Decentralized Dynamic State Estimation in Microgrids

TL;DR

This paper introduces a decentralized Kalman filter-based method for real-time state estimation in microgrids, improving computational efficiency and accuracy through measurement decoupling and covariance adjustments.

Contribution

It presents a novel decentralized dynamic state estimation scheme using Kalman filtering with measurement decoupling for microgrids.

Findings

Effective state estimation demonstrated through simulations

Reduced computational complexity achieved

Enhanced estimation accuracy with covariance adjustments

Abstract

This paper proposes a decentralized dynamic state estimation scheme for microgrids. The approach employs the voltage and current measurements in the dq0 reference frame through phasor synchronization to be able to exclude orthogonal functions from their relationship formulas. Based on that premise, we utilize a Kalman filter to dynamically estimate states of microgrids. The decoupling of measurement values to state and input vectors reduces the computational complexity. The Kalman filter considers the process noise covariances, which are modified with respect to the covariance of measured input values. Theoretical analysis and simulation results are provided for validation.