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Thermal Engineering

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01-10-2022 | AUTOMATION AND HEAT CONTROL IN POWER ENGINEERING

Main Steam Temperature Control Based on Variable Universe Fuzzy Dynamic Matrix Control

Author: Lian Lian

Published in: Thermal Engineering | Issue 10/2022

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Abstract

The main steam temperature is one of the important parameters that need real-time monitoring in the thermal process, which is directly related to the power generation efficiency and operation safety of the boiler. The main steam temperature is also one of the most difficult parameters to control because of its large inertia and nonlinearity. Based on the Fuzzy-DMC controller, the variable universe fuzzy dynamic matrix control (VU Fuzzy-DMC) controller is designed by introducing the idea of variable universe. By adjusting the scaling factor, the universe can be contracted or expanded with the error decreasing or increasing, so as to improve the adaptive ability and control accuracy of the fuzzy controller. The simulation results show that in the case of model matching and model mismatching¹, the variable universe VU Fuzzy-DMC controller has better control effect than PID-, DMC- and Fuzzy-DMC-control, greatly reduces the temperature overshoot and regulation time, and the system has strong robustness. The variable universe Fuzzy-DMC controller can quickly return to the set value with small overshoot after being disturbed, and the system has a good anti-interference performance. The designed control strategy is effective.

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Y. Ishiwatari, C. H. Peng, S. Ikejiri, and N. Oka, “Improvements of feedwater controller for the super fast reactor,” J. Nucl. Sci. Technol. 47, 1155–1164 (2010). https://doi.org/10.1080/18811248.2010.9720982CrossRef

C. Polton and E. Boje, “Quantitative feedback theory design of valve position control for co-ordinated superheater control of main steam temperatures of power plant boilers,” IFAC-PapersOnLine 53, 13070–13075 (2020). https://doi.org/10.1016/j.ifacol.2020.12.2255CrossRef

Z. Tian, Y. Ren, and G. Wang, “Fuzzy-PID controller based on variable universe for main steam temperature system,” Aust. J. Electr. Electron. Eng. 15, 21–28 (2018). https://doi.org/10.1080/1448837X.2018.1490163CrossRef

Z. Lv and Y. Zhang, “Application of fuzzy control based on time series prediction algorithm in main steam temperature system,” in Proc. 2018 Chinese Automation Congr., Xi’an, China, Nov. 30–Dec. 2, 2018 (IEEE, Piscataway, N. J., 2018). https://doi.org/10.1109/CAC.2018.8623673

T. Wang, C. Y. Han, and C. J. Jiang, “Design and simulation of main steam temperature controller based on interval type-2 fuzzy logic systems,” ICIC Express Lett., Part B: Appl. 4, 1359–1365 (2013).

H. Mi, C. Peng, and C. Cheng, “Modeling of main steam temperature using an improved Fuzzy particle swarm optimization algorithm,” in Recent Featured Applications of Artificial Intelligence Methods: Proc. LSMS 2020 and ICSEE 2020 Workshops, Shanghai, China, Oct. 25, 2020, Ed. by M. Fei, K. Li, Z. Yang, Q. Niu, and X. Li (Springer, Singapore, 2020), in Ser.: Communications in Computer and Information Science, Vol. 1303, pp. 123–136. https://doi.org/10.1007/978-981-33-6378-6_10

B. Hozifa, F.-E. Osman, H.-E. I. Yaser, M. H. Eltaher, and M. Dalia, “Superheated steam temperature control using fuzzy logic controller,” Am. Sci. Res. J. Eng., Technol. Sci. (ASRJETS) 17, 34–41 (2016).

T. Nahlovsky, “Optimization of fuzzy controller parameters for the temperature control of superheated steam,” Procedia Eng. 100, 1547–1555 (2015). https://doi.org/10.1016/j.proeng.2015.01.527CrossRef

Z. Tian, “Main steam temperature control based on GA-BP optimised fuzzy neural network,” Int. J. Eng. Syst. Modell. Simul. 9, 150–160 (2017). https://doi.org/10.1504/IJESMS.2017.085057CrossRef

10.

