A fault reconfiguration strategy based on logical structure and improved reinforcement learning for ship DC regional grid

Abstract

Ship DC Regional Grid (SDRG) is an advanced power system that divides the power system into different regions, which can improve energy efficiency and stability. However, little research has been done on its fault reconfiguration. In the event of a fault, the switch is controlled by the fault reconfiguration technique to reconfigure the direction of the flow, which effectively improves the reliability of the power supply to the loads. A two-stage fault reconfiguration method based on Logical Structure (LS) and Improved Reinforcement Learning (IRL) is proposed, aiming to maximize the power load and minimize the number of switches. First, LS is utilized to design the load connectivity matrix based on the grid characteristics, and the grid topology is adjusted to achieve fast reconfiguration based on load prioritization. Further, IRL is employed to improve the reconfiguration speed and quality by establishing the optimal solution experience pool and improving the reward and punishment mechanism. The proposed method enables the selection of an appropriate reconfiguration strategy based on fault size, consequently improving convergence time and flexibility. Comparative experimental results in a four-region SDRG show that the proposed method can effectively solve the SDRG fault reconfiguration problem.