Topology identification method of low voltage distribution network based on data association analysis

Abstract

This paper introduces a topology identification method of low-voltage distribution network based on data association analysis. The low-voltage distribution network to be identified is divided into the single distribution transformer power off station areas, multiple distribution transformer station areas caused by 10kV distribution line power outage and the distribution transformer areas without power interruption based on low-voltage distribution network blackout event, restoration power on event and geographic location information. In each type of station area, Tanimoto similarity coefficient is used to calculate the correlation and non-correlation between distribution transformer, branch box, meter box and smart meter in each group, so as to achieve the topology identification of the low-voltage distribution network. And then the identified topology can be verified by combining the topology verification rules of the same distribution transformer station area has the same of outage and live state, outage duration, geographical location, power supply radius and so on. Through the actual case, it is proved that the method proposed in this paper can solve the problems of large amount of calculation, inaccuracy of calculation results, and inability to verify based on the existing big data mining methods. It realizes the efficient and accurate identification of distribution transformer substation topology, and improves the information level and data quality of distribution network.