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
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摘要
当前超超临界机组协调系统采用的控制方案在变负荷过程中不能将主汽压力与中间点温度同时很好的控制在设计值附近,这严重影响机组安全性、经济性。基于控制受限MIMO(multiple input multiple output)预测控制的集中式协调控制方案在不解耦、无前馈的情况下即可解决MIMO系统的强耦合、大迟延问题。首先给出了控制受限MIMO预测控制的算法,然后在MATLAB中仿真实现。仿真结果表明,集中式协调控制方案与传统PID控制方案在功率具有基本相同跟踪性能的情况下,主汽压力与中间点温度的跟踪性能更优、鲁棒性更强。
With the traditional control scheme the main steam pressure and the steam temperature of intermediate point cannot be regulated around the target settings when the load of the unit is changing, which will cause severe impacts on the security and efficiency of the unit operation. In this paper, the centralized coordination control scheme based on control-constrained MIMO(multiple input multiple output) predictive control is proposed to resolve the strong coupling and large time delay problems in the MIMO system without extra procedures of decoupling and feedforward. Firstly, the algorithm of control-constrained MIMO predictive control is explored and then prototyped using MATLAB. From the simulation results the centralized coordination control scheme demonstrated comparable tracking performance in power output to the traditional PID control scheme. Nevertheless it outperforms the latter scheme in the aspect of the tracking performance of the main steam pressure and the steam temperature of intermediate point as well as its robustness.
With the traditional control scheme the main steam pressure and the steam temperature of intermediate point cannot be regulated around the target settings when the load of the unit is changing, which will cause severe impacts on the security and efficiency of the unit operation. In this paper, the centralized coordination control scheme based on control-constrained MIMO(multiple input multiple output) predictive control is proposed to resolve the strong coupling and large time delay problems in the MIMO system without extra procedures of decoupling and feedforward. Firstly, the algorithm of control-constrained MIMO predictive control is explored and then prototyped using MATLAB. From the simulation results the centralized coordination control scheme demonstrated comparable tracking performance in power output to the traditional PID control scheme. Nevertheless it outperforms the latter scheme in the aspect of the tracking performance of the main steam pressure and the steam temperature of intermediate point as well as its robustness.
引文
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[17] HOU Guolian, DU Huan, YANG Yu, 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.
[18] SUN Fengrui, YAO Yuedong, LI Xiangfang, et al. A numerical model for predicting distributions of pressure and temperature of superheated steam in multi-point injection horizontal wells[J].International Journal of Heat and Mass Transfer, 2018, 121:282-289.
[19] PENG Hui, KITAGAWA Genshiro, WU Jun, et al. Multivariable RBF-ARX model-based robust MPC approach and application to thermal power plant[J]. Applied Mathematical Modelling, 2011,35(7):3541-3551.
[20] ZHOU Hong, CHEN Cheng, LAI Jingang, et al. Affine nonlinear control for an ultra-supercritical coal fired once-through boilerturbine unit[J]. Energy, 2018, 153:638-649.
[21] MUNJE Ravindra, LIN Shuyi, ZHANG Guoqing, et al. ObserverBased output feedback integral control for coal-fired power plant:A three-time-scale perspective[J]. IEEE Transactions on Control Systems Technology,2018, 99:1-8.
[22] WANG Di, ZHOU Yunlong, LI Xiaoli. A dynamic model used for controller design for fast cut back of coal-fired boiler-turbine plant[J].Energy, 2018, 144:526-534.
[23] YANG Hang, ZHANG Yongxin, ZHENG Chenghang, et al. Energy consumption and energy-saving potential analysis of pollutant abatement systems in a 1000MW coal-fired power plant[J]. Journal of the Air&Waste Management Association, 2018, 68(9):920-930.
[2]刘久刚.超临界600 MW机组协调控制系统的研究[D].北京:华北电力大学,2012.
[3]王智燕.超临界机组协调系统神经网络逆控制的仿真研究[D].保定:华北电力大学,2014.
[4]杨明花.面向先进热工控制策略优化的热控仿真调试装置[J].中国电力,2017, 50(8):48-52.YANG Minghua. The research and application of thermal control simulation test device for optimization of advanced thermal control scheme[J]. Electric Power, 2017, 50(8):48-52.
