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Progress in Modeling and Control of Gas Turbine Power Generation Systems: A Survey

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  • Omar Mohamed

    (King Abdullah II School of Engineering, Princess Sumaya University for Technology, Amman 11941, Jordan)

  • Ashraf Khalil

    (Electrical and Electronic Engineering Department, Universiti Teknologi Brunei, Jalan Tungku Link, Gadong BE1410, Brunei Darussalam)

Abstract

This paper reviews the modeling techniques and control strategies applied to gas turbine power generation plants. Recent modeling philosophies are discussed and the state-of-the-art feasible strategies for control are shown. Research conducted in the field of modeling, simulation, and control of gas turbine power plants has led to notable advancements in gas turbines’ operation and energy efficiency. Tracking recent achievements and trends that have been made is essential for further development and future research. A comprehensive survey is presented here that covers the outdated attempts toward the up-to-date techniques with emphasis on different issues and turbines’ characteristics. Critical review of the various published methodologies is very useful in showing the importance of this research area in practical and technical terms. The different modeling approaches are classified and each category is individually investigated by reviewing a considerable number of research articles. Then, the main features of each category or approach is reported. The modern multi-variable control strategies that have been published for gas turbines are also reviewed. Moreover, future trends are proposed as recommendations for planned research.

Suggested Citation

  • Omar Mohamed & Ashraf Khalil, 2020. "Progress in Modeling and Control of Gas Turbine Power Generation Systems: A Survey," Energies, MDPI, vol. 13(9), pages 1-26, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2358-:d:355584
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    References listed on IDEAS

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    Cited by:

    1. Mohammad Alsarayreh & Omar Mohamed & Mustafa Matar, 2022. "Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning," Sustainability, MDPI, vol. 14(2), pages 1-25, January.
    2. Żymełka, Piotr & Szega, Marcin, 2020. "Issues of an improving the accuracy of energy carriers production forecasting in a computer-aided system for monitoring the operation of a gas-fired cogeneration plant," Energy, Elsevier, vol. 209(C).
    3. Wei, Zhengchao & Ma, Yue & Yang, Ningkang & Ruan, Shumin & Xiang, Changle, 2023. "Reinforcement learning based power management integrating economic rotational speed of turboshaft engine and safety constraints of battery for hybrid electric power system," Energy, Elsevier, vol. 263(PB).
    4. Rui Yang & Yongbao Liu & Xing He & Zhimeng Liu, 2022. "Gas Turbine Model Identification Based on Online Sequential Regularization Extreme Learning Machine with a Forgetting Factor," Energies, MDPI, vol. 16(1), pages 1-19, December.
    5. Jiandong Duan & Fan Liu & Yao Yang & Zhuanting Jin, 2021. "Flexible Dispatch for Integrated Power and Gas Systems Considering Power-to-Gas and Demand Response," Energies, MDPI, vol. 14(17), pages 1-26, September.
    6. Andrés Meana-Fernández & Juan M. González-Caballín & Roberto Martínez-Pérez & Francisco J. Rubio-Serrano & Antonio J. Gutiérrez-Trashorras, 2022. "Power Plant Cycles: Evolution towards More Sustainable and Environmentally Friendly Technologies," Energies, MDPI, vol. 15(23), pages 1-27, November.

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