Online Topology Identification for Smart Distribution Grids Based on LightGBM and Deep Neural Networks

To improve the security and economy of the power grid, future smart distribution grids should have a flexible topology reconfiguration as a fundamental characteristic. Most functions of the distribution management system (DMS), such as state estimation, power flow calculation, and voltage control, require the current topology of the grids. Therefore, topology identification is a key function of the DMS. Exploiting more accurate and efficient topology identification approaches is thus of great significance. Considering the topologies and operational characteristics of distribution grids, a topology identification framework of smart distribution grids under a machine learning scheme was developed in this study. An online topology identification method based on LightGBM and deep neural networks(DNNs)was also presented. A feature selection algorithm based on LightGBM was adopted to select the most effective measurements for topology identification task, and DNNs were built to model the mapping relationship between measurement data snapshots and topologies. Considering the possibility of missing measurement data in practice, an imputation method based on the minimum variance was proposed. The minimum variance was also used to identify unknown topologies that are not included in the training set. An incremental learning mechanism was then added to adjust the DNN and update the knowledge base of topologies. Compared with existing methods, the proposed method simply requires a small portion of nodal measurement snapshots;and is able to identify radial and mesh networks. Its computational efficiency meets the requirement of online applications. Finally, the proposed method was validated through IEEE 33-node and PG&E 69-node distribution grids using simulation data. The sensitivity to different noise levels, missing data, and unknown topologies was further analyzed. © 2020, Editorial Board of Journal of Tianjin University(Science and Technology). All right reserved.