Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systems
It addresses monitoring and optimization challenges in electric power systems, representing an incremental advancement by applying existing deep learning methods to this domain.
This PhD thesis applied graph neural networks to improve power system state estimation and used reinforcement learning for dynamic distribution network reconfiguration, with effectiveness confirmed through experiments and simulations.
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.