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dc.contributor.authorHossain, M.
dc.contributor.authorAbu-Siada, Ahmed
dc.contributor.authorMuyeen, S.M.
dc.date.accessioned2019-02-19T04:16:43Z
dc.date.available2019-02-19T04:16:43Z
dc.date.created2019-02-19T03:58:20Z
dc.date.issued2018
dc.identifier.citationHossain, M. and Abu-Siada, A. and Muyeen, S. 2018. A Hybrid Multilevel Power Electronic Inverter and Fault Location Identification of Switching Devices.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/74388
dc.identifier.doi10.1109/CMD.2018.8535618
dc.description.abstract

© 2018 IEEE. Multilevel power inverter has brought tremendous revolution in high-power medium voltage industrial drive applications due to their superior performance compared to conventional inverters. This paper proposes a hybrid multilevel power electronic inverter (HMPEI) which comprises only one DC source and less number of clamping diodes. Simulation analyses are conducted using MATLAB software to investigate the robustness of the inverter. On the other hand, number of faults are frequently occurring in the inverter leading to adverse impacts on the reliability of the inverter and increasing the system maintenance and operational cost. Condition monitoring plays a vital role to ensure the reliability of the power electronic inverters through detecting incipient faults and rectify them before progression. There are a number of condition monitoring and fault diagnosis techniques to ensure the reliability of the inverter. Electrical signal based condition monitoring is one of the dominating techniques that is widely used to monitor the condition of power electronic inverters because it is easy to implement, cost effective, non-intrusive, able to detect early stage faults, fault location and mode. In this paper, electrical signals are employed to monitor and identify the fault locations in the proposed HMPEI.

dc.titleA Hybrid Multilevel Power Electronic Inverter and Fault Location Identification of Switching Devices
dc.typeConference Paper
dcterms.source.title2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings
dcterms.source.series2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings
dcterms.source.isbn9781538641262
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available


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