Prospect of Offshore Floating Photovoltaic wind_tech D1

发布：2026-06-09 · 事件：2026-06-09

机器学习算法在压缩空气储能领域的研究进展 袁照威 , 万树 , 陈永安 , 朱治德 , 杨言 Advances in Machine Learning for Compressed Air Energy Storage Systems: A Review YUAN Zhaowei , WAN Shu , CHEN Yongan , ZHU Zhide , YANG Yan Article Text...

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机器学习算法在压缩空气储能领域的研究进展 袁照威 , 万树 , 陈永安 , 朱治德 , 杨言 Advances in Machine Learning for Compressed Air Energy Storage Systems: A Review YUAN Zhaowei , WAN Shu , CHEN Yongan , ZHU Zhide , YANG Yan Article Text (iFLYTEK Translation) × Close Disclaimer The English version of this article is automatically generated by iFLYTEK Translation and only for reference. We therefore are not responsible for its reasonableness, correctness and completeness, and will not bear any commercial and legal responsibilities for the relevant consequences arising from the English translation. Do not remind me again Cancel Confirm 摘要 HTML全文 图 (14) 表 (1) 参考文献 (36) 相关文章 施引文献 资源附件 (0) 摘要 摘要: 目的 机器学习凭借其强大的数据处理、非线性拟合及自适应优化能力，为压缩空气储能的高效运行与智能化发展提供重要支撑。 方法 文章综述了机器学习算法在压缩空气储能领域的进展，系统梳理了其在各个应用方向的技术路径和实践成果。首先，简述了压缩空气储能电站系统流程、核心设备及机器学习算法的工作流程。然后详细探讨了机器学习在系统建模与性能预测、状态监测与故障诊断、智能控制与优化运行、地下储气库应用等方面的应用案例。 结果 机器学习与压缩空气储能的交叉融合，已实现压缩机、膨胀机、换热器等关键设备的性能预测，并在设备状态诊断、系统参数优化等方面取得突破，显著提升了系统运行的智能化水平。 结论 未来随着压缩空气储能商业化运行电站增加及机器学习算法的持续迭代，机器学习将进一步推动压缩空气储能电站向具有自主感知、自适应调节、智能决策能力的智能电厂演进，为新型电力系统的灵活运行提供核心支撑。 Abstract: Objective Machine learning algorithms, with their powerful capabilities in data processing, nonlinear fitting, and adaptive optimization, provide crucial support for the efficient operation and intelligent evolution of compressed air energy storage (CAES). Method This paper reviews the advances of machine learning in the field of CAES, systematically summarizing the technical approaches and practical achievements across various application domains. It begins by briefly outlining the system architecture and core equipment of CAES plants, as well as the typical workflow of machine learning algorithms. The review then delves into specific applications of machine learning, including system modeling and performance prediction, condition monitoring and fault diagnosis, intelligent control and operational optimization, and its use in underground cavern management. Result The integration of machine learning with CAES has successfully enabled performance prediction for key equipment such as compressors, expanders, and heat exchangers. Significant breakthroughs have also been achieved in equipment condition diagnosis and system parameter optimization, markedly enhancing the operational intelligence of CAES systems. Conclusion In the future, with the growing number of commercial CAES plants and the continuous iteration of machine learning algorithms, this synergy will further drive the evolution of CAES facilities into intelligent power plants featuring autonomous perception, adaptive regulation, and smart decision-making. This advancement will provide core support for the flexible operation of future power systems. HTML全文 参考文献 (36) 相关文章 施引文献 资源附件 (0)