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《工程(英文)》 >> 2021年 第7卷 第9期 doi: 10.1016/j.eng.2021.04.020

机器学习和数据驱动算法在智慧发电系统中的应用——一种不确定性处理的视角

a Key Lab of Thermal Science and Power Engineering of the Ministry of Education, School of Energy and the Environment, Southeast University, Nanjing 210096, China
b Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA

收稿日期: 2020-08-19 修回日期: 2020-10-15 录用日期: 2021-04-02 发布日期: 2021-07-13

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摘要

由于人们对气候变化和环境保护的日益关注,智慧发电已成为常规火力发电厂和可再生能源系统经济安全运行的关键。面对日益增长的系统规模及其各种不确定性,传统的基于模型的第一定律方法已难以满足系统控制的要求。机器学习(ML)和数据驱动控制(DDC)技术的蓬勃发展为这些传统方法提供了一种替代方案。本文回顾了机器学习和数据驱动控制技术在发电系统监测、控制、优化和故障检测方面的典型应用,特别着重于揭示这些方法在评价、消除或耐受相关不确定性影响方面的作用。本文为智慧发电控制技术提供了一个从调节层到规划层的总体视角,分别从可见性、机动性、灵活性、经济性和安全性(简称“五性”)方面对机器学习和数据驱动控制技术的优势进行阐释。最后,对未来研究和应用进行了展望。

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