Smart Meters Integration in Distribution System State Estimation with Collaborative Filtering and Deep Gaussian Process

TL;DR

This paper introduces a novel method combining collaborative filtering and deep Gaussian processes to improve distribution system state estimation by integrating smart meter data and inferring pseudo measurements, enhancing accuracy.

Contribution

It presents a new approach that leverages deep Gaussian processes with collaborative filtering for better integration of smart meter data in distribution system state estimation.

Findings

Higher estimation accuracy demonstrated in numerical tests

Effective integration of slow and fast time-scale measurements

Robustness against anomalies in measurement data

Abstract

The problem of state estimations for electric distribution system is considered. A collaborative filtering approach is proposed in this paper to integrate the slow time-scale smart meter measurements in the distribution system state estimation, in which the deep Gaussian process is incorporated to infer the fast time-scale pseudo measurements and avoid anomalies. Numerical tests have demonstrated the higher estimation accuracy of the proposed method.