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Cluster Partition Operation Study of Air-Cooled Fan Groups in a Natural Wind Disturbance

Author

Listed:
  • Guijie Zheng

    (Energy and Electricity Research Centre, Jinan University, Zhuhai 519070, China)

  • Wentao Wen

    (Energy and Electricity Research Centre, Jinan University, Zhuhai 519070, China)

  • Hui Deng

    (Energy and Electricity Research Centre, Jinan University, Zhuhai 519070, China)

  • Yang Cai

    (Energy and Electricity Research Centre, Jinan University, Zhuhai 519070, China)

Abstract

This study discusses the influence of natural wind on the air flow of air-cooled condensers (ACCs) and then proposes a partition speed-regulation strategy for a fan group with enhanced generalized capability, which is of great practical significance for optimizing energy-saving operations. The stochastic time-varying features of natural wind are characterized by sine–Gaussian, Weibull, and composed winds. In a natural wind disturbance, using the Sugon Supercomputing Center, the transient numerical simulation of the dynamic evolution of the ACC flow field was found: the dynamic system of air flow is a typical time-varying nonlinear process. Cluster analysis was used to extract the nonlinear features of air flow, divide the fan group into four subregions with generalization capability, and implement a partitioned speed operation. It was found that giving priority to increasing the fan speed in the headwind partition can suppress the natural wind disturbance and improve the overall air flow, thus reducing the fan speed in the leeward partition, which reduces the overall air flow loss. The dynamic characteristics of the fan group obtained from the simulation and the proposed fan partition method can guide the optimized energy-saving operation of ACCs.

Suggested Citation

  • Guijie Zheng & Wentao Wen & Hui Deng & Yang Cai, 2023. "Cluster Partition Operation Study of Air-Cooled Fan Groups in a Natural Wind Disturbance," Energies, MDPI, vol. 16(9), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3717-:d:1133647
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    References listed on IDEAS

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    1. Stephen Johnson, 1967. "Hierarchical clustering schemes," Psychometrika, Springer;The Psychometric Society, vol. 32(3), pages 241-254, September.
    2. Wenhui Huang & Lei Chen & Lijun Yang & Xiaoze Du, 2021. "Energy-Saving Strategies of Axial Flow Fans for Direct Dry Cooling System," Energies, MDPI, vol. 14(11), pages 1-25, May.
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