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Power Substation Construction and Ventilation System Co-Designed Using Particle Swarm Optimization

Author

Listed:
  • Jau-Woei Perng

    (Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan)

  • Yi-Chang Kuo

    (Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
    Taiwan Power Company Southern Region Construction Office, Kaohsiung 81166, Taiwan)

  • Yao-Tsung Chang

    (Taiwan Power Company Southern Region Construction Office, Kaohsiung 81166, Taiwan)

  • Hsi-Hsiang Chang

    (Taiwan Power Company Southern Region Construction Office, Kaohsiung 81166, Taiwan)

Abstract

This study discusses a numerical study that was developed to optimize the ventilation system in a power substation prior to its installation. We established a multiobjective particle swarm optimizer to identify the best approach for simultaneously improving, first, the ventilation performance considering the most appropriate inlet size and outlet openings and second, the reduction of the synthetic noise of the ventilation and power consumption from the exhaust fan equipment and its operation. The study used building information modeling to construct indoor and outdoor models of the substation building and verified the overall performance using ANSYS FLUENT 18.0 software to simulate the air velocity and air temperature distribution within the building. Results show that the exhaust fan of the B1F cable finishing room and the 23 kV gas insulated switchgear (GIS) room optimize the reduction of horsepower by approximately 1 Hp and 0.5 Hp. The combined noise is reduced by 4 dBA and 2 dBA; the exhaust fan runs for 30 min, and the two equipment rooms can cool down by 2.9 °C and 1.7 °C, respectively. Therefore, it is confirmed that the MOPSO algorithm provides a more energy-efficient and environmentally friendly building ventilation environment.

Suggested Citation

  • Jau-Woei Perng & Yi-Chang Kuo & Yao-Tsung Chang & Hsi-Hsiang Chang, 2020. "Power Substation Construction and Ventilation System Co-Designed Using Particle Swarm Optimization," Energies, MDPI, vol. 13(9), pages 1-27, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2314-:d:354692
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    References listed on IDEAS

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    1. Jau-Woei Perng & Yi-Chang Kuo & Shih-Pin Lu, 2018. "Grounding System Cost Analysis Using Optimization Algorithms," Energies, MDPI, vol. 11(12), pages 1-19, December.
    2. Kanendra Naidu & Mohd Syukri Ali & Ab Halim Abu Bakar & Chia Kwang Tan & Hamzah Arof & Hazlie Mokhlis, 2020. "Optimized artificial neural network to improve the accuracy of estimated fault impedances and distances for underground distribution system," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-22, January.
    3. Ioan Petri & Sylvain Kubicki & Yacine Rezgui & Annie Guerriero & Haijiang Li, 2017. "Optimizing Energy Efficiency in Operating Built Environment Assets through Building Information Modeling: A Case Study," Energies, MDPI, vol. 10(8), pages 1-17, August.
    4. Omer, Abdeen Mustafa, 2008. "Renewable building energy systems and passive human comfort solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(6), pages 1562-1587, August.
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