Multiple event analysis for large-scale power systems through cluster-based sparse coding

Abstract

Accurate event analysis in real time is of paramount importance for high-fidelity situational awareness such that proper actions can take place before any isolated faults escalate to cascading blackouts. For large-scale power systems, due to the large intra-class variance and inter-class similarity, the nonlinear nature of the system, and the large dynamic range of the event scale, multi-event analysis presents an intriguing problem. Existing approaches are limited to detecting only single or double events or a specified event type. Although some previous works can well distinguish multiple events in small-scale power systems, the performance tends to degrade dramatically in large-scale systems. In this paper, we focus on multiple event detection, recognition, and temporal localization in large-scale power systems. We discover that there always exist groups of buses whose reaction to each event shows high degree similarity, and the group membership generally remains the same regardless of the type of event(s). We further verify that this reaction to multiple events can be approximated as a linear combination of reactions to each constituent event. Based on these findings, we propose a novel method, referred to as cluster-based sparse coding (CSC), to extract all the underlying single events involved in a multi-event scenario. Experimental results based on simulated large-scale system model (i.e., NPCC) show that the proposed CSC algorithm presents high detection and recognition rate with low false alarms.