Mathematical modeling of combined-cycle units has produced a variety of valuable results, primarily significant advances in their energy efficiency and education of power plant operators. These positive results motivate the need for more complex modeling and simulation techniques with the goal of greater accuracy and accessibility. This study presents a simplified and enhanced version of a gas turbine model for dynamic performance simulation. The theoretical improvements have been proved through modeling philosophy and simulation accuracy attained by metaheuristic optimization techniques. The model has been constructed using thermodynamic and mathematical concepts, with parameter optimization and calibration performed using metaheuristic optimizers. The suggested model’s resilience has been demonstrated by simulation results.
HuthMHeilosA. Fuel flexibility in gas turbine systems: impact on burner design and performance. In: JansohnP (ed.) Modern gas turbine systems: high efficiency, low emission, fuel flexible power generation. Oxford: Woodhead Publishing, 2013, pp. 635–684.
2.
RayaproluK. Boilers for power and process. Boca Raton, FL: CRC Press, 2009.
3.
International Energy Agency (IEA). Renewables 2022—analysis and forecasts to 2027. Paris: IEA, 2022.
4.
MohamedOKhalilA. Progress in modeling and control of gas turbine power generation systems: a survey. Energies2020; 13: 2358.
5.
MohamedOZa’terME. Comparative study between three modeling approaches for a gas turbine power generation system. Arab J Sci Eng2020; 45: 1803–1820.
6.
MohamedOWangJKhalilA, et al. Predictive control strategy of a gas turbine for improvement of combined cycle power plant dynamic performance and efficiency. SpringerPlus2016; 5: 980.
7.
MohamedOKhalilALimhabrashM, et al. The application of System Identification via Canonical Variate Algorithm to North Benghazi gas turbine Power generation system. In: Proceedings of the 2015 IEEE Jordan conference on applied electrical engineering and computing technologies (AEECT), Amman, 3–5 November 2015, pp. 1–6. New York: IEEE.
RowenWI. Simplified mathematical representations of heavy-duty gas turbines. J Eng Power: T ASME1983;105: 865–869.
10.
SajadAMokrovaNV. Black-box identification of gas turbine output parameters using dynamic neural network. In: Proceedings of the 2020 international multi-conference on industrial engineering and modern technologies (FarEastCon), Vladivostok, Russia, 6–9 October 2020, pp. 1–6. New York: IEEE.
11.
AlsarayrehMMohamedOMatarM. Modeling a practical dual-fuel gas turbine power generation system using dynamic neural network and deep learning. Sustainability2022; 14: 870.
12.
YudhantoRBiyantoTR. Performance optimization of gas turbine generator based on operating conditions using ANN-GA at Saka Indonesia Pangkah Ltd. IPTEK J Proc Ser2021; 90: 24–28.
13.
PierezanJMaidlGMassashi YamaoE, et al. Cultural coyote optimization algorithm applied to a heavy duty gas turbine operation. Energ Convers Manage2019; 199: 111932.
14.
DirikM. Prediction of NOx emissions from gas turbines of a combined cycle power plant using an ANFIS model optimized by GA. Fuel2022; 321: 124037.
15.
HouGGongLHuangC, et al. Fuzzy modeling and fast model predictive control of gas turbine system. Energy2020; 200: 117465.
16.
AissatSHafaifaAIratniA, et al. Fuzzy decoupled-states multi-model identification of gas turbine operating variables through the use of their operating data. ISA T2023; 133: 384–396.
17.
ZárateJJuárez-SmithPCarmonaJ, et al. Modelling the vibration response of a gas turbine using machine learning. Expert Syst2020; 37: e12560.
18.
HosseinalizadehTSalamatiSMSalamatiSA, et al. Improvement of identification procedure using hybrid Cuckoo Search algorithm for turbine-governor and excitation system. IEEE T Energy Conver2019; 34: 585–593.
19.
ZhipengYXuyanZJunkunL. Modeling and simulation of heavy duty gas turbine. In: Proceedings of the 2021 6th international conference on power and renewable energy (ICPRE), Shanghai, China, 17–20 September 2021, pp. 1495–1500. New York: IEEE.
20.
PlisMRusinowskiH. A mathematical model of an existing gas-steam combined heat and power plant for thermal diagnostic systems. Energy2018; 156: 606–619.
21.
AyvazA. Hunger games search algorithm for model order reduction of gas turbine power plants. In: Proceedings of the 2022 international congress on human-computer interaction, optimization and robotic applications (HORA), Ankara, Turkey, 9–11 June 2022, pp. 1–4. New York: IEEE.
22.
HadrougNHafaifaAKouzouA, et al. Dynamic model linearization of two shafts gas turbine via their input/output data around the equilibrium points. Energy2017; 120: 488–497.
