Distributed Estimation of Oscillations in Power Systems: an Extended Kalman Filtering Approach

TL;DR

This paper presents a distributed extended Kalman filtering method for real-time estimation of oscillation parameters in power systems, enhancing stability monitoring while addressing communication and privacy issues.

Contribution

It introduces a fully distributed diffusion extended Kalman filter for oscillation estimation, improving scalability and privacy compared to centralized approaches.

Findings

Effective in simulated environments

Validated with real-world data from Jiangsu Electric Power Company

Enhances system stability monitoring

Abstract

Online estimation of electromechanical oscillation parameters provides essential information to prevent system instability and blackout and helps to identify event categories and locations. We formulate the problem as a state space model and employ the extended Kalman filter to estimate oscillation frequencies and damping factors directly based on data from phasor measurement units. Due to considerations of communication burdens and privacy concerns, a fully distributed algorithm is proposed using diffusion extended Kalman filter. The effectiveness of proposed algorithms is confirmed by both simulated and real data collected during events in State Grid Jiangsu Electric Power Company.