A novel fault diagnosis method for photovoltaic array based on BP-Adaboost strong classifier - Details

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EI Scopus

Abstract：

Accurate　fault　diagnosis　of　photovoltaic　(PV)　array　is　important　for　effective　operation　of　PV　systems.　The　back　propagation　neural　network　(BPNN)　based　classifier　model　has　wide　application　in　fault　diagnosis　for　PV　array.　Due　to　insufficient　accuracy　obtained　by　using　single　BPNN,　this　paper　proposes　a　novel　fault　diagnosis　scheme　based　on　BP-Adaboost　strong　classifier.　Firstly,　several　indicators　constitute　an　effective　feature　vector　which　is　applied　to　build　several　BPNN　based　weak　classifier　models.　Secondly,　Adaboost　algorithm　is　adopted　to　build　a　strong　classifier　by　combining　those　weak　classifiers　into　the　final　output　with　certain　weights.　Four　operation　conditions　including　normal　condition,　short　circuit　fault,　partial　shade　fault,　open　circuit　fault　can　be　accurately　identified　by　the　proposed　method.　Dataset　from　a　1.8　kW　grid-connected　PV　system　with　6　×　3　PV　array　are　applied　to　experimentally　test　the　performance　of　the　developed　method.　©　Published　under　licence　by　IOP　Publishing　Ltd.

Keyword：

Adaptive boosting Backpropagation Failure analysis Fault detection Neural networks Photovoltaic cells Statistical tests

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[ 1 ] [Zheng, Y.L.]College of Physics and Information Engineering, And Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou; 350116, China
[ 2 ] [Zheng, Y.L.]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 ] [Yu, J.L.]College of Physics and Information Engineering, And Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou; 350116, China
[ 6 ] [Yu, J.L.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou; 213164, China
[ 7 ] [Lai, Y.F.]College of Physics and Information Engineering, And Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou; 350116, China
[ 8 ] [Lai, Y.F.]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 ] [Chen, Z.C.]College of Physics and Information Engineering, And Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou; 350116, China
[ 11 ] [Chen, Z.C.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou; 213164, China
[ 12 ] [Wu, L.J.]College of Physics and Information Engineering, And Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou; 350116, China
[ 13 ] [Wu, L.J.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou; 213164, China
[ 14 ] [Cheng, S.Y.]College of Physics and Information Engineering, And Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou; 350116, China
[ 15 ] [Cheng, S.Y.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou; 213164, China
[ 16 ] [Chen, G.D.]College of Physics and Information Engineering, And Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou; 350116, China
[ 17 ] [Chen, G.D.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou; 213164, China