electrical and Computer Engineering
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