基于人工智能技术,提出一种电缆温度远程自动化控制的系统方案.该方案综合利用深度学习、强化学习等算法,实现了电缆温度的实时预测、自适应优化控制和节能运行,并通过仿真实验验证了其有效性.实验结果表明,与传统比例-积分-微分(Proportional Integral Derivative,PID)控制相比,该方案在控制精度、响应速度和健壮性方面具有显著优势,可为智能电网的安全高效运行提供新的解决思路.
Research on Remote Automation Control System of Cable Temperature Based on Artificial Intelligence
Proposes a system scheme for remote automation control of cable temperature based on artificial intelligence technology.This scheme comprehensively utilizes deep learning,reinforcement learning and other algorithms to achieve real-time prediction of cable temperature,adaptive optimization control,and energy-saving operation,and its effectiveness has been verified through simulation experiments.The experimental results show that compared with traditional Proportional Integral Derivative(PID)control,this scheme has significant advantages in control accuracy,response speed,and robustness,and can provide new solutions for the safe and efficient operation of smart grids.
cable temperature controlartificial intelligencedeep learning
NETL
NSTL
万方数据
