Enriching Load Data Using Micro-PMUs and Smart Meters

TL;DR

This paper introduces a probabilistic modeling approach to enhance low-resolution smart meter data with high-resolution load information from micro-PMUs, improving distribution system monitoring.

Contribution

A novel statistical method combining Gaussian Processes and Markov chains to recover high-resolution load data from low-resolution measurements using trained models.

Findings

Effective high-resolution load recovery demonstrated on real data

Enhanced distribution system observability achieved

Significant improvement in load volatility representation

Abstract

In modern distribution systems, load uncertainty can be fully captured by micro-PMUs, which can record high-resolution data; however, in practice, micro-PMUs are installed at limited locations in distribution networks due to budgetary constraints. In contrast, smart meters are widely deployed but can only measure relatively low-resolution energy consumption, which cannot sufficiently reflect the actual instantaneous load volatility within each sampling interval. In this paper, we have proposed a novel approach for enriching load data for service transformers that only have low-resolution smart meters. The key to our approach is to statistically recover the high-resolution load data, which is masked by the low-resolution data, using trained probabilistic models of service transformers that have both high and low-resolution data sources, i.e, micro-PMUs and smart meters. The overall framework consists of two steps: first, for the transformers with micro-PMUs, a Gaussian Process is leveraged to capture the relationship between the maximum/minimum load and average load within each low-resolution sampling interval of smart meters; a Markov chain model is employed to characterize the transition probability of known high-resolution load. Next, the trained models are used as teachers for the transformers with only smart meters to decompose known low-resolution load data into targeted high-resolution load data. The enriched data can recover instantaneous load uncertainty and significantly enhance distribution system observability and situational awareness. We have verified the proposed approach using real high- and low-resolution load data.