Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems

TL;DR

This paper introduces a recursive multi-task Gaussian process method for real-time integration of multi-timescale sensor data in low-observable distribution systems, enhancing grid monitoring accuracy.

Contribution

It proposes a novel recursive Gaussian process framework that aggregates multi-timescale measurements with or without network info for improved distribution system state estimation.

Findings

Effective multi-time-scale data reconciliation demonstrated on IEEE test systems.

Improved accuracy in distribution system state estimation.

Real-time processing capability achieved.

Abstract

The transition to a smarter grid is empowered by enhanced sensor deployments and smart metering infrastructure in the distribution system. Measurements from these sensors and meters can be used for many applications, including distribution system state estimation (DSSE). However, these measurements are typically sampled at different rates and could be intermittent due to losses during the aggregation process. These multi time-scale measurements should be reconciled in real-time to perform accurate grid monitoring. This paper tackles this problem by formulating a recursive multi-task Gaussian process (RGP-G) approach that sequentially aggregates sensor measurements. Specifically, we formulate a recursive multi-task GP with and without network connectivity information to reconcile the multi time-scale measurements in distribution systems. The proposed framework is capable of aggregating the multi-time scale measurements batch-wise or in real-time. Following the aggregation of the multi time-scale measurements, the spatial states of the consistent time-series are estimated using matrix completion based DSSE approach. Simulation results on IEEE 37 and IEEE 123 bus test systems illustrate the efficiency of the proposed methods from the standpoint of both multi time-scale data aggregation and DSSE.