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Fault detection of FWTPs in coal‐fired power plants using K‐WD‐KPCA in consideration of multiple operation conditions.

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Title: Fault detection of FWTPs in coal‐fired power plants using K‐WD‐KPCA in consideration of multiple operation conditions.
Authors: Tang, Yuling1 (AUTHOR), Zhang, Shirong2 (AUTHOR) srzhang@whu.edu.cn
Source: Asia-Pacific Journal of Chemical Engineering. Mar2021, Vol. 16 Issue 2, p1-18. 18p.
Subjects: Coal-fired power plants, Water purification, Water utilities
Abstract: In coal‐fired power plants, the feed water treatment processes (FWTPs) supply qualified water to utility boilers. The faults of an FWTP may endanger the whole power plant. The classical PCA‐/KPCA‐based fault detection algorithms are valid mainly under single operation condition. When they are put to a practical FWTP, they cannot deal with the problems as treatment route switching, multiple operation conditions, process fluctuations, and process nonlinearity. In this paper, k‐means, wavelet denoise (WD), and KPCA are integrated together to form a new algorithm as K‐WD‐KPCA. It is expected to deal with the process nonlinearity through KPCA, cope with the multiple operation conditions through k‐means, and relieve the process fluctuations through WD. In the experiments on the data sets collected from a practical FWTP, the multiple operation conditions are classified into three categories; consequently, three KPCA models are trained and correspondingly scheduled for online condition matching. WD is further used to denoise the real‐time T2 and SPE statistics. Results show that the WD part of K‐WD‐KPCA algorithm can indeed lower the false alarm rate without reducing its fault detection performance. Finally, the proposed K‐WD‐KPCA algorithm is coded into a software platform and deployed to a coal‐fired power plant containing 2 × 1000 MW generation units. The effectiveness of the K‐WD‐KPCA algorithm is convinced through field application results. [ABSTRACT FROM AUTHOR]
Copyright of Asia-Pacific Journal of Chemical Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
  Group: Ti
  Data: Fault detection of FWTPs in coal‐fired power plants using K‐WD‐KPCA in consideration of multiple operation conditions.
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Tang%2C+Yuling%22">Tang, Yuling</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Shirong%22">Zhang, Shirong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> srzhang@whu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Asia-Pacific+Journal+of+Chemical+Engineering%22">Asia-Pacific Journal of Chemical Engineering</searchLink>. Mar2021, Vol. 16 Issue 2, p1-18. 18p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Coal-fired+power+plants%22">Coal-fired power plants</searchLink><br /><searchLink fieldCode="DE" term="%22Water+purification%22">Water purification</searchLink><br /><searchLink fieldCode="DE" term="%22Water+utilities%22">Water utilities</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In coal‐fired power plants, the feed water treatment processes (FWTPs) supply qualified water to utility boilers. The faults of an FWTP may endanger the whole power plant. The classical PCA‐/KPCA‐based fault detection algorithms are valid mainly under single operation condition. When they are put to a practical FWTP, they cannot deal with the problems as treatment route switching, multiple operation conditions, process fluctuations, and process nonlinearity. In this paper, k‐means, wavelet denoise (WD), and KPCA are integrated together to form a new algorithm as K‐WD‐KPCA. It is expected to deal with the process nonlinearity through KPCA, cope with the multiple operation conditions through k‐means, and relieve the process fluctuations through WD. In the experiments on the data sets collected from a practical FWTP, the multiple operation conditions are classified into three categories; consequently, three KPCA models are trained and correspondingly scheduled for online condition matching. WD is further used to denoise the real‐time T2 and SPE statistics. Results show that the WD part of K‐WD‐KPCA algorithm can indeed lower the false alarm rate without reducing its fault detection performance. Finally, the proposed K‐WD‐KPCA algorithm is coded into a software platform and deployed to a coal‐fired power plant containing 2 × 1000 MW generation units. The effectiveness of the K‐WD‐KPCA algorithm is convinced through field application results. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Asia-Pacific Journal of Chemical Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
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        Value: 10.1002/apj.2599
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 18
        StartPage: 1
    Subjects:
      – SubjectFull: Coal-fired power plants
        Type: general
      – SubjectFull: Water purification
        Type: general
      – SubjectFull: Water utilities
        Type: general
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      – TitleFull: Fault detection of FWTPs in coal‐fired power plants using K‐WD‐KPCA in consideration of multiple operation conditions.
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            NameFull: Tang, Yuling
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            NameFull: Zhang, Shirong
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            – D: 01
              M: 03
              Text: Mar2021
              Type: published
              Y: 2021
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              Value: 19322135
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              Value: 16
            – Type: issue
              Value: 2
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            – TitleFull: Asia-Pacific Journal of Chemical Engineering
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