Intelligent decision-making of start-up and shutdown for coal milling system in thermal power plants based on deep reinforcement learning
A comprehensive evaluation model for the start-up and shutdown decision-making of the milling system,taking into account the energy consumption and tracking performance of the unit load,has been proposed to address issues such as subjective decision-making based on manual experience,high labor intensity in operation,and difficulty in exploring energy-saving optimization potential.This model safely incorporates the grid load scheduling command signal as input.Furthermore,a milling system start-stop intelligent decision-making method based on deep reinforcement learning has been studied,and a closed-loop control system for the automatic start-stop of the milling system has been developed.The research results have been verified through simulation and successfully applied to a commonly used coal milling system in a certain ultra-supercritical 1 000 MW unit,achieving energy savings.The findings of this study can provide effective reference for the development of unmanned or minimally manned operation techniques for thermal power units.
deep reinforcement learningmilling systemautonomous start-up and shutdown controlintelligent decision-making
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