一种用于电力系统网络安全攻击的突变检测启发法
An Abrupt Change Detection Heuristic with Applications to Cyber Data Attacks on Power Systems
AI总结：
本文提出基于SVD的历史矩阵分析方法，用于检测系统突变，并应用于电力系统中的恶意网络攻击检测。
摘要：
我们提出了一种用于检测具有有界状态变化系统的突变检测启发法的分析方法。该分析基于由系统观测构建的历史矩阵的奇异值分解（SVD）。我们证明监控历史矩阵的最大奇异值可以作为检测系统输出突变的启发法。我们为该启发法提供了充分的可检测性条件。作为应用，我们考虑检测电力系统中的恶意网络攻击，并在IEEE 39节点测试平台上测试所提出的启发法。
英文摘要：
We present an analysis of a heuristic for abrupt change detection of systems with bounded state variations. The proposed analysis is based on the Singular Value Decomposition (SVD) of a history matrix built from system observations. We show that monitoring the largest singular value of the history matrix can be used as a heuristic for detecting abrupt changes in the system outputs. We provide sufficient detectability conditions for the proposed heuristic. As an application, we consider detecting malicious cyber data attacks on power systems and test our proposed heuristic on the IEEE 39-bus testbed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
