State Estimation in Power Distribution Systems Based on Ensemble Kalman Filtering

TL;DR

This paper introduces a dynamic ensemble Kalman filter-based method for distribution system state estimation that improves accuracy with fewer measurements and does not rely on embedded power flow equations.

Contribution

It proposes a novel Past-Aware State Estimation (PASE) method using Ensemble Kalman Filter that enhances accuracy and reduces measurement requirements in distribution networks.

Findings

Fewer PMUs needed for target accuracy compared to snapshot methods

The method does not embed power flow equations into the estimator

Validated on 33-bus system with real household data

Abstract

State estimation in power distribution systems is a key component for increased reliability and optimal system performance. Well understood in transmission systems, state estimation is now an area of active research in distribution networks. While several snapshot-based approaches have been used to solve this problem, few solutions have been proposed in a dynamic framework. In this paper, a Past-Aware State Estimation (PASE) method is proposed for distribution systems that takes previous estimates into account to improve the accuracy of the current one, using an Ensemble Kalman Filter. Fewer phasor measurements units (PMU) are needed to achieve the same estimation error target than snapshot-based methods. Contrary to current methods, the proposed solution does not embed power flow equations into the estimator. A theoretical formulation is presented to compute a priori the advantages of the proposed method vis-a-vis the state-of-the-art. The proposed approach is validated considering the 33-bus distribution system and using power consumption traces from real households.