کنترل مد لغزشی توربین گازی مبتنی بر رویت‌گر تطبیقی غیرخطی
محورهای موضوعی : انرژی های تجدیدپذیر
حامد طبیبی
1
(دانشکده مهندسی برق- واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران)
محسن پارسا
2
(مرکز تحقیقات ریز شبکه های هوشمند- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران)
کلید واژه: توربین گازی, کنترل مد لغزشی, پایداری لیاپانوف, رویت‌گر غیرخطی تطبیقی, محفظه احتراق,
چکیده مقاله :
با توجه به نقش حساس و پر اهمیتی که توربین های گازی در صنایع تولیدی مادر ایفا می کنند، پایش عملکرد آن ها اهمیت بسیار داشته زیرا این امر می تواند در پیش گیری از بروز خسارت های سنگین مالی به صنایع مادر و افزایش عمر مفید توربین ها موثر باشد. یکی از قسمت های مهم توربین های گازی محفظه ی احتراق بوده که پایش وضعیت آن از لحاظ فشار و دما حائز اهمیت است و می تواند به طور مستقیم در عمر مفید توربین تاثیر گذار باشد. اما برخلاف اهمیت بالای کمیت فشار محفظه ی احتراق، امکان اندازه گیری آن توسط سنسور وجود ندارد. بنابراین انتظار می رود که با در اختیار داشتن متغیر فشار تخمین زده شده بتوان به عملکرد و پایداری نسبی بیشتری نسبت به روش هایی که امکان دسترسی به متغیر فشار را ندارد، دست یافت. از این رو در این تحقیق ابتدا یک مدل دینامیکی غیرخطی مناسب با نظر گرفتن خروجی های توان تولیدی و دمای گاز خروجی انتخاب شده است. در ادامه برای تخمین متغیرهای حالت توربین که شامل فشار و دمای محفظه ی احتراق هستند یک رویت گر غیرخطی مبتنی بر روش سطح لغزش تطبیقی طراحی گردیده است. سپس با به کار گیری متغیرهای حالت تخمین زده شده و طراحی کنترل کننده ی مد لغزشی، توان تولیدی و دمای گاز خروجی به مقدار مطلوب همگرا خواهند شد. در این مقاله پایداری سیستم حلقه بسته شامل رویت گر و کنترل کننده به کمک روش لیاپانوف بررسی و تضمین می گردد. در انتها صحت نتایج به کمک شبیه سازی در محیط سیمولینک متلب بررسی خواهد شد.
According to the critical role of gas turbines in the industry, monitoring the performance of gas turbines is an important issue since it can prevent unexpected shutdowns and the serious consequent financial harms. One of the most important parts of a gas turbine is the combustion chamber. Although the internal pressure and temperature of the combustion chamber can directly affect the performance and useful life of this part, however, it is not possible to measure it directly through sensors. Therefore, estimation of pressure variable is a good choice to achieve greater performance and more relative stability comparing with the methods in which there is no access to the internal pressure of the chamber. In this research, a suitable nonlinear dynamic model with produced power and exhausted gas temperature as its outputs is chosen. Thereafter, an adaptive surface sliding observer is designed in order to estimate the combustion pressure and temperature which are the state variables of the gas turbine. Afterward, utilizing a sliding mode controller and applying the estimated states, the produced power and exhaustion gas temperature of the gas turbine is controlled. In this paper, the stability of the closed-loop system in the presence of the state observer through the Lyapunov approach is guaranteed. Finally, simulation results are provided to verify the validity and efficiency of the proposed method.
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[19] Z. Gao, X. Dai, T. Breikin, H. Wang, “Novel parameter identification by using a high-gain observer with application to a gas turbine engine”, IEEE Trans. On Industrial Informatics, vol. 4, no. 4, Nov. 2008 (doi: 10.1109/TII.2008.2007802).
[20] H. Lee, S. Snyder, N. Hovakimyan, “An adaptive unknown input observer for fault detection and isolation of aircraft actuator faults”, Proceeding of the AIAA, pp. 1-8, National Harbor, Maryland, Jan. 2014 (doi: 10.2514/6.2014-0266).
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[22] R. Franco, H. Ríos, D. Efimov, W. Perruquetti, “Adaptive estimation for uncertain nonlinear systems: A sliding-mode observer approach”, Proceeding of the IEEE/CDC, pp. 5506-5511, Miami Beach, FL, USA, Dec. 2018 (doi: 10.1109/CDC.2018.8619104).
_||_[1] G. G. Kulikov, H. A. Thompson, Dynamic Modelling of Gas Turbines, Identification, Simulation, Condition Monitoring and Optimal Control, Springer-Verlag, London, 2004, (doi: 10.1007/978-1-4471-3796-2).
[2] M.P. Boyce, Gas turbine engineering handbook, Hand Book, Elsevier, Fourth Edition, 2011 (doi: 10.1016/C2009-0-64242-2).
[3] Q.Z. Al-Hamdan, M.S.Y. Ebaid, “Modeling and simulation of a gas turbine engine for power generation”, ASME. Journal of Engineering Gas Turbines Power, vol. 128, no. 2, pp. 302-311, Apr. 2006 (doi: 10.1115/1.2061287).
