Fault diagnosis model for photovoltaic array using a dual-channels convolutional neural network with a feature selection structure

Lu, X; Lin, P; Cheng, S; Fang, G; He, X; Chen, Z; Wu, L

Fault diagnosis model for photovoltaic array using a dual-channels convolutional neural network with a feature selection structure

Lu, X Lin, P Cheng, S Fang, G

He, X

Chen, Z Wu, L

Permalink

Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Energy Conversion and Management, 2021, 248, pp. 1-13
Issue Date:: 2021-11-15

Closed Access

	Filename	Description	Size
	1-s2.0-S0196890421009535-main.pdf	Published version	7.76 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Lu, X
dc.contributor.author	Lin, P
dc.contributor.author	Cheng, S
dc.contributor.author	Fang, G https://orcid.org/0000-0003-0845-6718
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X
dc.contributor.author	Chen, Z
dc.contributor.author	Wu, L
dc.date.accessioned	2022-03-15T05:57:42Z
dc.date.available	2022-03-15T05:57:42Z
dc.date.issued	2021-11-15
dc.identifier.citation	Energy Conversion and Management, 2021, 248, pp. 1-13
dc.identifier.issn	0196-8904
dc.identifier.issn	1879-2227
dc.identifier.uri	http://hdl.handle.net/10453/155241
dc.description.abstract	The effective fault diagnosis algorithm for the DC side photovoltaic (PV) array of a PV system (PVS) plays an important role in the operation efficiency and safety for PV power plants. But for fault diagnosis models it may fail to diagnose PV array (PVA) faults without detailed and quite fine fault features, especially line-line faults (LLF) occurring in the PVS that works under complex working conditions like low irradiance conditions and LLF with fault impedance. To address these challenges, this paper proposes a fault diagnosis scheme to diagnose different PVA faults using a proposed Dual-channel Convolutional Neural Network (DcCNN), which is able to automatically extract features and weight these features for fault classification. The important and fine features from the current and voltage electrical time series graph (ETSG) are extracted respectively by DcCNN in a double input way. Then, a proposed feature selection structure (FSS) is designed to improve the proposed fault diagnosis model capacity for diagnosing PVA faults under various conditions, including LLF, partial shading condition (PSC) and open circuit faults (OCF). Comparing to manually designed features, FSS not only helps DcCNN extract important features from PVA current and voltage automatically but also evaluates extracted features for further classification of DcCNN. Moreover, in the training stage, a proposed penalty is applied on DcCNN to constrain FSS, resulting in its sparse weight distribution. A comprehensive experiment based on a laboratory roof grid connected PVS is conducted. The results demonstrate the superior performance of the proposed approach compared with other algorithms as it can extract high-discriminative features from PVA current and voltage for different PVA faults, which is also effective on diagnosing LLF under low irradiance conditions and LLF with fault impedance.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Energy Conversion and Management
dc.relation.isbasedon	10.1016/j.enconman.2021.114777
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Energy
dc.title	Fault diagnosis model for photovoltaic array using a dual-channels convolutional neural network with a feature selection structure
dc.type	Journal Article
utslib.citation.volume	248
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-03-15T05:57:31Z
pubs.publication-status	Published
pubs.volume	248

Abstract:

The effective fault diagnosis algorithm for the DC side photovoltaic (PV) array of a PV system (PVS) plays an important role in the operation efficiency and safety for PV power plants. But for fault diagnosis models it may fail to diagnose PV array (PVA) faults without detailed and quite fine fault features, especially line-line faults (LLF) occurring in the PVS that works under complex working conditions like low irradiance conditions and LLF with fault impedance. To address these challenges, this paper proposes a fault diagnosis scheme to diagnose different PVA faults using a proposed Dual-channel Convolutional Neural Network (DcCNN), which is able to automatically extract features and weight these features for fault classification. The important and fine features from the current and voltage electrical time series graph (ETSG) are extracted respectively by DcCNN in a double input way. Then, a proposed feature selection structure (FSS) is designed to improve the proposed fault diagnosis model capacity for diagnosing PVA faults under various conditions, including LLF, partial shading condition (PSC) and open circuit faults (OCF). Comparing to manually designed features, FSS not only helps DcCNN extract important features from PVA current and voltage automatically but also evaluates extracted features for further classification of DcCNN. Moreover, in the training stage, a proposed penalty is applied on DcCNN to constrain FSS, resulting in its sparse weight distribution. A comprehensive experiment based on a laboratory roof grid connected PVS is conducted. The results demonstrate the superior performance of the proposed approach compared with other algorithms as it can extract high-discriminative features from PVA current and voltage for different PVA faults, which is also effective on diagnosing LLF under low irradiance conditions and LLF with fault impedance.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/155241