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- Title
Hybrid data and model‐driven joint identification of distribution‐network topology and parameters.
- Authors
Yang, Xiu; Jiang, Jiafu; Liu, Fang; Tang, Jinzhang
- 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.
- Subjects
CONVOLUTIONAL neural networks; RADIAL distribution function; RING networks; ELECTRIC fault location; TOPOLOGY; ELECTRIC potential measurement
- Publication
IET Generation, Transmission & Distribution (Wiley-Blackwell), 2022, Vol 16, Issue 23, p4846
- ISSN
1751-8687
- Publication type
Article
- DOI
10.1049/gtd2.12634