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- W4310608949 abstract "This paper proposes a probabilistic topology identification framework combining state estimation (SE) and data-driven approaches. The proposed framework aims to obtain probabilistic information about the possible topologies from the real-time snapshot to exclude many low-probability topologies. It avoids the combinatorial explosion caused by too many topology errors in the topology search approach, and it solves the problem of SE-based topology identification when SE is non-observable or non-convergent. The proposed framework is mainly based on the Gaussian mixture model (GMM) to achieve clustering of simulated data with different topologies, so that probabilistic information about their possible topologies can be quickly obtained after collecting real-time snapshots. Simulations based on the IEEE 14-bus system show that GMM-based topology clustering achieves better clustering results compared to <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$K$</tex> -means clustering and can be applied to the distribution network with only voltage measurements and a few phase angle measurements. The proposed probabilistic topology identification framework can provide prior knowledge of the topology when the original SE is non-observable and does not change the software architecture of the original SE, which is a beneficial complement to it." @default.
- W4310608949 created "2022-12-13" @default.
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- W4310608949 date "2022-11-03" @default.
- W4310608949 modified "2023-09-28" @default.
- W4310608949 title "Probabilistic Topology Identification Combining State Estimation and Data-Driven Approaches" @default.
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- W4310608949 doi "https://doi.org/10.1109/ciycee55749.2022.9959068" @default.
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