This PhD thesis thoroughly examines the utilization of deep learning techniques as a means to advance the algorithms employed in the monitoring and optimization of electric power systems. The first major contribution of this thesis involves the application of graph neural networks to enhance power system state estimation. The second key aspect of this thesis focuses on utilizing reinforcement learning for dynamic distribution network reconfiguration. The effectiveness of the proposed methods is affirmed through extensive experimentation and simulations.

翻译：本博士论文系统性地探讨了将深度学习技术应用于电力系统监测与优化算法的方法。论文首个核心贡献在于采用图神经网络提升电力系统状态估计性能。第二个关键研究方向聚焦于运用强化学习实现配电网动态重构。通过大量实验与仿真验证了所提方法的有效性。