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基于深度学习的汽机热力系统运行优化技术

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随着能源需求增长,提高火力发电效率成为亟需解决的问题.文章提出了一种基于深度学习的汽机热力系统运行优化技术,通过智能数据采集与预处理、LSTM性能预测模型和智能优化控制策略,实现系统运行效率的提升.在国家能源集团科学技术研究院有限公司太原分公司的300 MW亚临界机组上进行的为期6个月的对比实验表明,该技术相比传统方法在发电煤耗、热效率、运行稳定性等方面均有显著改善.研究成果为火电行业的节能减排提供了新的技术路径,对推动能源技术创新和工业领域低碳高效发展具有重要意义.
Optimization Technology for Steam Turbine Thermal System Operation Based on Deep Learning
With the increasing demand for energy,improving the efficiency of thermal power generation has become an urgent problem to be solved.The article proposes a deep learning based optimization technique for the operation of steam turbine thermal systems,which improves system efficiency through intelligent data acquisition and preprocessing,LSTM performance prediction models,and intelligent optimization control strategies.A 6-month comparative experiment conducted on a 300 MW subcritical unit at Taiyuan Branch of National Energy Group Science and Technology Research Institute Co.,Ltd.showed that this technology significantly improved coal consumption,thermal efficiency,and operational stability compared to traditional methods.The research results provide a new technological path for energy conservation and emission reduction in the thermal power industry,which is of great significance for promoting energy technology innovation and low-carbon and efficient development in the industrial sector.

deep learningsteam turbine thermal systemrun optimization

张伟

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国家能源集团科学技术研究院有限公司太原分公司,山西太原 030006

深度学习 汽机热力系统 运行优化

2024

电力系统装备
《机电商报》社

电力系统装备

影响因子:0.008
ISSN:1671-8992
年,卷(期):2024.(12)