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A Model Predictive Control Design for Power Generation Heavy-Duty Gas Turbines

Author

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  • Alessandro Rosini

    (Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture, University of Genoa, Via Opera Pia 11, I-16145 Genova, Italy)

  • Alessandro Palmieri

    (Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture, University of Genoa, Via Opera Pia 11, I-16145 Genova, Italy)

  • Damiano Lanzarotto

    (Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture, University of Genoa, Via Opera Pia 11, I-16145 Genova, Italy)

  • Renato Procopio

    (Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture, University of Genoa, Via Opera Pia 11, I-16145 Genova, Italy)

  • Andrea Bonfiglio

    (Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture, University of Genoa, Via Opera Pia 11, I-16145 Genova, Italy)

Abstract

The new electric power generation scenario, characterized by growing variability due to the greater presence of renewable energy sources (RES), requires more restrictive dynamic requirements for conventional power generators. Among traditional power generators, gas turbines (GTs) can regulate the output electric power faster than any other type of plant; therefore, they are of considerable interest in this context. In particular, the dynamic performance of a GT, being a highly nonlinear and complex system, strongly depends on the applied control system. Proportional–integral–derivative (PID) controllers are the current standard for GT control. However, since such controllers have limitations for various reasons, a model predictive control (MPC) was designed in this study to enhance GT performance in terms of dynamic behavior and robustness to model uncertainties. A comparison with traditional PID-based controllers and alternative model-based control approaches (feedback linearization control) found in the literature demonstrated the effectiveness of the proposed approach.

Suggested Citation

  • Alessandro Rosini & Alessandro Palmieri & Damiano Lanzarotto & Renato Procopio & Andrea Bonfiglio, 2019. "A Model Predictive Control Design for Power Generation Heavy-Duty Gas Turbines," Energies, MDPI, vol. 12(11), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2182-:d:238101
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    References listed on IDEAS

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    1. M. S. Dresselhaus & I. L. Thomas, 2001. "Alternative energy technologies," Nature, Nature, vol. 414(6861), pages 332-337, November.
    2. Panwar, N.L. & Kaushik, S.C. & Kothari, Surendra, 2011. "Role of renewable energy sources in environmental protection: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(3), pages 1513-1524, April.
    3. Bonfiglio, A. & Cacciacarne, S. & Invernizzi, M. & Procopio, R. & Schiano, S. & Torre, I., 2017. "Gas turbine generating units control via feedback linearization approach," Energy, Elsevier, vol. 121(C), pages 491-512.
    4. Pires, Thiago S. & Cruz, Manuel E. & Colaço, Marcelo J. & Alves, Marco A.C., 2018. "Application of nonlinear multivariable model predictive control to transient operation of a gas turbine and NOX emissions reduction," Energy, Elsevier, vol. 149(C), pages 341-353.
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    Cited by:

    1. Hamza Fawzy & Qun Zheng & Naseem Ahmad & Yuting Jiang, 2020. "Optimization of A Swirl with Impingement Compound Cooling Unit for A Gas Turbine Blade Leading Edge," Energies, MDPI, vol. 13(1), pages 1-23, January.
    2. 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.

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