# Sequential Discrete Kalman Filter for Real-Time State Estimation in Power Distribution Systems: Theory and Implementation

## TL;DR

This paper presents a real-time state estimation prototype for power distribution systems using a Sequential Discrete Kalman Filter implemented on FPGA, validated with a standard test feeder, demonstrating feasibility and efficiency.

## Contribution

It introduces an FPGA-implementable SDKF for real-time power system state estimation, proving its equivalence to DKF and analyzing its computational advantages.

## Key findings

- Prototype successfully validates real-time state estimation on FPGA.
- SDKF is computationally more efficient than DKF for uncorrelated noise.
- The approach is validated on IEEE 34-node distribution test feeder.

## 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.