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Real-Time Multiple Event Detection and Classification in Power System Using Signal Energy Transformations

机译:利用信号能量转换的电力系统实时多事件检测与分类

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摘要

Real-time multiple event analysis is important for reliable situational awareness and secure operation of the power system. Multiple sequential events can induce complex superimposed pattern in the data and are challenging to analyze in real time. This paper proposes a method for accurate detection, temporal localization, and classification of multiple events in real time using synchrophasor data. For detection and temporal localization, a Teager-Kaiser energy operator (TKEO) based method is proposed. For event classification, a time series classification based method using energy similarity measure (ESM) is proposed. The proposed method is tested for simulated multiple event cases in the IEEE-118 bus system using DigSilent/PowerFactory and real PMU data for the Indian grid.
机译:实时多事件分析对于可靠的态势感知和电力系统的安全运行非常重要。多个顺序事件可能会在数据中引起复杂的叠加模式,并且难以实时分析。本文提出了一种使用同步相量数据实时准确检测,时间定位和对多个事件进行分类的方法。为了进行检测和时间定位,提出了一种基于Teager-Kaiser能量算子(TKEO)的方法。对于事件分类,提出了一种基于时间序列分类的能量相似度度量(ESM)方法。使用DigSilent / PowerFactory和印度电网的实际PMU数据,对所提出的方法在IEEE-118总线系统中模拟的多事件情况进行了测试。

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