Identification Method of Feeder-Consumer Connectivity in Low-Voltage Distribution Network Based on Multivariate Data Feature
To solve the problem that feeder-consumer connectivity is difficult to be efficiently identified and accurately checked due to the monotonous and large-scale measurement data,a method of identifying feeder-consumer connectivity in low-voltage distribu-tion network based on multivariate data feature is proposed.Firstly,the correlation of outage equipment is analyzed,and the abnormal data preprocessing method based on fuzzy C-means algorithm,threshold division and Neville interpolation is proposed for smart meter sampling anomalies.Secondly,a clustering method of smart meters based on outage correlation and Hausdroff distance is proposed.Thirdly,based on Kirchhoff's current law,a quadratic programming model for the identification of feeder-consumer con-nectivity is established,which is effectively solved by a solver after transformation.Finally,the effectiveness and superiority of the proposed identification method of feeder-consumer connectivity are verified by a practical example.
low-voltage distribution networkfeeder-consumer connectivitymultivariate data feature
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