Dynamic Modeling of Electromechanical Energy Conversion Systems in Advanced Gas Turbines using Adaptive Control Algorithms
Main Article Content
Abstract
This paper introduces a rigorous dynamic modeling and control platform of the electromechanical energy conversion systems of single-shaft, heavy-duty gas turbines. As contemporary power grids become increasingly volatile with the integration of intermittent renewable power sources, traditional fixed-gain Proportional-Integral-Derivative (PID) controllers are becoming less effective in maintaining a stable frequency and protecting the thermomechanical limits of the turbines. To address this limitation, a non-linear dynamic model was developed incorporating governor droop, valve actuator inertia, combustion thermodynamics, and rotor swing dynamics. Then, a synthesized PID-based Model Reference Adaptive Control (MRAC) algorithm that dynamically adjusts control gains in real-time using the MIT rule was developed. Three critical operational stress tests applied to the proposed MRAC-PID architecture included a 20% step load disturbance, high-frequency load tracking, and parameter uncertainty with half the grid inertia. Quantitative analysis demonstrates that the MRAC-PID controller shows significant improvements in comparison with the classical PID, reducing the maximum frequency undershoot from -0.018 p.u. to (a 61.1% improvement), reducing thermal overshoots by 88.8%, and decreasing transient settling time by 63.8%. Moreover, the adaptive algorithm effectively eliminated valve chattering during stochastic load operation and mitigated severe torsional resonance during low-inertia operation.
Downloads
Article Details
Section
How to Cite
References
Abbassen, L., Zaouia, M., Benamrouche, N., and Bousbaine, A., 2020. Control strategies of a gas turbine generator: a comparative study. Indonesian Journal of Electrical Engineering and Informatics, 8(4), pp. 626-636. https://doi.org/10.11591/ijeei.v8i4.2100.
Alaoui, M., Alshammari, O. S., Iratni, A., Hafaifa, A., and Jerbi, H., 2022. Gas turbine speed monitoring using a generalized predictive adaptive control algorithm. Studies in Informatics and Control, 31(3), P. 87. https://doi.org/10.24846/v31i3y202208
Alsarayreh, M., Mohamed, O., and Matar, M., 2022. Modeling a practical dual-fuel gas turbine power generation system using dynamic neural network and deep learning. Sustainability, 14, P. 870. https://doi.org/10.3390/su14020870
Al-Shami, M., Mohamed, O., and Abu Elhaija, W., 2023. Energy-efficient control of a gas turbine power generation system. Designs, 7(4), pp. 85. https://doi.org/10.3390/designs7040085
Andoga, R., Főző, L., and Vaščák, J., 2024, May. Fuzzy adaptive speed controller for a small turbojet engine. In 2024 IEEE 18th International Symposium on Applied Computational Intelligence and Informatics (SACI), pp. 000099-000104. IEEE. https://doi.org/10.1109/SACI60582.2024.10619911
Badoruzzaman, M., Shuvo, J. I., Anik, S. T. I., Mithil, M. S. K., Unjum, H. F., Huda, A. N., and Ahmad, S., 2025, February. Autonomous power and dynamic load management in grid-connected microgrid with supercapacitor and EV integration. In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1-6. IEEE. https://doi.org/10.1109/ECCE64574.2025.11013269
Bulatov, Y.N., Kryukov, A.V., and Van Huan, N., 2020. Simulation of gas turbine power plants with voltage and speed prognostic regulators. Proceedings of the 2020 International Russian Automation Conference (RusAutoCon), Sochi, Russia, pp. 160–164. https://doi.org/10.1109/RusAutoCon49822.2020.9208114
Farrar, N. O., Ali, M. H., and Dasgupta, D., 2023. Artificial intelligence and machine learning in grid connected wind turbine control systems: A comprehensive review. Energies, 16(3), P. 1530. https://doi.org/10.3390/en16031530
Ghmayes, I. K., 2025. Advanced hybrid PID–adaptive control strategy for enhanced gas turbine engine performance. Procedia of Engineering and Life Science, 8(1), pp. 114-129. https://doi.org/10.21070/pels.v8i1.2686
Haji, V., Fekih, A., Monje, C.A., and Fakhri Asfestani, R., 2019. H2, H∞, H2/H∞, and μ-synthesis controllers for the speed and temperature control of a real gas turbine unit in a combined cycle power plant. Energy Science & Engineering, 7, pp. 2205–2222. https://doi.org/10.1002/ese3.425
He, Y., Liu, Y., Yang, L., and Qu, X., 2023. Deep adaptive control: Deep reinforcement learning-based adaptive vehicle trajectory control algorithms for different risk levels. IEEE Transactions on Intelligent Vehicles, 9(1), pp. 1654-1666. https://doi.org/10.1109/TIV.2023.3303408
Javanmardi, H., and Hamedi, A., 2026. Advanced hybrid control for high-efficiency energy conversion in regenerative micro gas turbines. IEEE Access. https://doi.org/10.1109/ACCESS.2026.3667023
Jonathan, A. E., Olubiwe, M., Okozi, S. O., and Agubor, C. K., 2018. Exhaust temperature control of heavy-duty gas turbine due to incremental load demand. International Journal of Engineering Research & Technology, 7(7), pp. 420-426. http://www.ijert.org/
Kanashev, E.A., and Rozhko, E.V., 2018. Experimental research of gas turbine unit control systems using simulation model. Proceedings of the 2018 International Russian Automation Conference (RusAutoCon), Sochi, Russia, pp. 1–6. https://doi.org/10.1109/RUSAUTOCON.2018.8501685
Kundu, S., Singh, M., and Giri, A. K., 2024. Adaptive control approach-based isolated microgrid system with alleviating power quality problems. Electric Power Components and Systems, 52(7), pp. 1219-1234. https://doi.org/10.1080/15325008.2023.2239222
Li, L., Pei, W., Deng, W., & Su, X., 2024, November. Analysis on difference of dynamic characteristics and stability between synchronous generator and virtual synchronous generator. In 2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2), pp. 1226-1230. IEEE. https://doi.org/10.1109/EI264398.2024.10991686
Li, S., Sun, S., Xue, Y., and Sun, L., 2022, December. Dynamic modeling and predictive control of heavy-duty gas turbines. In Asian Simulation Conference, pp. 446-461. Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-19-9195-0_36
Li, Z., Han, H., Wang, H., and Feng, Q., 2023. Grid-connected adaptive analysis and control of the DC wind turbine under the grid-side transient faults. IEEE Access, 11, pp. 129340-129352. https://doi.org/10.1109/ACCESS.2023.3332359
Liu, Y., and Jiang, X., 2022, October. Towards predictive maintenance of a heavy-duty gas turbine a new hybrid intelligent methodology for performance simulation. In Annual Conference of the PHM Society 14(1).
