A Method Based on NGO-HKELM for the Autonomous Diagnosis of Semiconductor Power Switch Open-Circuit Faults in Three-Phase Grid-Connected Photovoltaic Inverters
With accelerating grid decarbonization and technological breakthroughs, grid-connected photovoltaic (PV) systems are continuously connected to distribution networks at all voltage levels. As the grid interaction interfaces between PV panels and the distribution network, PV inverters must operate fla...
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ftmdpi:oai:mdpi.com:/2071-1050/15/12/9588/ 2023-08-20T04:08:44+02:00 A Method Based on NGO-HKELM for the Autonomous Diagnosis of Semiconductor Power Switch Open-Circuit Faults in Three-Phase Grid-Connected Photovoltaic Inverters Xiao Xu Teng Zhang Ziwen Qiu Hui Gao Sanli Zhu agris 2023-06-14 application/pdf https://doi.org/10.3390/su15129588 EN eng Multidisciplinary Digital Publishing Institute Energy Sustainability https://dx.doi.org/10.3390/su15129588 https://creativecommons.org/licenses/by/4.0/ Sustainability; Volume 15; Issue 12; Pages: 9588 photovoltaic inverters fault detection fault localization maximum relevance-minimum redundancy hybrid kernel extreme learning machine northern goshawk optimization Text 2023 ftmdpi https://doi.org/10.3390/su15129588 2023-08-01T10:28:49Z With accelerating grid decarbonization and technological breakthroughs, grid-connected photovoltaic (PV) systems are continuously connected to distribution networks at all voltage levels. As the grid interaction interfaces between PV panels and the distribution network, PV inverters must operate flawlessly to avoid energy and financial losses. As the failure of semiconductor switches is the leading cause of abnormal operation of PV inverters and typically cannot be detected by internal protection circuits, this paper aims to develop a method for the autonomous diagnosis of semiconductor power switch open-circuit faults in three-phase grid-connected PV inverters. In this study, a ReliefF-mRMR-based multi-domain feature selection method is designed to ensure the completeness of the fault characteristics. An NGO-HKELM-based classification method is proposed to guarantee the desired balance between generalization and exploration capability. The proposed method overcomes the common problems of poor training efficiency and imbalances between generalization and exploration capabilities. The performance of the proposed method is verified with the detection of switch OC faults in a three-phase H-bridge inverter and neutral-point-clamped inverter, with diagnostic accuracy of 100% and 99.46% respectively. Text Northern Goshawk MDPI Open Access Publishing Sustainability 15 12 9588 |
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MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
photovoltaic inverters fault detection fault localization maximum relevance-minimum redundancy hybrid kernel extreme learning machine northern goshawk optimization |
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photovoltaic inverters fault detection fault localization maximum relevance-minimum redundancy hybrid kernel extreme learning machine northern goshawk optimization Xiao Xu Teng Zhang Ziwen Qiu Hui Gao Sanli Zhu A Method Based on NGO-HKELM for the Autonomous Diagnosis of Semiconductor Power Switch Open-Circuit Faults in Three-Phase Grid-Connected Photovoltaic Inverters |
topic_facet |
photovoltaic inverters fault detection fault localization maximum relevance-minimum redundancy hybrid kernel extreme learning machine northern goshawk optimization |
description |
With accelerating grid decarbonization and technological breakthroughs, grid-connected photovoltaic (PV) systems are continuously connected to distribution networks at all voltage levels. As the grid interaction interfaces between PV panels and the distribution network, PV inverters must operate flawlessly to avoid energy and financial losses. As the failure of semiconductor switches is the leading cause of abnormal operation of PV inverters and typically cannot be detected by internal protection circuits, this paper aims to develop a method for the autonomous diagnosis of semiconductor power switch open-circuit faults in three-phase grid-connected PV inverters. In this study, a ReliefF-mRMR-based multi-domain feature selection method is designed to ensure the completeness of the fault characteristics. An NGO-HKELM-based classification method is proposed to guarantee the desired balance between generalization and exploration capability. The proposed method overcomes the common problems of poor training efficiency and imbalances between generalization and exploration capabilities. The performance of the proposed method is verified with the detection of switch OC faults in a three-phase H-bridge inverter and neutral-point-clamped inverter, with diagnostic accuracy of 100% and 99.46% respectively. |
format |
Text |
author |
Xiao Xu Teng Zhang Ziwen Qiu Hui Gao Sanli Zhu |
author_facet |
Xiao Xu Teng Zhang Ziwen Qiu Hui Gao Sanli Zhu |
author_sort |
Xiao Xu |
title |
A Method Based on NGO-HKELM for the Autonomous Diagnosis of Semiconductor Power Switch Open-Circuit Faults in Three-Phase Grid-Connected Photovoltaic Inverters |
title_short |
A Method Based on NGO-HKELM for the Autonomous Diagnosis of Semiconductor Power Switch Open-Circuit Faults in Three-Phase Grid-Connected Photovoltaic Inverters |
title_full |
A Method Based on NGO-HKELM for the Autonomous Diagnosis of Semiconductor Power Switch Open-Circuit Faults in Three-Phase Grid-Connected Photovoltaic Inverters |
title_fullStr |
A Method Based on NGO-HKELM for the Autonomous Diagnosis of Semiconductor Power Switch Open-Circuit Faults in Three-Phase Grid-Connected Photovoltaic Inverters |
title_full_unstemmed |
A Method Based on NGO-HKELM for the Autonomous Diagnosis of Semiconductor Power Switch Open-Circuit Faults in Three-Phase Grid-Connected Photovoltaic Inverters |
title_sort |
method based on ngo-hkelm for the autonomous diagnosis of semiconductor power switch open-circuit faults in three-phase grid-connected photovoltaic inverters |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2023 |
url |
https://doi.org/10.3390/su15129588 |
op_coverage |
agris |
genre |
Northern Goshawk |
genre_facet |
Northern Goshawk |
op_source |
Sustainability; Volume 15; Issue 12; Pages: 9588 |
op_relation |
Energy Sustainability https://dx.doi.org/10.3390/su15129588 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/su15129588 |
container_title |
Sustainability |
container_volume |
15 |
container_issue |
12 |
container_start_page |
9588 |
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1774721199770697728 |