Feed-forward Control of Thermal Power Plants Using Neural Networks

DOI HANDLE オープンアクセス
  • Eki Yurio
    Department of Electrical and Electronic Systems Engineering, Kyushu University : Graduate Student (Omika Works, Hitachi LTD.)
  • 平澤 宏太郎
    九州大学システム情報科学研究院 : 教授
  • 村田 純一
    九州大学システム情報科学研究院 : 教授
  • Hu Jinglu
    Department of Electrical and Electronic Systems Engineering : Research Associate

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抄録

In thermal power plants, it is an important theme to improve the control performance of main steam pressure and temperature etc. during load up/down. This paper focuses on temperature control that is the most difficult problem due to the non-linearity and long dead times of power plants. Model Reference Adaptive Control (MRAC) is applicable to the feed-forward control of power plants, but there are some problems. The most serious problem is that persistently exciting (PE) condition is not satisfied, and so it is difficult to estimate plant parameters using the well-known recursive least squares method. It is proposed in this paper that Jacobians of the neural networks (NN) are applied to identify the above mentioned plant parameters and control law is obtained by two methods, that is, one is the method to use the Jacobians of the NN plant model which is obtained by off line forward model learning, the other is the method to utilize the Hessian of the cost function. This method is evaluated by a detailed simulator that represents accurately the dynamics of power plants, and usefulness and effectiveness of the proposed method is proved.

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詳細情報 詳細情報について

  • CRID
    1390290699820211840
  • NII論文ID
    110000579868
  • NII書誌ID
    AN10569524
  • DOI
    10.15017/1498313
  • ISSN
    21880891
    13423819
  • HANDLE
    2324/1498313
  • 本文言語コード
    en
  • データソース種別
    • JaLC
    • IRDB
    • CiNii Articles
  • 抄録ライセンスフラグ
    使用可

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