.png){kind=icon}
返回
Multiple Event Detection and Recognition for Large-Scale Power Systems Through Cluster-Based Sparse Coding
.png){kind=icon}
PRE
AI
DOI：10.1109/TPWRS.2017.2656146.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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. Existing approaches are limited to detect only single or double events or a specified event type. Although some previous works can well distinguish multiple events in small-scale 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 regions where the reaction of all buses to certain event within each region demonstrates high degree similarity, and that the boundary of the regions generally remains the same regardless of the type of event(s). We further verify that, within each region, 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 multievent scenario. Multiple events of three typical disturbances (e.g., generator trip, line trip, and load shedding) can be detected and recognized. Specifically, the CSC algorithm can effectively distinguish line trip events from oscillation, which has been a very challenging task for event analysis. 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.
Keyword:
Classification-based method
cluster-based sparse coding
frequency measurement
large-scale power systems
multievent analysis
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。