Hybrid data and model‐driven joint identification of distribution‐network topology and parameters

Abstract

Because of frequent changes in the topologies of distribution networks, the aging of lines and insufficient monitoring capacity compared to the transmission grid, the topology and line parameters are difficult to determine. Here, a hybrid data and model‐driven method is proposed for identifying the topologies and line parameters of distribution networks in the absence of voltage‐angle measurements. First, a topology identification model based on an attention mechanism and convolutional neural networks is constructed as an upper‐layer model, which requires a small number of voltage measurement snapshots of key nodes to identify the current topology. Second, a model‐driven two‐stage line‐parameter identification model is constructed as the lower‐layer model. Case studies based on the IEEE33 and PG&E69 node distribution systems are performed to validate the proposed method. The results confirm that the proposed method is effective for a stable and accurate identification of the topology and line parameters of distribution networks within a certain error range. In addition, the proposed method exhibits a reasonable generalization capability, and all models are equally applicable to both radial and ring networks.

Research on Topology Dynamic Perception and Line Parameter Identification of Distribution Network

“…Considering that the distribution network topology and line parameters will affect the monitoring and control of the distribution network, smart meters (SM) and phase measurement unit(PMU) data to analyze the network topology [17][18][19]. In terms of line parameter identification of distribution network, the branch impedance parameter estimation and topology identification of the whole network based on the measurement data obtained by AMI equipment [20][21][22].…”