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Open Circuit Fault Detection of T-Type Grid Connected Inverters Using Fast S Transform and Random Forest

To detect open circuit faults of grid-connected T-type inverters, this paper proposed a real-time method based on fast S transform and random forest. The three-phase fault currents of the inverter were used as the inputs of the new method and no additional sensors were needed. Some fault current har...

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Detalles Bibliográficos
Autores principales: You, Li, Ling, Zaixun, Cui, Yibo, Cai, Wanli, He, Shunfan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10217761/
https://www.ncbi.nlm.nih.gov/pubmed/37238533
http://dx.doi.org/10.3390/e25050778
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author You, Li
Ling, Zaixun
Cui, Yibo
Cai, Wanli
He, Shunfan
author_facet You, Li
Ling, Zaixun
Cui, Yibo
Cai, Wanli
He, Shunfan
author_sort You, Li
collection PubMed
description To detect open circuit faults of grid-connected T-type inverters, this paper proposed a real-time method based on fast S transform and random forest. The three-phase fault currents of the inverter were used as the inputs of the new method and no additional sensors were needed. Some fault current harmonics and direct current components were selected as the fault features. Then, fast S transform was used to extract the features of fault currents, and random forest was used to recognize the features and the fault type, as well as locate the faulted switches. The simulation and experiments showed that the new method could detect open-circuit faults with low computation complexity and the detection accuracy was 100%. The real-time and accurate open circuit fault detection method was proven effective for grid-connected T-type inverter monitoring.
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spelling pubmed-102177612023-05-27 Open Circuit Fault Detection of T-Type Grid Connected Inverters Using Fast S Transform and Random Forest You, Li Ling, Zaixun Cui, Yibo Cai, Wanli He, Shunfan Entropy (Basel) Article To detect open circuit faults of grid-connected T-type inverters, this paper proposed a real-time method based on fast S transform and random forest. The three-phase fault currents of the inverter were used as the inputs of the new method and no additional sensors were needed. Some fault current harmonics and direct current components were selected as the fault features. Then, fast S transform was used to extract the features of fault currents, and random forest was used to recognize the features and the fault type, as well as locate the faulted switches. The simulation and experiments showed that the new method could detect open-circuit faults with low computation complexity and the detection accuracy was 100%. The real-time and accurate open circuit fault detection method was proven effective for grid-connected T-type inverter monitoring. MDPI 2023-05-10 /pmc/articles/PMC10217761/ /pubmed/37238533 http://dx.doi.org/10.3390/e25050778 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
You, Li
Ling, Zaixun
Cui, Yibo
Cai, Wanli
He, Shunfan
Open Circuit Fault Detection of T-Type Grid Connected Inverters Using Fast S Transform and Random Forest
title Open Circuit Fault Detection of T-Type Grid Connected Inverters Using Fast S Transform and Random Forest
title_full Open Circuit Fault Detection of T-Type Grid Connected Inverters Using Fast S Transform and Random Forest
title_fullStr Open Circuit Fault Detection of T-Type Grid Connected Inverters Using Fast S Transform and Random Forest
title_full_unstemmed Open Circuit Fault Detection of T-Type Grid Connected Inverters Using Fast S Transform and Random Forest
title_short Open Circuit Fault Detection of T-Type Grid Connected Inverters Using Fast S Transform and Random Forest
title_sort open circuit fault detection of t-type grid connected inverters using fast s transform and random forest
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10217761/
https://www.ncbi.nlm.nih.gov/pubmed/37238533
http://dx.doi.org/10.3390/e25050778
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