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首页> 外文期刊>Industrial Informatics, IEEE Transactions on >Classification of Disturbances and Cyber-Attacks in Power Systems Using Heterogeneous Time-Synchronized Data
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Classification of Disturbances and Cyber-Attacks in Power Systems Using Heterogeneous Time-Synchronized Data

机译：使用异构时间同步数据对电力系统中的干扰和网络攻击进行分类

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摘要

Visualization and situational awareness are of vital importance for power systems, as the earlier a power-system event such as a transmission line fault or cyber-attack is identified, the quicker operators can react to avoid unnecessary loss. Accurate time-synchronized data, such as system measurements and device status, provide benefits for system state monitoring. However, the time-domain analysis of such heterogeneous data to extract patterns is difficult due to the existence of transient phenomena in the analyzed measurement waveforms. This paper proposes a sequential pattern mining approach to accurately extract patterns of power-system disturbances and cyber-attacks from heterogeneous time-synchronized data, including synchrophasor measurements, relay logs, and network event monitor logs. The term common path is introduced. A common path is a sequence of critical system states in temporal order that represent individual types of disturbances and cyber-attacks. Common paths are unique signatures for each observed event type. They can be compared to observed system states for classification. In this paper, the process of automatically discovering common paths from labeled data logs is introduced. An included case study uses the common path-mining algorithm to learn common paths from a fusion of heterogeneous synchrophasor data and system logs for three types of disturbances (in terms of faults) and three types of cyber-attacks, which are similar to or mimic faults. The case study demonstrates the algorithm’s effectiveness at identifying unique paths for each type of event and the accompanying classifier’s ability to accurately discern each type of event.
机译：可视化和态势感知对于电力系统至关重要，因为尽早识别出电力系统事件（如传输线故障或网络攻击），操作员可以更快地做出反应以避免不必要的损失。准确的时间同步数据（例如系统测量和设备状态）为系统状态监视提供了好处。但是，由于所分析的测量波形中存在瞬态现象，因此难以对此类异构数据进行时域分析以提取图案。本文提出了一种顺序模式挖掘方法，可以从异构时间同步数据（包括同步相量测量，中继日志和网络事件监视器日志）中准确提取电力系统扰动和网络攻击的模式。引入了通用路径一词。一条通用路径是按时间顺序排列的一系列关键系统状态，这些状态代表各种类型的干扰和网络攻击。公用路径是每种观察到的事件类型的唯一签名。可以将它们与观察到的系统状态进行比较以进行分类。本文介绍了从标记的数据日志中自动发现公用路径的过程。包含的案例研究使用通用路径挖掘算法从异构同步相量数据和系统日志的融合中学习通用路径，以了解三种类型的干扰（就故障而言）和三种类型的网络攻击，这些相似或模仿故障。案例研究证明了该算法在识别每种事件的唯一路径方面的有效性，以及伴随的分类器准确识别每种事件的能力。

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