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- Title
Spacecraft power-signal composite network optimization algorithm based on DRL.
- Authors
ZHANG Tingyu; ZENG Ying; LI Nan; HUANG Hongzhong
- Abstract
To maximize the utilization of limited energy and achieve flexible and efficient grid connection for spacecraft power supply systems' a composite grid topology optimization model for power transmission and signal communication is proposed based on deep reinforcement learning (DRL). Various interpretable component models are employed based on knowledge distillation principles to analyze the optimization mechanism. Firstly' the transformation law of the control domain of the spacecraft bus voltage regulation in the on-orbit operation stage is analyzed' and the composite network topology model of power transmission and signal communication is established by combining the node propagation parameters. Secondly asynchronous advantage actor-critic (A3C) is utilized to adaptively optimize potential operational reliability risks in routing distribution and topology of the electrical signal transmission network. Finally various interpretable components are used to perform knowledge destination on the trained DRL model forming an interpretable quantitative analysis method. The proposed method theoretically predicts optimal grid-connected processes of space power supply under random shadow effects, providing theoretical support and reference for designing space power supply controllers under higher task requirements and complex environments.
- Subjects
DEEP reinforcement learning; REINFORCEMENT learning; OPTIMIZATION algorithms; POWER resources; ENERGY consumption
- Publication
Systems Engineering & Electronics, 2024, Vol 46, Issue 9, p3060
- ISSN
1001-506X
- Publication type
Article
- DOI
10.12305/j.issn.1001-506X.2024.09.18