Intelligent condition recognition of transmission line based on digital image processing - Details

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EI Scopus

Abstract：

According　to　the　remote　monitoring　requirements　of　smart　grid　construction,　in　this　paper　the　condition　recognition　approach　combining　digital　image　processing　with　artificial　neural　networks　is　proposed　for　transmission　lines.　The　digital　image　processing　methods,　including　gray　scale　transformation,　histogram　modification,　wavelet　packet　denoising　and　edge　detection　are　used　to　process　the　images　of　transmission　lines　and　make　the　characteristics　more　outstanding.　After　dividing　the　images　into　some　regions　the　distribution　of　edge　features　of　transmission　line　components　is　extracted　as　characteristic　values.　This　method　has　good　adaptability.　At　last,　a　three-layer　back　propagation　(BP)　artificial　neural　network　(ANN)　is　constructed　and　applied　recognize　the　typical　transmission　line　conditions.　The　result　shows　that　this　approach　has　good　recognition　rate　and　popularization.

Keyword：

Image processing Electric lines Process control Automation Processing Neural networks Edge detection Multilayer neural networks

Author Community：

[ 1 ] [Zan, Tao]Key Laboratory of Beijing Municipality on Advanced Manufacturing Technology, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China
[ 2 ] [Wang, Min]Key Laboratory of Beijing Municipality on Advanced Manufacturing Technology, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China
[ 3 ] [Yu, Qingang]Key Laboratory of Beijing Municipality on Advanced Manufacturing Technology, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China
[ 4 ] [Li, Hongyun]Key Laboratory of Beijing Municipality on Advanced Manufacturing Technology, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China
[ 5 ] [Liu, Xiao]Key Laboratory of Beijing Municipality on Advanced Manufacturing Technology, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China
[ 6 ] [Jin, Hua]Key Laboratory of Beijing Municipality on Advanced Manufacturing Technology, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China