The paper introduces a robust method to detect random errors and cyber-attacks targeting alternating current (AC) dynamic state estimation through false data injection. The dynamic state estimator uses measurements that are delivered at a high reporting rate from synchrophasors or phasor measurement units (PMUs). A statistical outlier detection algorithm using the S-estimator is implemented on successive batch-mode regression representations of the extended Kalman filter (EKF). The S-estimator is a robust regression estimator that resists the effect of outliers and bad measurements. Theoretically, an S-estimator offers an improved detection of outliers compared with other methods implemented in the smart grid literature such as the Huber M-estimator and the Generalized Huber M-estimator (GM-estimator). The S-estimator has the advantage of offering a high breakdown point and can resist both observation outliers and leverage points. The introduced S-based EKF accurately tracks the fast variations of states when the PMU data is either clean or contaminated by bad measurements and cyber attacks. The proposed filter also accurately tracks the states when errors and cyber attacks target the topology of the grid. The simulations are conducted on the IEEE 14-bus system.

中文翻译：

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基于动态 PMU 的功率状态估计中异常值和网络攻击的诊断

本文介绍了一种鲁棒的方法，通过虚假数据注入来检测针对交流 (AC) 动态状态估计的随机错误和网络攻击。动态状态估计器使用同步相量或相量测量单元 (PMU) 以高报告率提供的测量值。在扩展卡尔曼滤波器 (EKF) 的连续批处理模式回归表示上实现了使用 S 估计器的统计异常值检测算法。S 估计器是一种稳健的回归估计器，可以抵抗异常值和不良测量的影响。从理论上讲，与智能电网文献中实现的其他方法（例如 Huber M-estimator 和 Generalized Huber M-estimator (GM-estimator)）相比，S-estimator 提供了改进的异常值检测。S-estimator 具有提供高击穿点的优点，并且可以抵抗观察异常值和杠杆点。当 PMU 数据干净或受到不良测量和网络攻击时，引入的基于 S 的 EKF 可以准确跟踪状态的快速变化。当错误和网络攻击以电网拓扑为目标时，所提出的过滤器还可以准确地跟踪状态。仿真是在 IEEE 14 总线系统上进行的。