Graph Algorithms for Topology Identification using Power Grid Probing
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Abstract (arXiv)
To perform any meaningful optimization task, power distribution operators need to know the topology and line impedances of their electric networks. Nevertheless, distribution grids currently lack a comprehensive metering infrastructure. Although smart inverters are widely used for control purposes, they have been recently advocated as the means for an active data acquisition paradigm: Reading the voltage deviations induced by intentionally perturbing inverter injections, the system operator can potentially recover the electric grid topology. Adopting inverter probing for feeder processing, a suite of graph-based topology identification algorithms is developed here. If the grid is probed at all leaf nodes but voltage data are metered at all nodes, the entire feeder topology can be successfully recovered. When voltage data are collected only at probing buses, the operator can find a reduced feeder featuring key properties and similarities to the actual feeder. To handle modeling inaccuracies and load non-stationarity, noisy probing data need to be preprocessed. If the suggested guidelines on the magnitude and duration of probing are followed, the recoverability guarantees carry over from the noiseless to the noisy setup with high probability.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Mar 2018.
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Citing papers per year: 2021: 2; 2022: 3.
5 papers.
- 2022Learning Distribution Grid Topologies: A Tutorialcited by 3
- 2021Bayesian Error-in-Variables Models for the Identification of Power Networkscited by 0
- 2021Data-driven Modeling for Distribution Grids Under Partial Observabilitycited by 0
- 2022Structure from Voltagecited by 0
- 2022Explainable Graph Theory-Based Identification of Meter-Transformer Mappingcited by 0
- 2022Learning Distribution Grid Topologies: A Tutorial22 Jun 2022
- 2022Explainable Graph Theory-Based Identification of Meter-Transformer Mapping19 May 2022
- 2022Structure from Voltage28 Feb 2022
- 2021Data-driven Modeling for Distribution Grids Under Partial Observability18 Aug 2021
- 2021Bayesian Error-in-Variables Models for the Identification of Power Networks9 Jul 2021