Spacecraft power-signal composite network optimization algorithm based on DRL
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 distillation 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.
space power systemcomplex network theorydeep reinforcement learning(DRL)reliability optimizationinterpretable analysis
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