A Novel Topology Identification Algorithm Fusing Multi-Source Measurement Data in Distribution Network

Abstract

With the development of new power systems, large amounts of decentralised clean energy and flexible loads will be connected to the smart grid, making the topology of the distribution network even more complex. At the same time, the demand for efficient interaction of “source-network-load” also makes the distribution network topology change with time, which makes it more difficult to identify the distribution network topology. However, topology identification is the basic data for power system regulation and safe optimal operation. Considering the above problems and requirements, this paper investigates and proposes a novel method to identify the topology of a distribution network based on the collaboration of data from multiple measurement devices. Firstly, a method based on multi-source data time synchronization method of μPMU measurement data is proposed, and generate additional data based on linear extrapolation to expand the available data set. Then, a method of topology optimization model identification based on least square method is proposed. Finally, the proposed algorithm is simulated in an example of the IEEE 33 bus feeder system standard. The results show that the scheme is effective in identifying the topology of the distribution network and improves the accuracy compared to traditional methods.