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Power Generation Prediction of an Open Cycle Gas Turbine Using Kalman Filter

Author

Listed:
  • Christos Manasis

    (Energy Systems Laboratory, National and Kapodistrian University of Athens, 34400 Psachna Evia, Greece)

  • Nicholas Assimakis

    (Energy Systems Laboratory, National and Kapodistrian University of Athens, 34400 Psachna Evia, Greece)

  • Vasilis Vikias

    (Energy Systems Laboratory, National and Kapodistrian University of Athens, 34400 Psachna Evia, Greece)

  • Aphrodite Ktena

    (Energy Systems Laboratory, National and Kapodistrian University of Athens, 34400 Psachna Evia, Greece)

  • Tassos Stamatelos

    (Thermodynamics and Thermal Engines Laboratory, Department of Mechanical Engineering, University of Thessaly, 38334 Volos, Greece)

Abstract

The motivation for this paper is the enhanced role of power generation prediction in power plants and power systems in the smart grid paradigm. The proposed approach addresses the impact of the ambient temperature on the performance of an open cycle gas turbine when using the Kalman Filter (KF) technique and the power-temperature (P-T) characteristic of the turbine. Several Kalman Filtering techniques are tested to obtain improved temperature forecasts, which are then used to obtain output power predictions. A typical P-T curve of an open-cycle gas turbine is used to demonstrate the applicability of the proposed method. Nonlinear and linear discrete process models are studied. Extended Kalman Filters are proposed for the nonlinear model. The Time Varying, Time Invariant, and Steady State Kalman Filters are used with the linearized model. Simulation results show that the power generation prediction obtained using the Extended Kalman Filter with the piecewise linear model yields improved forecasts. The linear formulations, though less accurate, are a promising option when a power generation forecast for a small-term and short-term time window is required.

Suggested Citation

  • Christos Manasis & Nicholas Assimakis & Vasilis Vikias & Aphrodite Ktena & Tassos Stamatelos, 2020. "Power Generation Prediction of an Open Cycle Gas Turbine Using Kalman Filter," Energies, MDPI, vol. 13(24), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:24:p:6692-:d:464339
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    References listed on IDEAS

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    1. González-Díaz, Abigail & Alcaráz-Calderón, Agustín M. & González-Díaz, Maria Ortencia & Méndez-Aranda, Ángel & Lucquiaud, Mathieu & González-Santaló, Jose Miguel, 2017. "Effect of the ambient conditions on gas turbine combined cycle power plants with post-combustion CO2 capture," Energy, Elsevier, vol. 134(C), pages 221-233.
    2. Angelos Angelopoulos & Aphrodite Ktena & Christos Manasis & Stamatis Voliotis, 2019. "Impact of a Periodic Power Source on a RES Microgrid," Energies, MDPI, vol. 12(10), pages 1-15, May.
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    Cited by:

    1. Joanna Kajewska-Szkudlarek & Jan Bylicki & Justyna Stańczyk & Paweł Licznar, 2021. "Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems," Energies, MDPI, vol. 14(22), pages 1-15, November.
    2. Konstantin Zadiran & Maxim Shcherbakov, 2023. "New Method of Degradation Process Identification for Reliability-Centered Maintenance of Energy Equipment," Energies, MDPI, vol. 16(2), pages 1-21, January.

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