Data-driven Operating Monitoring for Coal-fired Power Generation Equipment: The State of the Art and Challenge

被引：0
|
作者：
Zhao C.-H. [1 ]
Hu Y.-Y. [1 ]
Zheng J.-L. [1 ]
Chen J.-H. [1 ]
机构：
[1] State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2022年 / 48卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Coal-fired power generation equipment; condition monitoring; machine learning; nonstationary; varying load;
D O I：
10.16383/j.aas.c200993
中图分类号：
学科分类号：
摘要：
As major equipment in coal-fired power generation, 1000 MW ultra-supercritical unit has advantages of large capacity, high parameter and low energy consumption, which has become the mainstream of the development of the power industry in China. Its safety and reliability in operation are of great significance to promote the transformation and upgrading of power generation enterprises. Starting from the analysis of essential characteristics of coal-fired power generation equipment, this article revealed the nonstationary operation characteristics caused by the variable load, deep peak shaving, and the complex correlation characteristics of the whole process. Then, it summarized the problems that the coal-fired power generation process is different from general continuous processes, and points out the necessity of studying monitoring algorithms for coal-fired power generation equipment. Furthermore, based on these characteristics, it reviewed and analyzed the development of the data-driven algorithms for coal-fired power generation equipment monitoring in the past 30 years, showing different stages of algorithm development. On this basis, this article presented the current problems in operation monitoring of coal-fired power generation equipment, and further introduced the possible development direction in the future. © 2022 Science Press. All rights reserved.
引用
收藏
页码：2611 / 2633
页数：22
相关论文
共 168 条
  • [1] Hao Si-Peng, Huang Xian-Ming, Liu Hai-Tao, 1 000 MW Ultra Supercritical Thermal Power Generating Units Electrical Equipmeng and Its Operation, (2014)
  • [2] Typical Case Analysis of Equipment Failure and Shutdown of Thermal Power Generating Unit, (2020)
  • [3] Hua Zhi-Gang, Artificial Intelligence Application of Power Generation Industry, (2020)
  • [4] Zhao Chun-Hui, Wang Fu-Li, Intelligent Monitoring of Industrial Process Operation Status: Data-Driven Methods, (2019)
  • [5] Jiang Qing-Chao, Yan Xue-Feng, Hierarchical monitoring for multi-unit chemical processes based on local-global correlation features, Acta Automatica Sinica, 46, 9, pp. 1770-1782, (2020)
  • [6] Mao Wen-Tao, Tian Si-Yu, Dou Zhi, Zhang Di, Ding Ling, A new deep transfer learning-based online detection method of rolling bearing early fault, Acta Automatica Sinica, 48, 1, pp. 302-314, (2022)
  • [7] Yu K, Lin T R, Ma H, Li X, Li X., A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning, Mechanical Systems and Signal Processing, 146, (2021)
  • [8] Zhao Chun-Hui, Lu Ning-Yun, Statistical Monitoring and Quality Analysis of Batch Process, (2014)
  • [9] Liu Qiang, Qin S. Joe, Perspectives on big data modeling of process industries, Acta Automatica Sinica, 42, 2, pp. 161-171, (2016)
  • [10] Qin S J., Process data analytics in the era of big data, AIChE Journal, 60, 9, pp. 3092-3100, (2014)
12345678910