Learning-based State Estimation in Distribution Systems with Limited Real-Time Measurements

TL;DR

This paper introduces a learning-based method for state estimation in distribution systems with limited real-time measurements, leveraging joint probability distributions to improve accuracy over existing pseudo-measurement approaches.

Contribution

It presents a novel methodology that uses unobservable state estimator outputs to exploit joint probability distributions, enhancing state estimation accuracy in distribution systems.

Findings

Outperforms existing state forecasting methods

Effective with limited real-time measurements

Demonstrated on a realistic distribution grid

Abstract

The task of state estimation in active distribution systems faces a major challenge due to the integration of different measurements with multiple reporting rates. As a result, distribution systems are essentially unobservable in real time, indicating the existence of multiple states that result in identical values for the available measurements. Certain existing approaches utilize historical data to infer the relationship between real-time available measurements and the state. Other learning-based methods aim to estimate the measurements acquired with a delay, generating pseudo-measurements. Our paper presents a methodology that utilizes the outcome of an unobservable state estimator to exploit information on the joint probability distribution between real-time available measurements and delayed ones. Through numerical simulations conducted on a realistic distribution grid with insufficient real-time measurements, the proposed procedure showcases superior performance compared to existing state forecasting approaches and those relying on inferred pseudo-measurements.