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Gas turbine off-design behavior modelling and operation windows analysis under different ambient conditions

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  • Zhang, Yuanzhe
  • Liu, Pei
  • Li, Zheng

Abstract

As a core equipment for energy conversion and clean utilization, gas turbines are utilized in many areas under various ambient conditions. Ambient conditions can vary with seasons and time of a year, even in the same location. Ambient conditions also have significant impacts on gas turbine performances at part-load. In this paper, we proposed an off-design behavior mechanism model of gas turbines, which aims at quantifying impacts of ambient conditions on gas turbine performances. Impacts of key parameters are considered, including compressor geometry, staged combustion in a combustor and turbine cooling. Moreover, an operation windows analysis is presented. In order to illustrate variation logics of NOx emissions, load regulating capacity with variations of ambient temperature, pressure and relative humidity, case studies are performed considering twelve parameters, four regions and seven ambient conditions. Results show that the ambient temperature has the most significant impacts on the gas turbine performances. The output power, gas turbine efficiency and load regulation capacity are improved at base-load as well as part-load. When the ambient temperature drops from 15 °C to −15 °C at base-load, the efficiency and the power increase by 2.1% and 13.9%, respectively, whilst the regulation range of which increases from 46.4% to 51.4%.

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  • Zhang, Yuanzhe & Liu, Pei & Li, Zheng, 2023. "Gas turbine off-design behavior modelling and operation windows analysis under different ambient conditions," Energy, Elsevier, vol. 262(PA).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222022307
    DOI: 10.1016/j.energy.2022.125348
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    Cited by:

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    2. Gong, Linjuan & Hou, Guolian & Li, Jun & Gao, Haidong & Gao, Lin & Wang, Lin & Gao, Yaokui & Zhou, Junbo & Wang, Mingkun, 2023. "Intelligent fuzzy modeling of heavy-duty gas turbine for smart power generation," Energy, Elsevier, vol. 277(C).
    3. Zhang, Xinhai & Wang, Kang & Geng, Jia & Li, Ming & Song, Zhiping, 2024. "A fault-tolerant acceleration control strategy for turbofan engine based on multi-layer perceptron with exponential Gumbel loss," Energy, Elsevier, vol. 294(C).

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