Z. Yong and J. Dang, “Application of PSO-BP neural network in main steam temperature control,” in Proc. 28th Chinese Control and Decision Conf., Yinchuan, China, May 28–30, 2016 (IEEE, Piscataway, N. J., 2016), 5607–5611. https://doi.org/10.1109/ccdc.2016.7532000

11.

Y. Sun, J. Gao, H. Zhao, D. Peng, and Liqin, “The application of BPNN based on improved PSO in main steam temperature control of supercritical unit,” in Proc. 22nd Int. Conf. on Automation and Computing, Colchester, U.K., Sept. 7–8, 2016 (IEEE, Piscataway, N. J., 2016), pp. 188–192. https://ieeexplore.ieee.org/document/7604916

12.

N. A. Mazalan, A. A. Malek, M. A. Wahid, and M. Mailah, “Sensitivity analysis on neural network algorithm for primary superheater spray modeling,” Heat Transfer Eng. 38, 417–422 (2017). https://doi.org/10.1080/01457632.2016.1195134CrossRef

13.

A. M. Naseri and A. Yazdizadeh, “Neural network-based IMC-PID controller design for main steam temperature of a power plant,” in Proc. 6th Int. Symp. on Neural Networks, Wuhan, China, May 26–29, 2009 (Springer, Berlin, 2009), pp. 1059–1068. https://doi.org/10.1007/978-3-642-01510-6_120

14.

D. Wang, X. Wu, and J. Shen, “An efficient robust predictive control of main steam temperature of coal-fired power plant,” Energies 13, 3775 (2020). https://doi.org/10.3390/en13153775CrossRef

15.

G.-L. Wang, W.-W. Yan, S. Chen, and H.-H. Shao, “Multivariable constrained predictive control of main steam temperature in ultra-supercritical coal-fired power unit,” J. Energy Inst. 88, 181–187 (2015). https://doi.org/10.1016/j.joei.2014.06.003CrossRef

16.

Z. Tian, S. Li, and Y. Wang, “Generalized predictive PID control for main steam temperature based on improved PSO algorithm,” J. Adv. Comput. Intell. Intell. Inf. 21, 507–517 (2017). https://doi.org/10.20965/jaciii.2017.p0507CrossRef

17.

H. Hu, Y. Li, Q. Yang, and Y. Cai, “Combined moving horizon estimation and model predictive control for main steam temperature system,” in Proc. of the 36th Chinese Control Conf. (CCC), Dalian, China, July 26–28, 2017 (IEEE, Piscataway, N. J., 2017), pp. 3134–3139. https://doi.org/10.23919/ChiCC.2017.8027840

18.

P. Sindareh-Esfahani, S. S. Tabatabaei, and J. K. Pieper, “Model predictive control of a heat recovery steam generator during cold start-up operation using piecewise linear models,” Appl. Therm. Eng. 119, 516–529 (2017). https://doi.org/10.1016/j.applthermaleng.2017.03.041CrossRef

19.

A. Sanchez-Lopez, G. Arroyo-Figueroa, and A. Villavicencio-Ramirez, “Advanced control algorithms for steam temperature regulation of thermal power plants,” Int. J. Electr. Power Energy Syst. 26, 779–785 (2004). https://doi.org/10.1016/j.ijepes.2004.08.003CrossRef

20.

J. Mathew, S. S. Shankar, H. Pratheesh, R. B. Singh, C. S. Lajitha, and I. A. Muhammed, “Implementation of high availability SCADA system for superheater steam temperature control in a 210 MW thermal power plant,” in Proc. 2014 IEEE Int. Conf. on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India, Jan. 6–7, 2014 (IEEE, Piscataway, N. J., 2014). https://doi.org/10.1109/CONECCT.2014.6740354

21.

R. R. Pérez, A. Geddes, and A. Clegg, “Adaptive predictive expert control of superheated steam temperature in a coal-fired power plant,” Int. J. Adapt. Control Signal Process 26, 932–944 (2012). https://doi.org/10.1002/acs.2318CrossRefMATH

22.

