Error Bounds for Radial Network Topology Learning from Quantized Measurements

BibTeX

@article{talkington_quantization_2026,
      title={{Error Bounds for Radial Network Topology Learning from Quantized Measurements}}, 
      author={Samuel Talkington and Aditya Rangarajan and Pedro A. de Alcântara and Line Roald and Daniel K. Molzahn and Daniel R. Fuhrmann},
      year={2026},
      journal={IEEE Transactions on Power Systems},
      doi={10.1109/TPWRS.2026.3666699},
      eprint={2508.05620},
      archivePrefix={arXiv},
      primaryClass={eess.SY},
      url={https://arxiv.org/abs/2508.05620}, 
}

Abstract

We probabilistically bound the error of a solution to a radial network topology learning problem where both connectivity and line parameters are estimated. In our model, data errors are introduced by the precision of the sensors, i.e., quantization. This produces a nonlinear measurement model that embeds the operation of the sensor communication network into the learning problem, expanding beyond the additive noise models typically seen in power system estimation algorithms. We show that the error of a learned radial network topology is proportional to the quantization bin width and grows sublinearly in the number of nodes, provided that the number of samples per node is logarithmic in the number of nodes.

Making Every Bit Count for A-Optimal State Estimation
Cameron Khanpour, Daniel Turizo, and Samuel Talkington
arXiv / bibTeX
Error Bounds for Radial Network Topology Learning from Quantized Measurements
Samuel Talkington, Aditya Rangarajan, Pedro A. de Alcântara, Line Roald, Daniel K. Molzahn, and Daniel R. Fuhrmann
IEEE Transactions on Power Systems / arXiv / bibTeX
Localized Structure in Secondary Distribution System Voltage Sensitivity Matrices
Samuel Talkington, Santiago Grijalva, Matthew J. Reno, Joseph A. Azzolini, and Jouni Peppanen
Electric Power Systems Research / bibTeX
Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power Injections
Samuel Talkington, Daniel Turizo, Santiago Grijalva, Jorge Fernandez, Daniel K. Molzahn
IEEE Transactions on Power Systems / arXiv / code / bibTeX
Sparse Time Series Sampling for Recovery of Behind-the-Meter Inverter Control Models
Samuel Talkington, Santiago Grijalva, and Matthew J. Reno
IEEE PES Innovative Smart Grid Technologies Conference (ISGT) / bibTeX