Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systems

TL;DR

This thesis explores deep learning techniques, including graph neural networks and reinforcement learning, to improve monitoring and optimization in electric power systems, validated through extensive experiments.

Contribution

It introduces novel applications of graph neural networks and reinforcement learning for power system state estimation and network reconfiguration.

Findings

Enhanced accuracy in power system state estimation

Effective dynamic reconfiguration of distribution networks

Validated improvements through simulations

Abstract

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.