The security assessment, based on which determinant decisions should be made for power system design, control and operation, is a challenging issue for utility engineers and network designers, especially in large‐scale power systems. Numerous methods have been proposed and implemented for this purpose, and a variety of indices have been suggested to address the static security condition of power networks. Large‐scale datasets of measurements in continually expanding power systems necessitate advanced knowledge in big data analytics. In this review paper, numerical techniques and machine learning‐based methods are reviewed as two main categories for static security assessment in power systems based on principal features of static security status classification such as type of classifier, the static security index, and feature selection and extraction methods. This paper can be used as a useful reference for static security assessment of power systems.

中文翻译：

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电力系统静态安全评估：回顾

对于公用事业工程师和网络设计人员来说，尤其是在大型电力系统中，安全评估是决定电力系统设计，控制和运行的决定性决策的基础，这是一个充满挑战的问题。为此已经提出并实施了许多方法，并且已经提出了多种指标来解决电力网络的静态安全状况。不断扩展的电力系统中的大规模测量数据集需要大数据分析方面的高级知识。在这篇综述文件中，基于静态安全状态分类的主要特征（例如分类器的类型，静态安全性指标，以及特征选择和提取方法。本文可作为电力系统静态安全评估的有用参考。