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A data-based approach for benchmark interval determination with varying operating conditions in the coal-fired power unit

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  • Xu, Jing
  • Bi, Dapeng
  • Ma, Suxia
  • Bai, Jin

Abstract

The modern coal-fired power units in China are mostly operated in a flexible manner. However, flexible operation results in performance degradation, energy-efficiency penalties, and increased energy consumption, which necessitates the detection of performance degradation to save energy. This paper presents a model for detecting the performance degradation of coal-fired power units by determining the benchmark intervals of variables under varying operating conditions using data-mining methods. The K-means clustering method is employed to categorize the operating conditions according to the similarity of historical operational data. Gaussian mixture model is adopted to determine the benchmark interval with respect to the varying operating conditions by estimating the probability of historical runtime data. The methodology is validated using a feedwater heating system of an on-duty coal-fired power unit. The results indicate that in comparison with the design-based method, the proposed method can provide benchmark intervals for 225 operating conditions. In addition, the determined benchmark interval can detect performance degradation earlier than design-based values, thereby providing opportunities for energy-efficiency enhancement.

Suggested Citation

  • Xu, Jing & Bi, Dapeng & Ma, Suxia & Bai, Jin, 2020. "A data-based approach for benchmark interval determination with varying operating conditions in the coal-fired power unit," Energy, Elsevier, vol. 211(C).
    • Handle: RePEc:eee:energy:v:211:y:2020:i:c:s0360544220316637
    DOI: 10.1016/j.energy.2020.118555
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    1. Blanco, J.M. & Vazquez, L. & Peña, F. & Diaz, D., 2013. "New investigation on diagnosing steam production systems from multivariate time series applied to thermal power plants," Applied Energy, Elsevier, vol. 101(C), pages 589-599.
    2. Li, Yong & Wang, Yanhong & Cao, Lihua & Hu, Pengfei & Han, Wei, 2018. "Modeling for the performance evaluation of 600 MW supercritical unit operating No.0 high pressure heater," Energy, Elsevier, vol. 149(C), pages 639-661.
    3. Morosuk, Tatiana & Tsatsaronis, George, 2019. "Advanced exergy-based methods used to understand and improve energy-conversion systems," Energy, Elsevier, vol. 169(C), pages 238-246.
    4. Fan, Cheng & Sun, Yongjun & Shan, Kui & Xiao, Fu & Wang, Jiayuan, 2018. "Discovering gradual patterns in building operations for improving building energy efficiency," Applied Energy, Elsevier, vol. 224(C), pages 116-123.
    5. Gu, Yujiong & Xu, Jing & Chen, Dongchao & Wang, Zhong & Li, Qianqian, 2016. "Overall review of peak shaving for coal-fired power units in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 723-731.
    6. Hirotugu Akaike, 1987. "Factor analysis and AIC," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 317-332, September.
    7. Blanco, Jesús M. & Vazquez, L. & Peña, F., 2012. "Investigation on a new methodology for thermal power plant assessment through live diagnosis monitoring of selected process parameters; application to a case study," Energy, Elsevier, vol. 42(1), pages 170-180.
    8. Wang, Jiawei & You, Shi & Zong, Yi & Træholt, Chresten & Dong, Zhao Yang & Zhou, You, 2019. "Flexibility of combined heat and power plants: A review of technologies and operation strategies," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    9. Wang, Yanhong & Cao, Lihua & Hu, Pengfei & Li, Bo & Li, Yong, 2019. "Model establishment and performance evaluation of a modified regenerative system for a 660 MW supercritical unit running at the IPT-setting mode," Energy, Elsevier, vol. 179(C), pages 890-915.
    10. Fan, Cheng & Xiao, Fu & Zhao, Yang & Wang, Jiayuan, 2018. "Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data," Applied Energy, Elsevier, vol. 211(C), pages 1123-1135.
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