Online Fault Identification Based on an Adaptive Observer for Modular Multilevel Converters Applied to Wind Power Generation Systems

Due to the possibility of putting a large number of modules consisting of switches and capacitors connected in series, the modular multilevel converter (MMC) can easily be scaled to high power and high voltage power conversion, which is an attractive feature for filter-less and transformer-less desi...

Full description

Bibliographic Details
Main Authors: Hui Liu, Ke Ma, Poh Chiang Loh, Frede Blaabjerg
Format: Article
Language:English
Published: MDPI AG 2015-07-01
Series:Energies
Subjects:
Online Access:http://www.mdpi.com/1996-1073/8/7/7140
id doaj-7716ae5dc1c24f479f6cdb9ca7374982
record_format Article
spelling doaj-7716ae5dc1c24f479f6cdb9ca73749822020-11-24T22:17:44ZengMDPI AGEnergies1996-10732015-07-01877140716010.3390/en8077140en8077140Online Fault Identification Based on an Adaptive Observer for Modular Multilevel Converters Applied to Wind Power Generation SystemsHui Liu0Ke Ma1Poh Chiang Loh2Frede Blaabjerg3Department of Energy Technology, Aalborg University, Aalborg 9220, DenmarkDepartment of Energy Technology, Aalborg University, Aalborg 9220, DenmarkDepartment of Energy Technology, Aalborg University, Aalborg 9220, DenmarkDepartment of Energy Technology, Aalborg University, Aalborg 9220, DenmarkDue to the possibility of putting a large number of modules consisting of switches and capacitors connected in series, the modular multilevel converter (MMC) can easily be scaled to high power and high voltage power conversion, which is an attractive feature for filter-less and transformer-less design and helpful to achieve high efficiency. However, a significantly increased amount of sub-modules in a MMC may increase the requirements for sensors and also increase the risk of failures. As a result, fault detection and diagnosis of MMC sub-modules are of great importance for continuous operation and post-fault maintenance. Therefore, in this paper, an effective fault diagnosis technique for real-time diagnosis of the switching device faults covering both the open-circuit faults and the short-circuit faults in MMC sub-modules is proposed, in which the faulty phase and the fault type is detected by analyzing the difference among the three output load currents, while the localization of the faulty switches is achieved by comparing the estimation results by the adaptive observer. In contrast to other methods that use additional sensors or devices, the presented technique uses the measured phase currents only, which are already available for MMC control. In additional, its operation, effectiveness and robustness are confirmed by simulation results under different operating conditions and load conditions.http://www.mdpi.com/1996-1073/8/7/7140modular multilevel converterfault detectionfault localizationcapacitor voltageadaptive observer
collection DOAJ
language English
format Article
sources DOAJ
author Hui Liu
Ke Ma
Poh Chiang Loh
Frede Blaabjerg
spellingShingle Hui Liu
Ke Ma
Poh Chiang Loh
Frede Blaabjerg
Online Fault Identification Based on an Adaptive Observer for Modular Multilevel Converters Applied to Wind Power Generation Systems
Energies
modular multilevel converter
fault detection
fault localization
capacitor voltage
adaptive observer
author_facet Hui Liu
Ke Ma
Poh Chiang Loh
Frede Blaabjerg
author_sort Hui Liu
title Online Fault Identification Based on an Adaptive Observer for Modular Multilevel Converters Applied to Wind Power Generation Systems
title_short Online Fault Identification Based on an Adaptive Observer for Modular Multilevel Converters Applied to Wind Power Generation Systems
title_full Online Fault Identification Based on an Adaptive Observer for Modular Multilevel Converters Applied to Wind Power Generation Systems
title_fullStr Online Fault Identification Based on an Adaptive Observer for Modular Multilevel Converters Applied to Wind Power Generation Systems
title_full_unstemmed Online Fault Identification Based on an Adaptive Observer for Modular Multilevel Converters Applied to Wind Power Generation Systems
title_sort online fault identification based on an adaptive observer for modular multilevel converters applied to wind power generation systems
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2015-07-01
description Due to the possibility of putting a large number of modules consisting of switches and capacitors connected in series, the modular multilevel converter (MMC) can easily be scaled to high power and high voltage power conversion, which is an attractive feature for filter-less and transformer-less design and helpful to achieve high efficiency. However, a significantly increased amount of sub-modules in a MMC may increase the requirements for sensors and also increase the risk of failures. As a result, fault detection and diagnosis of MMC sub-modules are of great importance for continuous operation and post-fault maintenance. Therefore, in this paper, an effective fault diagnosis technique for real-time diagnosis of the switching device faults covering both the open-circuit faults and the short-circuit faults in MMC sub-modules is proposed, in which the faulty phase and the fault type is detected by analyzing the difference among the three output load currents, while the localization of the faulty switches is achieved by comparing the estimation results by the adaptive observer. In contrast to other methods that use additional sensors or devices, the presented technique uses the measured phase currents only, which are already available for MMC control. In additional, its operation, effectiveness and robustness are confirmed by simulation results under different operating conditions and load conditions.
topic modular multilevel converter
fault detection
fault localization
capacitor voltage
adaptive observer
url http://www.mdpi.com/1996-1073/8/7/7140
work_keys_str_mv AT huiliu onlinefaultidentificationbasedonanadaptiveobserverformodularmultilevelconvertersappliedtowindpowergenerationsystems
AT kema onlinefaultidentificationbasedonanadaptiveobserverformodularmultilevelconvertersappliedtowindpowergenerationsystems
AT pohchiangloh onlinefaultidentificationbasedonanadaptiveobserverformodularmultilevelconvertersappliedtowindpowergenerationsystems
AT fredeblaabjerg onlinefaultidentificationbasedonanadaptiveobserverformodularmultilevelconvertersappliedtowindpowergenerationsystems
_version_ 1725784737571667968