Instance selection is a vital technique for energy big data analytics. It is challenging to process a massive amount of streaming data generated at high speed rates by intelligent monitoring devices. Instance selection aims at removing noisy and bad data that can compromise the performance of data-driven learners. In this context, this paper proposes a novel similarity based instance selection (SIS) method for real-time phasor measurement unit data. In addition, we develop a variant of the Hoeffding-Tree learner enhanced with the SIS for classifying disturbances and cyber-attacks. We validate the merits of the proposed learner by exploring its performance under four scenarios that affect either the system physics or the monitoring architecture. Our experiments are simulated by using the datasets of industrial control system cyber-attacks. Finally, we conduct an implementation analysis which shows the deployment feasibility and high-performance potential of the proposed learner, as a part of real-time monitoring applications.

中文翻译：

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实时电力系统事件检测：一种新颖的实例选择方法

实例选择是能源大数据分析的重要技术。处理智能监控设备高速生成的海量流数据具有挑战性。实例选择旨在消除可能影响数据驱动学习器性能的嘈杂和不良数据。在这种情况下，本文提出了一种新颖的基于相似度的实例选择（SIS）方法，用于实时相量测量单元数据。此外，我们开发了一种用 SIS 增强的 Hoeffding-Tree 学习器的变体，用于对干扰和网络攻击进行分类。我们通过探索其在影响系统物理或监控架构的四种场景下的性能来验证所提出的学习器的优点。我们的实验是通过使用工业控制系统网络攻击的数据集来模拟的。最后，