论文标题
实时电源系统事件检测：一种新颖的实例选择方法
Real-Time Power System Event Detection: A Novel Instance Selection Approach
论文作者
论文摘要
实例选择是能源大数据分析的重要技术。通过智能监控设备处理以高速速率生成的大量流数据是具有挑战性的。实例选择旨在删除可能损害数据驱动学习者的表现的嘈杂和不良数据。在这种情况下，本文提出了一种基于相似性的实例选择（SIS）方法，用于实时相量测量单元数据。此外，我们开发了Hoeffding-tree学习者的变体，并通过SIS增强了干扰和网络攻击的SIS。我们通过在影响系统物理或监视体系结构的四种情况下探索其表现，来验证拟议的学习者的优点。我们的实验通过使用工业控制系统网络攻击的数据集进行模拟。最后，我们进行了实施分析，该分析显示了拟议学习者的部署可行性和高性能潜力，这是实时监控应用程序的一部分。
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.
