A power regulation strategy for heat pipe cooled reactors based on deep learning and hybrid data-driven optimization algorithm

功率(物理) 过程(计算) 工程类 人工神经网络 计算机科学 控制工程 人工智能 量子力学 操作系统 物理
作者
Mengqi Huang,Changhong Peng,Zhengyu Du,Yu Liu
出处
期刊:Energy [Elsevier BV]
卷期号:289: 130050-130050 被引量:3
标识
DOI:10.1016/j.energy.2023.130050
摘要

Heat pipe cooled reactors are ideal for use in remote or isolated locations as dependable, small-scale power sources, thanks to their excellent design characteristics. To tackle real-time changes in power demand within a dynamic environment, this research proposes a decision-making strategy for regulating the power of heat pipe cooled reactors. The strategy is founded on a hybrid data-driven optimization algorithm and deep learning, enabling the attainment of safe and efficient control of heat pipe cooled reactors under specified power requirements. Initially, a power forecast model founded on artificial neural networks for heat pipe cooled reactors is established. Then, an appraisal standard for power regulation arrangements, combining reactor safety and operational effectiveness, is developed based on the utility theory. Finally, this study introduces a hybrid data-driven optimization algorithm that efficiently identifies the power regulation scheme with the greatest utility for given power demands. The proposed technique's effectiveness was demonstrated by selecting the power regulation process of the MegaPower heat pipe cooled reactor as an example. The results indicate that the strategy can make steady, accurate, and near-optimal power regulation decisions for any power demand within 20 s.
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