Maharmi, B., Cholid, I., Syafii, and Arya, E. H., 2024. Optimization of speed droop governor operation at gas turbine cogeneration unit. Indonesian Journal of Electrical Engineering and Computer Science, 33(1), pp. 20-30. https://doi.org/10.11591/ijeecs.v33.i1.
Mao, H., and Guo, Y., 2025. Joint system modeling approach for fault simulation of start-er/generator and gas generator in all-electric APU. arXiv preprint arXiv:2506.10562. https://arxiv.org/pdf/2506.10562
Mevissen, F., Krewinkel, R., and Wiedermann, A., 2026. The future of industrial gas turbines: technological advances and market trends. Journal of Thermal Science and Engineering Applications, 18(3), P. 031002. https://doi.org/10.1115/1.4070223
Mohamed, O., and Khalil, A., 2020. Progress in modeling and control of gas turbine power generation systems: A survey. Energies, 13, P. 2358. https://doi.org/10.3390/en13092358
Mohamed, O., Wang, J., Khalil, A., and Limhabrash, M., 2016. Predictive control strategy of a gas turbine for improvement of combined cycle power plant dynamic performance and efficiency. SpringerPlus, 5, P. 980. https://doi.org/10.1186/s40064-016-2679-2
Muhammad, M. M., Gana, M. A., Usman, J., and Mustapha, I., 2023. Review on load frequency control techniques. Nigerian Journal of Engineering Science and Technology Research, 9(1), pp. 38-48.
Odinaka, O. B., 2023. Adaptive speed controller for micro gas turbine systems using evolutionary search based on genetic algorithms. Journal of Computational Mechanics, 6(4). https://doi.org/10.46253/jcmps.v6i4.a2
Patrascioiu, C., and Popescu, M., 2020. Control system for natural gas compression of sinca plant. Proceedings of the 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Bucharest, Romania. https://doi.org/10.1109/ECAI50035.2020.9223184
Prakash, S., Alkhatib, M., Sindi, H. F., Alghamdi, S., Behera, R. K., and Muduli, U. R., 2025. Adaptive control for shunt active power filter under stochastic solar photovoltaics behavior. IEEE Journal of Emerging and Selected Topics in Power Electronics, 13(3), pp. 3673-3687. https://doi.org/10.1109/JESTPE.2025.3525489
Qasem, M., Mohamed, O., and Abu Elhaija, W., 2022. Parameter identification and sliding pressure control of a supercritical power plant using whale optimizer. Sustainability, 14, P. 8039. https://doi.org/10.3390/su14138039
Shahin, S., Elkhashap, A., Abdelgelil, M., and Hamad, M.S., 2021. Nonlinear model predictive control for power generation gas turbines. Proceedings of the 2021 International Telecommunications Conference (ITC-Egypt), Alexandria, Egypt, pp. 1–6. https://doi.org/10.1109/ITC-Egypt52936.2021.9513953
Shi, G., Wu, Z., He, T., Li, D., Ding, Y., and Liu, S., 2020. Decentralized active disturbance rejection control design for the gas turbine. Measurement and Control, 53, pp. 1589–1601. https://doi.org/10.1177/0020294020947130
Sjöberg, A., and Blohmé, K., 2025. Frequency stability in grids with a high share of variable generation: the role of gas turbines. Lund University. http://lup.lub.lu.se/student-papers/record/9193354
Talah, D., and Bentarzi, H., 2020. Modeling and analysis of heavy-duty gas turbine based on frequency dependent model. Proceedings of the 2020 International Conference on Electrical Engineering (ICEE), Istanbul, Turkey, pp. 1–4. https://doi.org/10.1109/ICEE49691.2020.9249948
Tayyah, A., AlShami, M., and Daboubi, O., 2019. Design of a discrete time model predictive control for a fossil fueled power plant. Princess Sumaya University for Technology, Amman, Jordan. https://doi.org/10.3390/designs7040085
Wang, Y., Bhattacharyya, D., and Turton, R., 2021. Multiobjective dynamic optimization for optimal load-following of natural gas combined cycle power plants under stress constraints. Ind. Eng. Chem. Res., 60, pp. 14251–14270. https://doi.org/10.1021/acs.iecr.1c01461
Wu, Z., Li, D., Xue, Y., and Chen, Y., 2019. Gain scheduling design based on active disturbance rejection control for thermal power plant under full operating conditions. Energy, 185, pp. 744–762. https://doi.org/10.1016/B978-0-32-395207-1.00029-9