Strategic Electric Distribution Network Sensing via Spectral Bandits
BibTeX
@inproceedings{talkington_spectralbandits_2024,
author={Talkington, Samuel and Gupta, Rahul and Asiamah, Richard and Buason, Paprapee and Molzahn, Daniel K.},
booktitle={63rd IEEE Conference on Decision and Control (CDC)},
title={{Strategic Electric Distribution Network Sensing via Spectral Bandits}},
year={2024},
volume={},
number={},
pages={4269-4276},
doi={10.1109/CDC56724.2024.10886546}
}Abstract
Despite their wide-scale deployment and ability to make accurate, high-frequency voltage measurements, communication network limitations have largely precluded the use of smart meters for real-time monitoring purposes in electric distribution systems. While smart meter communication networks have very limited bandwidth available per meter, they also have the ability to dedicate higher bandwidth to varying subsets of meters. Leveraging this capability in order to enable real-time monitoring from smart meters, this paper proposes an online bandwidth-constrained sensor sampling algorithm that exploits the graphical structure inherent in the power flow equations. The key idea is to use a spectral bandit framework where the estimated parameters are the graph Fourier transform coefficients of the nodal voltages. The structure provided by this framework promotes a sampling policy that strategically accounts for electrical distance. Maxima of sub-Gaussian random variables model the policy rewards, which relaxes distributional assumptions common in prior work. The scheme is implemented on realistic electrical networks to dynamically identify meters exposing violations of voltage magnitude limits and illustrating the effectiveness of the proposed method.