Electrical power systems are increasing in size, complexity, as well as dynamics due to the growing integration of renewable energy resources, which have sporadic power generation. This necessitates the development of near real-time power system algorithms, demanding lower computational complexity regarding the power system size. Considering the growing trend in the collection of historical measurement data and recent advances in the rapidly developing deep learning field, the main goal of this paper is to provide a review of recent deep learning-based power system monitoring and optimization algorithms. Electrical utilities can benefit from this review by re-implementing or enhancing the algorithms traditionally used in energy management systems (EMS) and distribution management systems (DMS).

翻译：电力系统正随着可再生能源资源（其发电具有间歇性）日益集成而不断扩大规模、增加复杂性及动态性。这要求开发近实时的电力系统算法，以降低与系统规模相关的计算复杂度。鉴于历史测量数据收集的持续增长趋势，以及快速发展的深度学习领域的最新进展，本文的主要目标是综述近年来基于深度学习的电力系统监测与优化算法。电力公用事业可通过重新实现或增强能源管理系统（EMS）与配电管理系统（DMS）中传统使用的算法，从本综述中获益。