23.
ChaibakhshAAmirkhaniS. A simulation model for transient behaviour of heavy-duty gas turbines. Appl Therm Eng2018; 132: 115–127.
24.
TalaatMGobranMHWasfiM. A hybrid model of an artificial neural network with thermodynamic model for system diagnosis of electrical power plant gas turbine. Eng Appl Artif Intel2018; 68: 222–235.
25.
CengelYBolesMKanoğluM. Thermodynamics: an engineering approach, vol. 5. New York: McGraw Hill, 2011.
26.
ThomasP. Simulation of industrial processes for control engineers. Amsterdam: Elsevier Science, 1999.
27.
CelisCPintoPMRBarbosaRS, et al. Modeling of variable inlet guide vanes affects on a one shaft industrial gas turbine used in a combined cycle application. In: Proceedings of the ASME turbo expo 2008: power for land, sea, and air, Berlin, 9–13 June 2009, pp. 1–6. New York: American Society of Mechanical Engineers (ASME).
28.
MolièreM. The fuel flexibility of gas turbines: a review and retrospective outlook. Energies2023; 16: 3962.
29.
BenderWR. Lean pre-mixed combustion. In: SmithLKarimHEtemadSPfefferleWC (eds) The gas turbine handbook. Washington, DC: National Energy Technology Laboratory (NETL), 2006, pp. 217–227.
30.
AdamsJClarkDRLouisJR, et al. Mathematical modeling of once-through boiler dynamics. IEEE T Power Ap Syst1965; 84: 146–156.
31.
LuSHoggBW. Dynamic nonlinear modelling of power plant by physical principles and neural networks. Int J Elec Power2000; 22: 67–78.
32.
MohamedORI. Study of energy efficient supercritical coal-fired power plant dynamic responses and control strategies. PhD Thesis, University of Birmingham, Birmingham, 2012.
33.
BorgnakkeCSonntagRE. Fundamentals of thermodynamics. 8th ed.Hoboken, NJ: John Wiley & Sons, 2012, 912 pp.
34.
InoueTTaniguchiHIkeguchiY. A model of fossil fueled plant with once-through boiler for power system frequency simulation studies. IEEE T Power Syst2000; 15: 1322–1328.
35.
ChaibakhshAGhaffariAMoosavianSAA. A simulated model for a once-through boiler by parameter adjustment based on genetic algorithms. Simul Model Pract Th2007; 15: 1029–1051.
36.
TalahDBentarziH. Frequency control system effectiveness in a combined cycle gas turbine plant. Eng Proc2022; 14: 1.
37.
YuY-N. Electric power system dynamics. New York: Academic Press, 1983, 255 pp.
38.
MachowskiJLubosnyZBialekJW, et al. Power system dynamics: stability and control. Hoboken, NJ: John Wiley & Sons, 2020.
39.
JafariSNikolaidisT. Meta-heuristic global optimization algorithms for aircraft engines modelling and controller design; A review, research challenges, and exploring the future. Prog Aerosp Sci2019; 104: 40–53.
40.
NarueiIKeyniaF. Wild horse optimizer: a new meta-heuristic algorithm for solving engineering optimization problems. Eng Comput2022; 38: 3025–3056.
41.
AbdollahzadehBSoleimanian GharehchopoghFMirjaliliS. Artificial gorilla troops optimizer: a new nature-inspired metaheuristic algorithm for global optimization problems. Int J Intell Syst2021; 36: 5887–5958.
42.
MirjaliliSMirjaliliSMLewisA. Grey Wolf Optimizer. Adv Eng Softw2014; 69: 46–61.
43.
MirjaliliSLewisA. The Whale Optimization Algorithm. Adv Eng Softw2016; 95: 51–67.
44.
KaurSAwasthiLKSangalAL, et al. Tunicate Swarm Algorithm: a new bio-inspired based metaheuristic paradigm for global optimization. Eng Appl Artif Intel2020; 90: 103541.
45.
AbdollahzadehBGharehchopoghFSMirjaliliS. African vultures optimization algorithm: a new nature-inspired metaheuristic algorithm for global optimization problems. Comput Ind Eng2021; 158: 107408.
46.
BraikMHammouriAAtwanJ, et al. White Shark Optimizer: a novel bio-inspired meta-heuristic algorithm for global optimization problems. Knowl-Based Syst2022; 243: 108457.
47.
TeplovBDRadinYAFilinAA, et al. Thermal tests of the SGT5-4000F gas-turbine plant of the PGU-420T power-generating unit at Combined Heat and Power Plant 16 of Mosenergo. Therm Eng2016; 63: 536–543.
