Topology Identification in Distribution Network based on Phasor Measurement Units

Kwaye, Marcel Pendieu; Lazzari, Riccardo

doi:10.1109/eeeic/icpseurope51590.2021.9584505

Cited by 3 publications

(1 citation statement)

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“…Many studies have been performed on the identification of the topology and line parameters of distribution networks. Regarding topology identification, a distribution-network topology was obtained by monitoring the Jacobian matrix and dynamic system state matrix changes in real time according to measured data [5,6]. A topology identification method using the correlation between node voltages to determine the feeder to which the load belongs and the upstream and downstream FIGURE 1 Framework of the joint topology and line parameter identification models locations of the load in the feeder according to the voltage magnitude was proposed [7,8].…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Jiang

Liu

et al. 2022

IET Generation Trans & Dist

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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.

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