• Home
  • Journals
  • Focus
  • Conferences
  • Researchers
  • Sign in

Outline

  • Abstract
  • Keywords

Figures(5)

标签(1)

Table 1

其他(2)

PDF
Document

Frontiers in Energy

2020, Volume 14,  Issue 4, Pages 817-835
    • PDF
    • collect

    Dynamic simulation of gas turbines via feature similarity-based transfer learning

    Key Laboratory of Power Machinery and Engineering (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200240, China

    Accepted: 2020-12-09 Available online:2020-12-09
    Show More
    10.1007/s11708-020-0709-9
    Cite this article
    Dengji ZHOU, Jiarui HAO, Dawen HUANG, Xingyun JIA, Huisheng ZHANG.Dynamic simulation of gas turbines via feature similarity-based transfer learning[J].Frontiers in Energy,2020,14(4):817-835.

    Abstract

    Since gas turbine plays a key role in electricity power generating, the requirements on the safety and reliability of this classical thermal system are becoming gradually strict. With a large amount of renewable energy being integrated into the power grid, the request of deep peak load regulation for satisfying the varying demand of users and maintaining the stability of the whole power grid leads to more unstable working conditions of gas turbines. The startup, shutdown, and load fluctuation are dominating the operating condition of gas turbines. Hence simulating and analyzing the dynamic behavior of the engines under such instable working conditions are important in improving their design, operation, and maintenance. However, conventional dynamic simulation methods based on the physic differential equations is unable to tackle the uncertainty and noise when faced with variant real-world operations. Although data-driven simulating methods, to some extent, can mitigate the problem, it is impossible to perform simulations with insufficient data. To tackle the issue, a novel transfer learning framework is proposed to transfer the knowledge from the physics equation domain to the real-world application domain to compensate for the lack of data. A strong dynamic operating data set with steep slope signals is created based on physics equations and then a feature similarity-based learning model with an encoder and a decoder is built and trained to achieve feature adaptive knowledge transferring. The simulation accuracy is significantly increased by 24.6% and the predicting error reduced by 63.6% compared with the baseline model. Moreover, compared with the other classical transfer learning modes, the method proposed has the best simulating performance on field testing data set. Furthermore, the effect study on the hyper parameters indicates that the method proposed is able to adaptively balance the weight of learning knowledge from the physical theory domain or from the real-world operation domain.

    Keywords

    gas turbine ; dynamic simulation ; data-driven ; transfer learning ; feature similarity
    Previous article in issue
    article in issue Next
    登录后,您可以进行评论。请先登录

    评论

    评论

    • 所有评论
     咋就跳到顶部了
    2019-04-23 11:24:14
    回复 (0)
    inspur  手机账号
    2019-05-10 11:30:17
    回复 (0)

    Read

    87

    Download

    5

    Related Research

    Current Issue
      Current Issue
        Follow us
        Copyright © 2015 China Engineering Science Press.
        京ICP备11030251号
        Follow us
        Copyright © 2015 China Engineering Science Press.
        京ICP备11030251号