Fault diagnosis and classification for photovoltaic arrays based on principal component analysis and support vector machine - Details

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In　order　to　dig　out　more　typical　features　of　photovoltaic　(PV)　with　multitudinous　characteristic　parameters,　and　realize　fault　diagnosis　and　classification　for　PV　arrays　effectively.　A　method　based　on　principal　component　analysis　(PCA)　has　been　proposed　in　this　paper.　At　first,　the　data　set　of　PV　array　is　processed　by　PCA　and　then　a　transform　matrix　is　produced.　Second,　the　processed　data　will　be　classified　by　supporting　vector　machine　(SVM).　Finally,　a　classification　model　will　be　built.　Two　sets　of　data,　collected　from　PV　simulation　system　and　actual　PV　array,　are　adopted　to　examine　this　method.　The　result　shows　that　the　method　is　able　to　recognize　four　kinds　of　states　accurately　(normal,　open　circuit,　short　circuit　and　partial　shadow).　Consequently,　the　fault　of　PV　array　can　be　diagnosed　and　classified.　©　Published　under　licence　by　IOP　Publishing　Ltd.

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[ 1 ] [Chen, L.C.]College of Physics and Information Engineering, And Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou, 350116, China
[ 2 ] [Chen, L.C.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou, 213164, China
[ 3 ] [Lin, P.J.]College of Physics and Information Engineering, And Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou, 350116, China
[ 4 ] [Lin, P.J.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou, 213164, China
[ 5 ] [Zhang, J.]College of Physics and Information Engineering, And Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou, 350116, China
[ 6 ] [Zhang, J.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou, 213164, China
[ 7 ] [Chen, Z.C.]College of Physics and Information Engineering, And Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou, 350116, China
[ 8 ] [Chen, Z.C.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou, 213164, China
[ 9 ] [Lin, Y.H.]College of Computer and Information Sciences, Fujian Agriculture and Forest University, Fuzhou, 350002, China
[ 10 ] [Wu, L.J.]College of Physics and Information Engineering, And Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou, 350116, China
[ 11 ] [Wu, L.J.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou, 213164, China
[ 12 ] [Cheng, S.Y.]College of Physics and Information Engineering, And Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou, 350116, China
[ 13 ] [Cheng, S.Y.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou, 213164, China

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IOP Conference Series: Earth and Environmental Science

ISSN： 1755-1307

Year： 2018

Issue： 1

Volume： 188

Language： English

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SCOPUS Cited Count： 13

ESI Highly Cited Papers on the List： 0 Unfold All

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30 Days PV： 1

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