G. Hou, M. Wang, L. Gong, and J. Zhang, “Parameters optimization of ADRC based on adaptive CPSO algorithm and its application in main-steam temperature control system,” in Proc. 13th IEEE Conf. on Industrial Electronics and Applications, Wuhan, China, May 31–June 2, 2018 (IEEE, Piscataway, N. J., 2018), pp. 497–501. https://doi.org/10.1109/ICIEA.2018.8397768

23.

L. Wei and Z. Junmin, “Particle swarm optimization PID neural network control method in the main steam temperature control system,” in Proc. 2012 Int. Conf. on Computer Science and Electronics Engineering, Hangzhou, China, Mar. 23–25, 2012 (IEEE, Piscataway, N.J., 2012), Vol. 2, pp. 137–140. https://doi.org/10.1109/ICCSEE.2012.289

24.

Z. Keliang and Q. Jieqiong, “PID controller parameters tuning of main steam temperature based on chaotic particle swarm optimization,” in Proc. 2011 IEEE Int. Conf. on Computer Science and Automation Engineering, Shanghai, China, 10–12 June 2011 (IEEE, Piscataway, N. J., 2011), pp. 647–650. https://doi.org/10.1109/CSAE.2011.5952759

25.

A. Rezaie, G. Tsatsaronis, and U. Hellwig, “Thermal design and optimization of a heat recovery steam generator in a combined-cycle power plant by applying a genetic algorithm,” Energy 168, 346–357 (2019). https://doi.org/10.1016/j.energy.2018.11.047CrossRef

26.

S. B. Savargave and M. J. Lengare, “Self-adaptive firefly algorithm with neural network for design modelling and optimization of boiler plants,” in Proc. 2017 Int. Conf. on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), Palladam, India, Feb. 10–11, 2017 (IEEE, Piscataway, N. J., 2017), pp. 289–293. https://doi.org/10.1109/I-SMAC.2017.8058357

27.

C. Guang, Q. Feng, and D. Keqin, “Methods and systems for high-temperature strain measurement of the main steam pipe of a boiler of a power plant while in service,” J. Opt. Soc. Korea 20, 770–777 (2016). https://doi.org/10.3807/JOSK.2016.20.6.770CrossRef

28.

C. Cheng, C. Peng, D. Zeng, and T. Zhang, “Modeling of main steam temperature using an improved fuzzy genetic algorithm,” in Proc. 39th Chinese Control Conf., Shenyang, China, 27–29 July 2020 (IEEE, Piscataway, N. J., 2020), pp. 1196–1202. https://doi.org/10.23919/CCC50068.2020.9188991

29.

T. Klopot, P. Skupin, P. Grelewicz, and J. Czeczot, “Practical PLC-based implementation of adaptive dynamic matrix controller for energy-efficient control of heat sources,” IEEE Trans. Ind. Electron. 68, 4269–4278 (2021). https://doi.org/10.1109/TIE.2020.2987272CrossRef

30.

Z. Tian, “Networked control system time-delay compensation based on PI-based dynamic matrix control,” at-Automatisierungstechnik 69, 41–51 (2021). https://doi.org/10.1515/auto-2020-0020

31.

U.-C. Moon and K. Y. Lee, “An adaptive dynamic matrix control with Fuzzy-interpolated step-response model for a drum-type boiler-turbine system,” IEEE Trans. Energy Convers. 26, 393–401 (2021). https://doi.org/10.1109/TEC.2011.2116023CrossRef

32.

Z. Cao and S. Zheng, “MR-SAS and electric power steering variable universe fuzzy PID integrated control,” Neural Comput. Appl. 31, 1249–1258 (2019). https://doi.org/10.1007/s00521-017-3157-727CrossRef

33.

D. Hu, T. Jiang, and X. Yu, “Construction of non-convex fuzzy sets and its application,” Neurocomputing 393, 175–183 (2020). https://doi.org/10.1016/j.neucom.2018.10.111CrossRef

Title: Main Steam Temperature Control Based on Variable Universe Fuzzy Dynamic Matrix Control
Author: Lian Lian
Publication date: 01-10-2022
Publisher: Pleiades Publishing
Published in: Thermal Engineering / Issue 10/2022
Print ISSN: 0040-6015
Electronic ISSN: 1555-6301
DOI: https://doi.org/10.1134/S0040601522100044

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