شناسایی حلقه بسته سیستم احتراق با استفاده از سیستم استنباط فازی- عصبی تطبیقی بازگشتی و شبکه با ورودی‌های برون‌زا
محورهای موضوعی : مهندسی برق و کامپیوتراحسان آقاداودی 1 , غضنفر شاهقلیان 2 *
1 - دانشگاه آزاد اسلامی واحد نجف آباد
2 - مهندسی برق
کلید واژه: احتراقسیستم استنباط فازی- عصبی تطبیقی بازگشتیبکه با ورودی‌های برون‌زای سری- موازیشناسایی سیستممجزاسازی سیستم,
چکیده مقاله :
بویلر- توربین یک سیستم چندمتغیره و پیچیده در نیروگاه‌های بخار است و از سه حلقه کنترل اصلی و مجزای احتراق، دما و سطح آب درام تشکیل شده است. انتخاب حلقه‌های کنترلی به عنوان یک حلقه واحد به منظور کنترل و شناسایی بویلر به صورت یکپارچه، به علت حضور مشخصه‌های دینامیکی غیر خطی متغیر با زمان بسیار سخت و پیچیده خواهد بود. بنابراین برای تحقق یک مدل واقعی و دقیق برای طراحی کنترل‌کننده مناسب، هر حلقه کنترلی باید جداگانه شناسایی شود. همچنین عملکرد مؤثر و کارامد مدل شناسایی‌شده در زمان تغییرات بار نیز حایز اهمیت است. در این مقاله شناسایی حلقه بسته سیستم احتراق ارائه شده است. با توجه به حساسیت، پیچیدگی، غیر خطی و حلقه بسته بودن سیستم، شناسایی سیستم با استفاده از روش‌های هوشمند مانند سیستم استنباط فازی- عصبی تطبیقی (ANFIS) بازگشتی و شبکه با ورودی‌های برون‌زا (NARX) سری- موازی انجام می‌گیرد. در انتها مقایسه نتایج دو روش با یکدیگر و همچنین مقایسه با داده‌های واقعی نمونه‌برداری شده از بویلر واحد 320 مگاوات نیروگاه بخار اصفهان- ایران ارائه شده و دقت روش‌ها نشان داده می‌شود.
Boiler-turbine is a multi-variable and complicated system in steam power plants including combustion, temperature and drum water level. Selecting control loops as a unique loop in order to identify and control the boiler as a whole unit is a difficult and complicated task, because of nonlinear time variant dynamic characteristics of the boiler. It is necessary to identify each control group in order to accomplish a realistic and effective model, appropriate for designing an efficient controller. Both the effective and efficient performance of the identified model during the load change is of major importance. Here, not all parts of the system should be considered as a unit part, if determining and effective and realistic model is sought. The combustion loop of the 320 MW steam power plant of Islam Abad, Isfahan is the subject. Due to the sensitivity and complexity of the system, with respect to its nonlinear and closed loop characteristics, the identification of the system is conducted through intelligent procedures like recurrent adaptive neuro-fuzzy inference system (RANFIS) and nonlinear autoregressive model with exogenous input (NARX). The comparisons of the findings with actual data collected from the plant are presented and the accuracy of the procedures is determined.
[1] A. Shoulaie, M. Bayati-Poudeh, and G. Shahgholian, "Damping torsional torques in turbine generator shaft by novel PSS based on genetic algorithm and fuzzy logic," J. of Intelligent Procedures in Electrical Technology, vol. 1, no. 2, pp. 3-10, Sep. 2010.
[2] G. Shahgholian, P. Shafaghi, and H. Mahdavinasab, "A comparative analysis and simulation of ALFC in single area system for different turbines," in Proc. of the IEEE Int. Conf. on Information and Automation, ICECT’10, pp. 50-54, Kuala Lumpur, Malaysia, 7-10 May 2010.
[3] W. Ya-Gang, C. Shi-Yuan, C. Wen-Jian, Z. Xi, and L. Xiao-Feng, "Closed-loop identification for boiler-turbine coordinated control system of power unit," in Proc. of the IEEE Int. Conf. on Information and Automation, ICECT’13, pp. 789-793, Siem Reap, Cambodia, 19-21 Jun. 2013.
[4] A. Shoulaie, M. Bayati-Poodeh, G. Shahgholian, and A. H. Zaeri, "Fuzzy logic controller for damping sub-synchronous oscillation in power system," in Proc. of the IEEE Int. Conf. on Information and Automation, ICECT’07, vol. 1, pp. 887-892, Kuala Lumpur, Malaysia, 25-28 Nov. 2007.
[5] Advance Manufacturing Office, Improving Steam System Performance: A Sourcebook for Industry, Department of Energy, Energy Efficiency and Renewable Energy, Washington D.C., 2004.
[6] S. Li, Y. Wang, and Z. Zhao, "Performance evaluation of the boiler combustion control system based on data-driven," in Proc. of the IEEE/ICIA, pp. 2547-2551, 8-10 Aug. 2015.
[7] Z. Lijun, "Design of the boiler temperature measure and controlling system for fuzzy and coupling system," in Proc. of the IEEE 2nd Int. Conf. on Computing, Control and Industrial Engineering, ICECT’11, vol. 1, pp. 26-28, Wuhan, China, 20-21 Aug. 2011.
[8] P. U. Sunil, J. J. Barve, and P. S. V. Nataraj, "Boiler drum-level control using QFT," in Proc. of the IEEE/NUICONE, 6 pp., Ahmedabad, India, 28-30 Nov. 2013.
