View in EDS PDF Full Text

Estimating Feasible Power Output Boundaries of CHP Units by Mining Extreme Power Values from Historical Operation Data Sequence.

Saved in:
Bibliographic Details
Title: Estimating Feasible Power Output Boundaries of CHP Units by Mining Extreme Power Values from Historical Operation Data Sequence.
Authors: Du, Hongfei1 Du_9011@163.com, Jin, Tao1 JingT_7805@163.com, Jia, Fengsheng1 Jiafs_9011@163.com, Cong, Lin1 Congl_8209@163.com
Source: Engineering Letters. May2026, Vol. 34 Issue 5, p1579-1588. 10p.
Subjects: K-means clustering, Piecewise linear approximation, Data mining, Electric power system control, Trigeneration (Energy), Electric power system management
Abstract: With substantial amounts of renewable energies being integrated into power grids, combined heat and power (CHP) units are required to regulate their power outputs to compensate for power fluctuations caused by renewable energies. The feasible power output boundaries are important indices, which indicate the regulative capacities of CHP units. This paper proposes a method to estimate the feasible power output boundaries. The proposed method comprises both an offline stage and an online stage. During the offline stage, the operating conditions of the mass flow rate of extraction steam are identified through the K-means algorithm. Subsequently, the stable power output data segments corresponding to each operating condition are determined via the piecewise linear representation algorithm. Lastly, the power output boundaries of each operating condition are regarded as the maximum and minimum mean values of the stable power output data segments. During the online stage, the current operating condition is determined by finding the minimum Euclidean distance between the mass flow rate of extraction steam to each center of the operating conditions, and then the power output boundaries of the current operating condition are regarded as the estimates of the feasible power output boundaries. To determine the operating conditions via the K-means algorithm in an unsupervised manner, an optimal clustering number determination algorithm is formulated. An industrial example is provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) 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.)
Database: Engineering Source
FullText Links:
  – Type: pdflink
Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 193453916
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Estimating Feasible Power Output Boundaries of CHP Units by Mining Extreme Power Values from Historical Operation Data Sequence.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Du%2C+Hongfei%22">Du, Hongfei</searchLink><relatesTo>1</relatesTo><i> Du_9011@163.com</i><br /><searchLink fieldCode="AR" term="%22Jin%2C+Tao%22">Jin, Tao</searchLink><relatesTo>1</relatesTo><i> JingT_7805@163.com</i><br /><searchLink fieldCode="AR" term="%22Jia%2C+Fengsheng%22">Jia, Fengsheng</searchLink><relatesTo>1</relatesTo><i> Jiafs_9011@163.com</i><br /><searchLink fieldCode="AR" term="%22Cong%2C+Lin%22">Cong, Lin</searchLink><relatesTo>1</relatesTo><i> Congl_8209@163.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. May2026, Vol. 34 Issue 5, p1579-1588. 10p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink><br /><searchLink fieldCode="DE" term="%22Piecewise+linear+approximation%22">Piecewise linear approximation</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+system+control%22">Electric power system control</searchLink><br /><searchLink fieldCode="DE" term="%22Trigeneration+%28Energy%29%22">Trigeneration (Energy)</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+system+management%22">Electric power system management</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: With substantial amounts of renewable energies being integrated into power grids, combined heat and power (CHP) units are required to regulate their power outputs to compensate for power fluctuations caused by renewable energies. The feasible power output boundaries are important indices, which indicate the regulative capacities of CHP units. This paper proposes a method to estimate the feasible power output boundaries. The proposed method comprises both an offline stage and an online stage. During the offline stage, the operating conditions of the mass flow rate of extraction steam are identified through the K-means algorithm. Subsequently, the stable power output data segments corresponding to each operating condition are determined via the piecewise linear representation algorithm. Lastly, the power output boundaries of each operating condition are regarded as the maximum and minimum mean values of the stable power output data segments. During the online stage, the current operating condition is determined by finding the minimum Euclidean distance between the mass flow rate of extraction steam to each center of the operating conditions, and then the power output boundaries of the current operating condition are regarded as the estimates of the feasible power output boundaries. To determine the operating conditions via the K-means algorithm in an unsupervised manner, an optimal clustering number determination algorithm is formulated. An industrial example is provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193453916
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 10
        StartPage: 1579
    Subjects:
      – SubjectFull: K-means clustering
        Type: general
      – SubjectFull: Piecewise linear approximation
        Type: general
      – SubjectFull: Data mining
        Type: general
      – SubjectFull: Electric power system control
        Type: general
      – SubjectFull: Trigeneration (Energy)
        Type: general
      – SubjectFull: Electric power system management
        Type: general
    Titles:
      – TitleFull: Estimating Feasible Power Output Boundaries of CHP Units by Mining Extreme Power Values from Historical Operation Data Sequence.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Du, Hongfei
      – PersonEntity:
          Name:
            NameFull: Jin, Tao
      – PersonEntity:
          Name:
            NameFull: Jia, Fengsheng
      – PersonEntity:
          Name:
            NameFull: Cong, Lin
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 1816093X
          Numbering:
            – Type: volume
              Value: 34
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Engineering Letters
              Type: main
ResultId 1