Removing the redundant features from massive data collected from power systems is of paramount importance in improving the efficiency of data-driven diagnostic systems. This work proposes a novel concrete feature selection based on mutual information, called CFMI, for selecting proper features to enhance diagnosing faults and cyber-attacks in power systems. The proposed technique is then compared with various state-of-the-art techniques and a comprehensive study has been performed on the selected features. All techniques are evaluated with respect to simulated scenarios on IEEE 39-bus system and a Three-Bus Two-Line experimental setup. The attained results, on one hand, verify the superiority of the proposed CFMI technique over other techniques. On the other hand, the selected features from both setups denote that current and voltage features are more informative than other features for diagnostic systems. Furthermore, the results of the comprehensive study shows that those features collected from generation buses are of higher priority for diagnostic systems.

从电力系统收集的海量数据中删除冗余功能对于提高数据驱动诊断系统的效率至关重要。这项工作提出了一种基于互信息的新颖具体特征选择，称为CFMI，用于选择适当的特征以增强对电力系统中的故障和网络攻击的诊断。然后，将所提出的技术与各种最新技术进行比较，并对所选功能进行了全面研究。所有技术均针对IEEE 39总线系统和三总线两线实验装置上的模拟场景进行了评估。一方面，所获得的结果证明了所提出的CFMI技术优于其他技术的优越性。另一方面，从两个设置中选择的功能表示电流和电压功能比诊断系统的其他功能提供更多信息。此外，全面研究的结果表明，从发电总线收集的那些功能对于诊断系统具有更高的优先级。