基于物理信息机器学习算法的电厂空冷岛运行策略优化研究OA

The air-cooled island(ACI)is a major power-consuming facility in thermal power plants.To reduce the coal consumption rate of power generation,further optimization of ACI operation strategies is essential.Due to the complex and variable operating conditions of ACIs,purely data-driven AI algorithms exhibit insuffi-cient generalization capabilities.Taking a thermal power plant in Shanxi for example,this study proposes a da-ta-physics co-driven optimization algorithm centered on the physics-informed neural network(PINN)frame-work.The algorithm employs the ACI mechanistic model as a skeleton model,integrating big data-driven deep learning to establish a ACI mechanistic-AI fusion model.With economic optimization as the goal,an intelligent ACI operation system is developed based on the turbine's optimal vacuum model and the ACI mechanistic-AI fusion model.This system intelligently optimizes fan rotational speed or current to maintain optimal vacuum conditions under specified constraints.Results from typical daily analyses show that the average coal consump-tion savings in spring,summer,and autumn reach approximately 1.0 g/(kW·h).Coal savings increase as am-bient temperature decreases,peaking at 3.66 g/(kW·h)with significant energy-saving effects.Physics-in-formed machine learning algorithms demonstrate strong generalization capabilities in optimizing ACI operation strategies,achieving substantial coal-saving benefits.