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Research and Application of Data Mining in Power Plant Process Control and Optimization

  • Jian-qiang Li
  • Cheng-lin Niu
  • Ji-zhen Liu
  • Luan-ying Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)

Abstract

As more and more real-time data is sent to databases by DAS, large amounts of data are accumulated in power plants. Abundant knowledge exists in historical data but it is hard to find and summarize this in a traditional way. This paper proposes a method of operation optimization based on data mining in a power plant. The basic structure of the operation optimization based on data mining is established and the improved fuzzy association rule mining is introduced to find the optimization values from the quantitative data in a power plant. Based on the historical data of a 300MW unit, the optimal values of the operating parameters are found by using data mining techniques. The optimal values are provided to guide the operation online and experiment results show that excellent performance is achieved in the power plant.

Keywords

Data Mining Association Rule Operation Optimization Data Mining Technique Coal Consumption 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Wang, X.Z.: Automatic Classification for Mining Process Operational Data. Ind. Eng. Chem. Res. 37, 2215–2222 (1998)CrossRefGoogle Scholar
  2. 2.
    Tony Ogilvie, B.W., Hogg, C.: Use of data mining techniques in the performance monitoring and operation of a thermal power plant. In: IEEE Colloquium on Knowledge Discovery and Data Mining (1998)Google Scholar
  3. 3.
    Hu, H., Huang, T., Ai, W.: Study and determination of operation optimization target values of large size fossil-fired power unit. Electric Power 37, 22–25 (2004)Google Scholar
  4. 4.
    Agrawal, R., Imielinski, T.: Mining association rules between sets of items in large database. In: Proc. of the ACM SIGMOD conf on Management of data, pp. 207–216 (1993)Google Scholar
  5. 5.
    Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 1–12 (1996)Google Scholar
  6. 6.
    Zou, X., Lu, J., Song, Z.: Mining linguistic valued association rules. Journal of System Simulation 14, 1130–1132 (2002)Google Scholar
  7. 7.
    Liu, F., Hao, W., Yang, J.: Economic analyze for utility boiler operated in different oxygen content outlet furnace and it’s optimization. Proceedings of the CSEE 23, 172–176 (2003)Google Scholar
  8. 8.
    Liu, F.: Performance characteristics identification and optimization of station boiler based on statistical analysis 24, 477–494 (2004)Google Scholar
  9. 9.
    Hong, T.P., Kuo, C.S., Chi, S.C.: Mining association rules from quantitative data. Intelligent Data Analysis 3, 363–376 (1999)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jian-qiang Li
    • 1
  • Cheng-lin Niu
    • 1
  • Ji-zhen Liu
    • 1
  • Luan-ying Zhang
    • 1
  1. 1.Department of AutomationNorth China Electric Power UniversityBaodingChina

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