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Event Detection and Its Signal Characterization in PMU Data Stream

机译：PMU数据流中的事件检测及其信号表征

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摘要

The potential application of signal processing techniques is not only to detect the event but also to characterize them according to physical disturbance. In this paper, event detection and its characterization algorithm is presented. The event detection scheme uses computation of spectral kurtosis on sum of intrinsic mode functions. The algorithm is capable of detecting the event in phasor measurement units data by comparing the maximum energy and root-mean square of energy content of present analysis segment with respect to previous segment. The statistical indices applied are capable to flag specific data and thus the timely detection of events. Further, statistical features extracted from event-related segment suggest that the transient signals from different regions are distinct and thus can be classified. The signal characterization is further represented in terms of short-term energy and group delay. The analysis on event triggered signal demonstrates the related physical phenomenon in each event type. The study suggests the most relevant signal associated with a particular type of event.
机译：信号处理技术的潜在应用不仅是检测事件，还可以根据物理干扰来表征事件。本文提出了事件检测及其表征算法。事件检测方案使用固有模式函数之和计算频谱峰度。该算法能够通过比较当前分析段相对于先前段的最大能量和能量含量的均方根来检测相量测量单位数据中的事件。应用的统计指标能够标记特定数据，从而及时检测事件。此外，从事件相关段中提取的统计特征表明，来自不同区域的瞬态信号是不同的，因此可以进行分类。信号表征进一步用短期能量和群时延表示。对事件触发信号的分析表明了每种事件类型中的相关物理现象。该研究表明与特定事件类型相关的最相关信号。

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