[4] O.D. Lyantsev, A.V. Kazantsev, A.I. Abdulnagimov, “Identification method for nonlinear dynamic models of gas turbine engines on acceleration mode”, Procedia Engineering, vol. 176, pp. 409-415, 2017 (doi: 10.1016/j.proeng.2017.02.339).
[5] M. Amozegar, K. Khorasani, “An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines”, Neural Networks, vol. 26, pp. 106-121, April 2016 (doi: 10.1016/j.neunet.2016.01.003).
[6] J. Mu, D. Rees, “Approximate model predictive control for gas turbine engines”, Proceeding of the IEEE/ACC, vol. 6, pp. 5704-5709, Boston, MA, USA, July 2004 (doi: 10.23919/ACC.2004.1384765).
[7] B. J. Brunell, R.R. Bitmead, A.J. Connolly, “Nonlinear model predictive control of an aircraft gas turbine engine”, Proceedings of the IEEE/CDC, pp. 4649-4651, Las Vegas, Nevada USA, Dec. 2002 (doi: 10.1109/CDC.2002.1185111).
[8] T.S. Pires, M E. Cruz, M.J. Colaco, M.A C. Alves, “Application of nonlinear multivariable model predictive control to transient operation of a gas turbine and nox emissions reduction”, Energy, vol. 149, pp. 341-353, April 2018 (doi: 10.1016/j.energy.2018.02.042).
[9] S. H. Mousavi, A. Azizi, H. Nourisola, “Decentralized multivariable PID controller with pre compensator for gas turbine system”, Proceeding of the IEEE/KBEL, Tehran, Iran, Dec 2017 (doi: 10.1109/KBEI.2017.8324918).
[10] A. Bonfiglio, S. Cacciacarne, M. Invernizzi, R. Procopio, S. Schiano, I. Torre, “Gas turbine generating units control via feedback linearization approach”, Energy, vol. 121, pp. 491-512, Feb. 2017 (doi: 10.1016/j.energy.2017.01.048).
[11] P. Ailer, B. Pongracz, G. Szederkenyi, “Constrained control of a low power industrial gas turbine based on input-output linearization”, Proceeding of the IEEE/ICCA, Budapest, Hungary, June 2005 (doi: 10.1109/ICCA.2005.1528147).
[12] B. Yu, C. Cao, W. Shu, Z. Hu, “A new method for the design of optimal control in the transient state of a gas turbine engine”, IEEE Access, vol. 5, pp. 23848-23857, Oct. 2017 (doi: 10.1109/ACCESS.2017.2764056).
[13] A. Hafaifa, A. Benyounes, M. Guemana, “Control of an industrial gas turbine based on fuzzy model”, Proceeding of the IFAC, Sozopol, Bulgaria, Sept. 2015 (doi: 10.1186/s40929-017-0017-8).
[14] G. Hou, L. Gong, X. Dai, M. Wang, C. Huang, “A novel fuzzy model predictive control of a gas turbine in the combined cycle unit”, Hindawi Complexity, vol. 2018, no. 1, pp. 1-18, 2018 (doi: 10.1155/2018/6468517).
[15] E. Najimi, M.H. Ramezani, “Robust control of speed and temperature in a power plant gas turbine”, ISA Transaction, vol. 51, no. 2, pp. 304-308, March 2012, (doi: 10.1016/j.isatra.2011.10.001).
[16] S.M. Camporeale, L. Dambrosio, B. Fortunato, “One-step-ahead adaptive control for gas turbine power plants”, Journal of Dynamic Systems Measurement and Control, vol. 124, pp. 341-348, 2002 (doi: 10.1115/99-GT-062).
[17] S. A. Shete, V. S. Jape, “Design of a fuzzy modified model reference adaptive controller for a gas turbine rotor speed control using T-S fuzzy mechanism”, Proceeding of the IEEE/ TAPENERGY, Kollam, India, June 2018 (doi: 10.1109/TAPENERGY.2017.8397207).
[18] A. Bonfiglio, S. Cacciacarne, M. Invernizzi, D. Lanzarotto, A. Palmieri, R. Procopio, “A sliding mode control approach for gas turbine power generators”, IEEE Trans. on Energy Conversion, vol. 34, no. 2, pp. 921-932, June 2019 (doi: 10.1109/TEC.2018.2879688).
[19] Z. Gao, X. Dai, T. Breikin, H. Wang, “Novel parameter identification by using a high-gain observer with application to a gas turbine engine”, IEEE Trans. On Industrial Informatics, vol. 4, no. 4, Nov. 2008 (doi: 10.1109/TII.2008.2007802).
[20] H. Lee, S. Snyder, N. Hovakimyan, “An adaptive unknown input observer for fault detection and isolation of aircraft actuator faults”, Proceeding of the AIAA, pp. 1-8, National Harbor, Maryland, Jan. 2014 (doi: 10.2514/6.2014-0266).
[21] S. Rahme, N. Meskin, “Adaptive sliding mode observer for sensor fault diagnosis of an industrial gas turbine”, Control Engineering, vol. 38, pp. 57-74, May 2015, (doi: 10.1016/j.conengprac.2015.01.006).
[22] R. Franco, H. Ríos, D. Efimov, W. Perruquetti, “Adaptive estimation for uncertain nonlinear systems: A sliding-mode observer approach”, Proceeding of the IEEE/CDC, pp. 5506-5511, Miami Beach, FL, USA, Dec. 2018 (doi: 10.1109/CDC.2018.8619104).
