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).

本论文综述了近期基于深度学习算法的电力系统监测和优化算法，以应对由于可再生能源资源集成而引起的电力系统的规模、复杂性和动力性的增加，以及历史测量数据收集的趋势和快速发展的深度学习领域的最新进展。