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Experimental and numerical study on self-sustaining performance of a 30-kW micro gas turbine generator system during startup process

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  • Guan, Jin
  • Lv, Xiaojing
  • Spataru, Catalina
  • Weng, Yiwu

Abstract

The safe startup of micro gas turbine (MGT) generator system is the premise of normal operation. The whole start-up process contains motor startup, ignition, speed acceleration, motor switching to generator and power acceleration. Motor switching to generator happens at the self-sustaining state, which is significant to safe start-up process. However, characteristics of MGT generator system at self-sustaining state are hardly to investigate due to the lack of performance maps and complete experiments. Therefore, this work analyzed start-up schedule and presented a theoretical and experimental study on the self-sustaining performance of MGT generator system, based on the self-designed 30 kW MGT generator system built in Jiangjin Turbocharger Plant, China. The self-sustaining speed boundary and fuel consumption area is determined from the aspects of safe startup. A novel principle for determining the self-sustaining point (SSP) is proposed. Results show that the self-sustaining state can be achieved only when speed is over 26,750 rpm, and the SSP is determined at the speed of 30,750 rpm based on the proposed principle. Finally, the self-sustaining TIT and natural gas flow are compared with the experimental data, with two relative errors both almost within 4%. This method is instructive to the MGT generator system startup process.

Suggested Citation

  • Guan, Jin & Lv, Xiaojing & Spataru, Catalina & Weng, Yiwu, 2021. "Experimental and numerical study on self-sustaining performance of a 30-kW micro gas turbine generator system during startup process," Energy, Elsevier, vol. 236(C).
    • Handle: RePEc:eee:energy:v:236:y:2021:i:c:s0360544221017163
    DOI: 10.1016/j.energy.2021.121468
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    References listed on IDEAS

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    Cited by:

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    2. Wenxiang Zhou & Sangwei Lu & Wenjie Kai & Jichang Wu & Chenyang Zhang & Feng Lu, 2023. "A Novel Adaptive Generation Method for Initial Guess Values of Component-Level Aero-Engine Start-Up Models," Sustainability, MDPI, vol. 15(4), pages 1-25, February.
    3. Cheng, Hongzhi & Li, Ziliang & Duan, Penghao & Lu, Xingen & Zhao, Shengfeng & Zhang, Yanfeng, 2023. "Robust optimization and uncertainty quantification of a micro axial compressor for unmanned aerial vehicles," Applied Energy, Elsevier, vol. 352(C).
    4. Nareg Basmadjian & Sean Yun & Zekai Hong, 2023. "Optimization of Micro Gas Turbine Based Hybrid Systems for Remote Off-grid Communities," Energy & Environment, , vol. 34(4), pages 1060-1080, June.
    5. Wang, Xusheng & Lv, Xiaojing & Mi, Xicong & Spataru, Catalina & Weng, Yiwu, 2022. "Coordinated control approach for load following operation of SOFC-GT hybrid system," Energy, Elsevier, vol. 248(C).

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