Fault detection for modular multilevel converters based on sliding mode observer

This letter presents a fault detection method for modular multilevel converters (MMC) which is capable of lo¬cating a faulty semiconductor switching device in the circuit. The proposed fault detection method is based on a sliding mode observer (SMO) and a switching model of a half-bridge, the approa...

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Main Authors: Shao, Shuai, Wheeler, Patrick, Clare, Jon C., Watson, Alan James
Format: Article
Published: IEEE 2013
Subjects:
Online Access:https://eprints.nottingham.ac.uk/43946/
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author Shao, Shuai
Wheeler, Patrick
Clare, Jon C.
Watson, Alan James
author_facet Shao, Shuai
Wheeler, Patrick
Clare, Jon C.
Watson, Alan James
author_sort Shao, Shuai
building Nottingham Research Data Repository
collection Online Access
description This letter presents a fault detection method for modular multilevel converters (MMC) which is capable of lo¬cating a faulty semiconductor switching device in the circuit. The proposed fault detection method is based on a sliding mode observer (SMO) and a switching model of a half-bridge, the approach taken is to conjecture the location of fault, modify the SMO accordingly and then compare the observed and measured states to verify, or otherwise, the assumption. This technique requires no additional measurement elements and can easily be implemented in a DSP or micro-controller. The operation and robustness of the fault detection technique are confirmed by simulation results for the fault condition of a semiconductor switching device appearing as an open-circuit.
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institution University of Nottingham Malaysia Campus
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spelling nottingham-439462020-05-04T16:39:41Z https://eprints.nottingham.ac.uk/43946/ Fault detection for modular multilevel converters based on sliding mode observer Shao, Shuai Wheeler, Patrick Clare, Jon C. Watson, Alan James This letter presents a fault detection method for modular multilevel converters (MMC) which is capable of lo¬cating a faulty semiconductor switching device in the circuit. The proposed fault detection method is based on a sliding mode observer (SMO) and a switching model of a half-bridge, the approach taken is to conjecture the location of fault, modify the SMO accordingly and then compare the observed and measured states to verify, or otherwise, the assumption. This technique requires no additional measurement elements and can easily be implemented in a DSP or micro-controller. The operation and robustness of the fault detection technique are confirmed by simulation results for the fault condition of a semiconductor switching device appearing as an open-circuit. IEEE 2013-11-30 Article PeerReviewed Shao, Shuai, Wheeler, Patrick, Clare, Jon C. and Watson, Alan James (2013) Fault detection for modular multilevel converters based on sliding mode observer. IEEE Transactions on Power Electronics, 28 (11). pp. 4867-4872. ISSN 0885-8993 Fault detection modular multilevel converter sliding mode observer switching model http://ieeexplore.ieee.org/document/6418042/ doi:10.1109/TPEL.2013.2242093 doi:10.1109/TPEL.2013.2242093
spellingShingle Fault detection
modular multilevel converter
sliding mode observer
switching model
Shao, Shuai
Wheeler, Patrick
Clare, Jon C.
Watson, Alan James
Fault detection for modular multilevel converters based on sliding mode observer
title Fault detection for modular multilevel converters based on sliding mode observer
title_full Fault detection for modular multilevel converters based on sliding mode observer
title_fullStr Fault detection for modular multilevel converters based on sliding mode observer
title_full_unstemmed Fault detection for modular multilevel converters based on sliding mode observer
title_short Fault detection for modular multilevel converters based on sliding mode observer
title_sort fault detection for modular multilevel converters based on sliding mode observer
topic Fault detection
modular multilevel converter
sliding mode observer
switching model
url https://eprints.nottingham.ac.uk/43946/
https://eprints.nottingham.ac.uk/43946/
https://eprints.nottingham.ac.uk/43946/