[9] E. Woodruff, H. Lammers, and T. Lammers, Steam Plant Operation, McGraw-Hill, 9th Edition, 2012 (ISBN: 978-0-07-166796-8).
[10] D. Wang and S. Yuan, "Identification of LPV model for superheated steam temperature system using A-QPSO," Simulation Modelling Practice and Theory, vol. 69, pp. 1-13, Dec. 2016.
[11] H. Kim, et al., "Prediction-based feedforward control of superheated steam temperature of a power plant," International J. of Electrical Power and Energy Systems, vol. 71, pp. 351-357, Oct. 2015.
[12] Q. Z. Al-Hamdan and M. S. Ebaid, "Modeling and simulation of a gas turbine engine for power generation," J. of Engineering for Gas Turbines and Power, vol. 128, no. 2, pp. 302-311, Apr. 2006.
[13] G. Crosa, F. Pittaluga, A. Trucco, F. Beltrami, A. Torelli, and F. Traverso, "Heavy duty gas turbine plant aerothermodynamic simulation using simulink," ASME Gas Turbines and Power, vol. 120, no. 3, pp. 550-556, Jul. 1998.
[14] S. M. Camporeale, B. Fortunato, and A. Dumas, "Dynamic modeling of recuperative gas turbines," in Proc. by IMECH, vol. 1, pp. 213-225, May 2000.
[15] D. Wang, B. Huang, L. Meng, and P. Han, "Predictive control for boiler-turbine unit using ANFIS," in Proc. of the IEEE Conference on Test and Measurement, ICTM’09, vol. 2, 4 pp., Hong Kong, China, 5-6 Dec. 2009.
[16] I. T. Nabney and D. C. Cressy, "Neural network control of a gas turbine," Neural Computing and Applications, vol. 4, no. 4, pp. 198-208, Dec. 1996.
[17] V. Verda and R. Borchiellini, "Exergetic and economic evaluation of control strategies for a gas turbine plant," J. of Energy, vol. 29, no. 12, pp. 2253-2271, Oct. 2004.
[18] M. Basso, L. Giarre, and G. Zappa, "NARX models of an industrial power plant gas turbine," IEEE Trans. on Control Systems Technology, vol. 13, no. 4, pp. 599-604, Jul. 2005.
[19] M. Rahnama, H. Ghorbani, and A. Montazeri, "Nonlinear identification of a gas turbine system in transient operation mode using neural network," in Proc. of the 4th Conf. on Thermal Power, CTPP’12, Plants, 6 pp., Tehran, Iran, 18-19 Dec. 2012.
[20] L. C. Deng, G. J. Wang, and H. Chen, "Fuzzy identification on inverse dynamic process of steam temperature object of boiler," in Proc. of the CSEE, vol. 27, no. 20, pp. 76-80, Jul. 2007.
[21] H. X. Zhu, J. Shen, and Y. G. Li, "A novel dynamic clustering algorithm and its application in fuzzy modeling for thermal processes," in Proc. Int. Confe. on Machine Learning and Cybernetics, pp. 34-40, Dalian, China, 13-16 Aug. 2006.
[22] L. Chaoshun, Z. Jianzhong, L. Qingqing, A. Xueli, and X. Xiang, "A new T-S fuzzy-modeling approach to identify a boiler-turbine system," Expert Systems with Applications, vol. 37, no. 3, pp. 2214-2221, Mar. 2010.
[23] O. M. Mohamed Vall and R. M'hiri, "An approach to polynomial NARX/NARMAX systems identification in a closed-loop with variable structure control," International J. of Automation and Computing, vol. 5, no. 3, pp. 313-318, 2008.
[24] G. R. Ahmadi and D. Toghraie, "Energy and energy analysis of Montazeri Steam Power Plant in Iran," Renewable and Sustainable Energy Reviews, vol. 56, no. 1, pp. 454-463, Apr. 2016.
[25] G. Shahgholian and A. Movahedi, "Coordinated control of TCSC and SVC for system stability enhancement using ANFIS method," International Review on Modelling and Simulations, vol. 4, no. 5, pp. 2367-2375, Oct. 2011.
[26] A. Movahedi, G. Shahgholian, and M. R. Yousefi, "Designing of controllers for non-linear liquid level system with ANFIS method," in Proc. of the 4th Int. Conf. on Computer and Electrical Engineering, vol. 1, pp. 99-103, Singapore, Oct. 2011.
[27] M. Wei, B. Bai, A. H. Sung, Q. Liu, J. Wang, and M. E. Cather, "Predicting injection profiles using ANFIS," Information Sciences, vol. 177, no. 20, pp. 4445-4461, Oct. 2007.
[28] H. Fattahi, "Adaptive neuro fuzzy inference system based on fuzzy c-means clustering algorithm, a technique for estimation of tbm penetration rate," International J. of Optimization in Civil Engineering, vol. 6, no. 2, pp. 159-171, Dec. 2016.
[29] J. C. Bezdec, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.
[30] F. Zhao, L. Hu, and Z. Li, "Nonlinear system identification based on NARX network," in Proc. 10th Asian Control Conf., ASCC’09, pp. 517-525, Kota Kinabalu, Malaysia, 31 May -3 Jun. 2009.
[31] M. Annabestani and N. Naghavi, "Nonlinear identification of IPMC actuators based on ANFIS-NARX paradigm," Sensors and Actuators A: Physical, vol. 209, pp. 140-148, 1 Mar. 2014.
[32] T. P. Vogl, J. K. Mangis, A. K. Rigler, W. T. Zink, and D. L. Alkon, "Accelerating the convergence of the backpropagation method," Biological Cybernetics, vol. 59, no. ¬4-5, pp. 257-263, Sept. 1988.