Topology Identification for Distribution Network Driven by Diverse Measurement Data

The analysis, optimization and control of the power grid becomes more complicated as the scale of renewable energy continues to expand. There are two factors causing the scarcity of accurate distribution network topology and parameters. Firstly, with lots of distributed equipment attached, the distribution network changes topology frequently. Secondly, the amount of monitoring equipment is limited. However, inferring the correct network topology through historical operating data becomes possible crediting to data-driven technology. Current identification methods require full sets of measurements. Unfortunately, actual measurements are generally incomplete and are from various sources. Data sets with this property are defined as diverse measurement data in this paper. Driven by diverse measurement data, a distribution network topology identification algorithm is proposed. Regression method and the branch power flow equation are employed to perform the identification. The analysis of IEEE 7-node and 33-node networks shows that accurate topology identification results are achieved, which indicates the algorithm proposed is robust and suitable for a variety of identification scenarios.