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Hybrid data-driven physics model-based framework for enhanced cyber-physical smart grid security

机译：混合数据驱动物理模型的增强网络物理智能电网安全性的基于物理模型的框架

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摘要

This study presents a hybrid data-driven physics model-based framework for real-time monitoring in smart grids. As the power grid transitions to the use of smart grid technology, it's real-time monitoring becomes more vulnerable to cyber-attacks like false data injections (FDIs). Although smart grids cyber-physical security has an extensive scope, this study focuses on FDI attacks, which are modelled as bad data. State-of-the-art strategies for FDI detection in real-time monitoring rely on physics model-based weighted least-squares state estimation solution and statistical tests. This strategy is inherently vulnerable by the linear approximation and the companion statistical modelling error, which means it can be exploited by a coordinated FDI attack. In order to enhance the robustness of FDI detection, this study presents a framework which explores the use of data-driven anomaly detection methods in conjunction with physics model-based bad data detection via data fusion. Multiple anomaly detection methods working at both the system level and distributed local detection level are fused. The fusion takes into consideration the confidence of the various anomaly detection methods to provide the best overall detection results. Validation considers tests on the IEEE 118-bus system.
机译：本研究提出了一种混合数据驱动物理模型的基于物理模型，用于智能电网的实时监控。随着电网转换到使用智能电网技术，它的实时监控变得更容易受到网络攻击等虚假数据喷射（FDIS）的影响。虽然智能电网网络 - 物理安全性具有广泛的范围，但这项研究侧重于FDI攻击，这些攻击是如糟糕的数据建模。实时监控中的FDI检测最先进的策略依赖于基于物理模型的加权最小二乘状态估计解决方案和统计测试。这种策略本质上易受线性近似和伴侣统计建模错误，这意味着它可以通过协调的外国直接投资攻击来利用。为了提高FDI检测的稳健性，本研究提出了一个框架，探讨了通过数据融合的基于物理模型的不良数据检测使用数据驱动的异常检测方法的框架。在系统级和分布式局部检测级别工作的多种异常检测方法融合。融合考虑了各种异常检测方法的置信度，提供了最佳的整体检测结果。验证考虑了IEEE 118-Bus系统的测试。

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