Sequential Discrete Kalman Filter for Real-Time State Estimation in Power Distribution Systems: Theory and Implementation
Abstract
This paper demonstrates the feasibility of implementing Real-Time State Estimators (RTSEs) for Active Distribution Networks (ADNs) in Field-Programmable Gate Arrays (FPGAs) by presenting an operational prototype. The prototype is based on a Linear State Estimator (LSE) that uses synchrophasor measurements from Phasor Measurement Units (PMUs). The underlying algorithm is the Sequential Discrete Kalman Filter (SDKF), an equivalent formulation of the Discrete Kalman Filter (DKF) for the case of uncorrelated measurement noise. In this regard, this work formally proves the equivalence the SDKF and the DKF, and highlights the suitability of the SDKF for an FPGA implementation by means of a computational complexity analysis. The developed prototype is validated using a case study adapted from the IEEE 34-node distribution test feeder.
Keywords
Cite
@article{arxiv.1702.08262,
title = {Sequential Discrete Kalman Filter for Real-Time State Estimation in Power Distribution Systems: Theory and Implementation},
author = {Andreas Martin Kettner and Mario Paolone},
journal= {arXiv preprint arXiv:1702.08262},
year = {2017}
}
Comments
Index Terms: Active Distribution Network (ADN), Real-Time State Estimator (RTSE), Phasor Measurement Unit (PMU), Sequential Discrete Kalman Filter (SDKF), Field-Programmable Gate Array (FPGA)