Online Fault Diagnosis for Photovoltaic Arrays Based on Fisher Discrimination Dictionary Learning for Sparse Representation

The nonlinear output characteristics of PV arrays and maximum power point tracking (MPPT) techniques bring more difficulties to fault diagnosis. The fault diagnosis model based on electrical transient time-domain analysis is an effective method for solving the above problems. However, existing studi...

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Autores principales:	Peng Xi, Peijie Lin, Yaohai Lin, Haifang Zhou, Shuying Cheng, Zhicong Chen, Lijun Wu
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Photovoltaic array fault diagnosis machine learning sparse representation fisher discrimination criterion fisher discrimination dictionary learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/e6cd972e1c7643a698369b6563f58fdb
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id	oai:doaj.org-article:e6cd972e1c7643a698369b6563f58fdb
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spelling	oai:doaj.org-article:e6cd972e1c7643a698369b6563f58fdb2021-11-19T00:05:51ZOnline Fault Diagnosis for Photovoltaic Arrays Based on Fisher Discrimination Dictionary Learning for Sparse Representation2169-353610.1109/ACCESS.2021.3059431https://doaj.org/article/e6cd972e1c7643a698369b6563f58fdb2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9354631/https://doaj.org/toc/2169-3536The nonlinear output characteristics of PV arrays and maximum power point tracking (MPPT) techniques bring more difficulties to fault diagnosis. The fault diagnosis model based on electrical transient time-domain analysis is an effective method for solving the above problems. However, existing studies using transient processes usually train their models by extensive labeled datasets, and some approaches apply normalization methods with environmental condition sensors or reference PV panels. Therefore, Fisher discrimination dictionary learning (FDDL) for sparse representation is explored for diagnosing PV array faults, including line-to-line faults (LLF), open-circuit faults (OCF), and partial shading faults (PSF), with a small labeled dataset, and a dynamic normalization method without additional sensors is proposed to process transient data. Moreover, LLF and PSF that have similar characteristics under low mismatch should be further distinguished. The proposed model is designed with two stages. In the first stage, a multiple classifier trained using small labeled datasets with all fault types is applied to diagnose all kinds of studied PV array faults. Then, a dictionary only for PSF and LLF is learned in the second stage to further identify LLF and PSF. Finally, a 1.8 kW rooftop grid-connected PV system with <inline-formula> <tex-math notation="LaTeX">$6\times3$ </tex-math></inline-formula> PV arrays is applied to validate the performance of the proposed model. The comparison result shows the superiority of the proposed model.Peng XiPeijie LinYaohai LinHaifang ZhouShuying ChengZhicong ChenLijun WuIEEEarticlePhotovoltaic arrayfault diagnosismachine learningsparse representationfisher discrimination criterionfisher discrimination dictionary learningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 30180-30192 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Photovoltaic array fault diagnosis machine learning sparse representation fisher discrimination criterion fisher discrimination dictionary learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Photovoltaic array fault diagnosis machine learning sparse representation fisher discrimination criterion fisher discrimination dictionary learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Peng Xi Peijie Lin Yaohai Lin Haifang Zhou Shuying Cheng Zhicong Chen Lijun Wu Online Fault Diagnosis for Photovoltaic Arrays Based on Fisher Discrimination Dictionary Learning for Sparse Representation
description	The nonlinear output characteristics of PV arrays and maximum power point tracking (MPPT) techniques bring more difficulties to fault diagnosis. The fault diagnosis model based on electrical transient time-domain analysis is an effective method for solving the above problems. However, existing studies using transient processes usually train their models by extensive labeled datasets, and some approaches apply normalization methods with environmental condition sensors or reference PV panels. Therefore, Fisher discrimination dictionary learning (FDDL) for sparse representation is explored for diagnosing PV array faults, including line-to-line faults (LLF), open-circuit faults (OCF), and partial shading faults (PSF), with a small labeled dataset, and a dynamic normalization method without additional sensors is proposed to process transient data. Moreover, LLF and PSF that have similar characteristics under low mismatch should be further distinguished. The proposed model is designed with two stages. In the first stage, a multiple classifier trained using small labeled datasets with all fault types is applied to diagnose all kinds of studied PV array faults. Then, a dictionary only for PSF and LLF is learned in the second stage to further identify LLF and PSF. Finally, a 1.8 kW rooftop grid-connected PV system with <inline-formula> <tex-math notation="LaTeX">$6\times3$ </tex-math></inline-formula> PV arrays is applied to validate the performance of the proposed model. The comparison result shows the superiority of the proposed model.
format	article
author	Peng Xi Peijie Lin Yaohai Lin Haifang Zhou Shuying Cheng Zhicong Chen Lijun Wu
author_facet	Peng Xi Peijie Lin Yaohai Lin Haifang Zhou Shuying Cheng Zhicong Chen Lijun Wu
author_sort	Peng Xi
title	Online Fault Diagnosis for Photovoltaic Arrays Based on Fisher Discrimination Dictionary Learning for Sparse Representation
title_short	Online Fault Diagnosis for Photovoltaic Arrays Based on Fisher Discrimination Dictionary Learning for Sparse Representation
title_full	Online Fault Diagnosis for Photovoltaic Arrays Based on Fisher Discrimination Dictionary Learning for Sparse Representation
title_fullStr	Online Fault Diagnosis for Photovoltaic Arrays Based on Fisher Discrimination Dictionary Learning for Sparse Representation
title_full_unstemmed	Online Fault Diagnosis for Photovoltaic Arrays Based on Fisher Discrimination Dictionary Learning for Sparse Representation
title_sort	online fault diagnosis for photovoltaic arrays based on fisher discrimination dictionary learning for sparse representation
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/e6cd972e1c7643a698369b6563f58fdb
work_keys_str_mv	AT pengxi onlinefaultdiagnosisforphotovoltaicarraysbasedonfisherdiscriminationdictionarylearningforsparserepresentation AT peijielin onlinefaultdiagnosisforphotovoltaicarraysbasedonfisherdiscriminationdictionarylearningforsparserepresentation AT yaohailin onlinefaultdiagnosisforphotovoltaicarraysbasedonfisherdiscriminationdictionarylearningforsparserepresentation AT haifangzhou onlinefaultdiagnosisforphotovoltaicarraysbasedonfisherdiscriminationdictionarylearningforsparserepresentation AT shuyingcheng onlinefaultdiagnosisforphotovoltaicarraysbasedonfisherdiscriminationdictionarylearningforsparserepresentation AT zhicongchen onlinefaultdiagnosisforphotovoltaicarraysbasedonfisherdiscriminationdictionarylearningforsparserepresentation AT lijunwu onlinefaultdiagnosisforphotovoltaicarraysbasedonfisherdiscriminationdictionarylearningforsparserepresentation
_version_	1718420701799514112

Online Fault Diagnosis for Photovoltaic Arrays Based on Fisher Discrimination Dictionary Learning for Sparse Representation

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