Multi-objective Predictive Control of Gas Turbine System Based on T-S Fuzzy Model

doi:10.11930/j.issn.1004-9649.201909088

Abstract

Abstract: The traditional control strategy of gas turbine system mainly focuses on the load tracking problem without taking the economic performance into full consideration. In this paper, a T-S fuzzy model-based multi-objective predictive controller is proposed to enhance both the tracking performance and economic performance together. First, regarding the strong non-linearities of gas turbine system, an incremental T-S fuzzy structure is applied and model identification is processed based on some historical data from a combined cycle unit. To avoid possible model mismatch, the parameters of the prediction model is updated in realtime according to the current operation conditions. Next, the multi-objective predictive controller is designed in which the load tracking index and economic index are defined and combined into a comprehensive multi-objective cost function. Then, in order to improve the settling speed of load tracking process, the simultaneous heat transfer search algorithm is employed to optimize the cost function and determine the control variables. Simulated experiment results have shown that this multi-objective predictive control scheme could enhance tracking performance and economic performance effectively.

Key words: gas turbine, multi-objective predictive control, T-S fuzzy model, system identification, SHTS

HOU Guolian, DAI Xiaoyan, GONG Linjuan, XU Haixin, ZHANG Jianhua. Multi-objective Predictive Control of Gas Turbine System Based on T-S Fuzzy Model[J]. Electric Power, 2020, 53(11): 212-219,226.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201909088

https://www.electricpower.com.cn/EN/Y2020/V53/I11/212

References

[1] 张金营, 孙蓟光. 基于控制受限MIMO预测控制的超超临界机组集中式协调控制[J]. 中国电力, 2019, 52(5): 21-28
ZHANG Jinying, SUN Jiguang. Centralized coordination control of ultra-supercritical unit based on control-constrained MIMO predictive control[J]. Electric Power, 2019, 52(5): 21-28
[2] 蔡利军, 朱豫才, 吕霞, 等. 模型预测控制在超超临界机组AGC协调控制和主汽温控制中的应用[J]. 中国电力, 2018, 51(7): 68-77
CAI Lijun, ZHU Yucai, LV Xia, et al. MPC applications in AGC CCS and steam temperature control on two ultra-supercritical coal-fired power generation units[J]. Electric Power, 2018, 51(7): 68-77
[3] 钱虹, 陈丹, 杨祖魁, 等. 约束预测控制在低氮燃烧机组过热蒸汽温度控制系统中的应用[J]. 中国电力, 2017, 50(10): 129-135
QIAN Hong, CHEN Dan, YANG Zukui, et al. Application of constrained predictive control in steam temperature control system of low NOx combustion power unit[J]. Electric Power, 2017, 50(10): 129-135
[4] LAWRYNCZUK M. Nonlinear predictive control of a boiler-turbine unit: a state-space approach with successive on-line model linearisation and quadratic optimisation[J]. ISA Transactions, 2017, 67: 476-495.
[5] BONFIGLIO A, CACCIACARNE S, INVERNIZZI M, et al. Gas turbine generating units control via feedback linearization approach[J]. Energy, 2017, 121: 491-512.
[6] WIESE A P, BLOM M J, MANZIE C, et al. Model reduction and MIMO model predictive control of gas turbine systems[J]. Control Engineering Practice, 2015, 45(45): 194-206.
[7] WU X, SHEN J, LI Y G, et al. Flexible operation of post-combustion solvent-based carbon capture for coal-fired power plants using multi-model predictive control: a simulation study[J]. Fuel, 2018, 220: 931-941.
[8] TAN P, XIA J, ZHANG C, et al. Modeling and reduction of NO_X emissions for a 700 MW coal-fired boiler with the advanced machine learning method[J]. Energy, 2016, 94: 672-679.
[9] LIU X J, KONG X B, HOU G L, et al. Modeling of a 1000 MW power plant ultra super-critical boiler system using fuzzy-neural network methods[J]. Energy Conversion and Management, 2013, 65(1): 518-527.
[10] TAKAGI T, SUGENO M. Fuzzy identification of systems and its applications to modeling and control[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1985, 15(1): 116-132.
[11] HOU G, YANG Y, JIANG Z, et al. A new approach of modeling an ultra-super-critical power plant for performance improvement[J]. Energies, 2016, 9(5): 310.
[12] HOU G L, DU H, YANG Y, et al. Coordinated control system modelling of ultra-supercritical unit based on a new T-S fuzzy structure[J]. ISA Transactions, 2018, 74: 120-133.
[13] KLAUČO M, KVASNICA M. Control of a boiler-turbine unit using MPC-based reference governors[J]. Applied Thermal Engineering, 2017, 110: 1437-1447.
[14] KONG X B, LIU X J, LEE K Y. An effective nonlinear multivariable HMPC for USC power plant incorporating NFN-based modeling[J]. IEEE Transactions on Industrial Informatics, 2016, 12(2): 555-566.
[15] 崔靖涵, 刘向杰, 孔小兵. 锅炉-汽轮机系统的模糊经济模型预测控制[J]. 控制理论与应用, 2018, 35(3): 308-316
CUI Jinghan, LIU Xiangjie, KONG Xiaobing. Fuzzy economic predictive control of boiler-turbine system[J]. Control Theory & Applications, 2018, 35(3): 308-316
[16] HOU G L, GONG L J, HUANG C Z, et al. Novel fuzzy modeling and energy-saving predictive control of coordinated control system in 1000 MW ultra-supercritical unit[J]. ISA Transactions, 2019, 86: 48-61.
[17] PATEL V K, SAVSANI V J. Heat transfer search (HTS): a novel optimization algorithm[J]. Information Sciences, 2015, 324: 217-246.
[18] MAHARANA D, KOTECHA P. Simultaneous heat transfer search for computationally expensive numerical optimization[C]//2016 IEEE Congress on Evolutionary Computation (CEC). Vancouver, Canada, 2016.
[19] HOU G L, GONG L J, DAI X Y, et al. A novel fuzzy model predictive control of a gas turbine in the combined cycle unit[J]. Complexity, 2018: 1-18.
[20] CAMPOREALE S M, FORTUNATO B, MASTROVITO M. A modular code for real time dynamic simulation of gas turbines in simulink[J]. Journal of Engineering for Gas Turbines and Power, 2006, 128(3): 506-517.
[21] 富兆龙, 刘志勇, 张琨鹏,等. PG9171E型燃气轮机温度控制分析[J]. 中国电力, 2015, 48(2): 31-37
FU Zhaolong, LIU Zhiyong, ZHANG Kunpeng, et al. Analysis on temperature control for PG9171E gas turbines[J]. Electric Power, 2015, 48(2): 31-37
[22] ARTHUR D, VASSILVITSKⅡ S. k-means++: The advantages of careful seeding[C]//Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics, New Orleans, USA, 2007.
[23] 李少远, 李柠. 复杂系统的模糊预测控制及其应用[M]. 北京: 科学出版社, 2003: 57-65.