Strategic Electric Distribution Network Sensing via Spectral Bandits

TL;DR

This paper introduces a spectral bandit-based online sensor sampling algorithm for real-time voltage monitoring in electric distribution networks, optimizing smart meter data collection under bandwidth constraints.

Contribution

It presents a novel spectral bandit framework leveraging graph Fourier transforms to improve real-time monitoring with limited communication bandwidth.

Findings

Effective voltage violation detection demonstrated on synthetic network

Spectral bandit approach outperforms traditional sampling methods

Promotes strategic meter selection based on electrical distance

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. Although smart meter communication networks have limited bandwidth available per meter, they also have the ability to dedicate higher bandwidth to varying subsets of meters. Using this capability to enable real-time monitoring from smart meters, this paper proposes an online bandwidth-constrained sensor sampling algorithm that takes advantage of 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 a synthetic electrical network to dynamically identify meters exposing violations of voltage magnitude limits, illustrating the effectiveness of the proposed method.