A robust real-time low-frequency oscillation detection and analysis (LFODA) system with innovative ensemble filtering

Abstract

Low-frequency oscillations are hazardous to power system operation, and can lead to cascading failures if not detected and mitigated in a timely manner. This paper presents a robust and automated real-time monitoring system for detecting grid oscillations and analyzing their mode shapes using PMU measurements. A novel Extended Kalman Filtering (EKF) based approach is introduced to detect and analyze oscillations. To further improve the accuracy and efficiency of the presented software system, the EKF approach takes advantages of three effective signal processing methods (including Prony's Method, Hankel Total Least Square (HTLS) Method, EKF) and adopts a novel voting schema to significantly reduce the computation cost. Results from these methods are processed through a timeseries filter to ensure the consistency of detected oscillations and reduce the number of false alarms. The Density-based Spatial Clustering of Applications with Noise (DBSCAN) method is used to accurately classify oscillation modes and the PMU measurement channels. The LFODA system has been functioning well in the State Grid Jiangsu Electric Power Company with 176 PMUs and 1000+ channels since February 2018, demonstrating outstanding performance in reducing false alarms with much less computational cost.