[5]曹楠.多变量广义预测控制在单元机组协调控制系统中的应用研究[D].北京:华北电力大学,1999.
[6]胡武奇.600 MW超临界锅炉给水控制系统研究及应用[D].上海:上海交通大学,2008.
[7]宫广正.超临界火电机组运行灵活性提升控制策略研究及应用[J].中国电力,2017, 50(8):22-26.GONG Guangzheng. Research on and application of the control strategy for flexibility inprovement of supercritical fossil-fired power units[J]. Electric Power,2017, 50(8):22-26.
[8]张建华,侯国莲,李农庄,等.电厂过热汽温神经网络内模控制系统的仿真研究[J].现代电力,2000, 17(2):19-24.ZHANG Jianhua, HOU Guolian, LI Nongzhuang, et al. A simulating study on superheated steam temperature control system of a power plant based on NN&IMC[J]. Modern Electric Power, 2000, 17(2):19-24.
[9]姚峻,高磊,陈维和,等.900 MW超临界机组协调控制及AGC策略的研究与应用[J].中国电力,2005,38(8):62-65.YAO Jun, GAO Lei, CHEN Weihe, et al. Research and application of coordinated control and AGC strategy in 900 MW supercritical unit[J]. Electric Power, 2005, 38(8):62-65.
[10]张建华,侯国莲,李农庄,等.基于内模控制的再热汽温控制系统[J].现代电力,1999, 16(1):7-12.ZHANG Jianhua, HOU Guolian, LI Nongzhuang, et al. Control system of reheating temperature process based on internal model control[J]. Modern Electric Power, 1999, 16(1):7-12.
[11]李益国,沈炯,吕震中.火电单元机组负荷模糊内模控制及其仿真研究[J].中国电机工程学报,2002, 22(4):90-93.LI Yiguo, SHEN Jiong, LV Zhenzhong. Fuzzy internal model control on the load system of a thermal power unit and its simulating study[J]. Proceedings of the CSEE, 2002, 22(4):90-93.
[12]华志刚,胡光宇,吴志功,等.基于先进控制技术的机组优化控制系统[J].中国电力,2013, 46(6):10-15,21.HUA Zhigang, HU Guangyu, WU Zhigong, et al. Optimal control cystem based on advanced control technologies[J]. Electric Power,2013, 46(6):10-15, 21.
[13]张秋生,梁华,胡晓花,等.超超临界机组的两种典型协调控制方案[J].中国电力,2011,44(10):74-79.ZHANG Qiusheng, LIANG Hua, HU Xiaohua, et al. Two kinds of typical coordinated control systems in ultra-supercritical units[J].Electric Power, 2011, 44(10):74-79.
[14]赵猛.多变量系统控制输入受限的约束预测控制[J].石油化工高等学校学报,2003, 16(3):66-69.ZHAO Meng. Constrained predictive control for multivariable systems subject to input constraints[J]. Journal of Petrochemical Universities, 2003, 16(3):66-69.
[15]席裕庚.预测控制[M].北京:国防工业出版社,1991.
[16] LI Xiangjun, YAN Heming. Fuzzy logic-based coordinated control method for multi-type battery energy storage systems[J]. Artificial Intelligence Review, 2018, 49(10):1-17.
[17] HOU Guolian, DU Huan, YANG Yu, 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.
[18] SUN Fengrui, YAO Yuedong, LI Xiangfang, et al. A numerical model for predicting distributions of pressure and temperature of superheated steam in multi-point injection horizontal wells[J].International Journal of Heat and Mass Transfer, 2018, 121:282-289.
[19] PENG Hui, KITAGAWA Genshiro, WU Jun, et al. Multivariable RBF-ARX model-based robust MPC approach and application to thermal power plant[J]. Applied Mathematical Modelling, 2011,35(7):3541-3551.
[20] ZHOU Hong, CHEN Cheng, LAI Jingang, et al. Affine nonlinear control for an ultra-supercritical coal fired once-through boilerturbine unit[J]. Energy, 2018, 153:638-649.
[21] MUNJE Ravindra, LIN Shuyi, ZHANG Guoqing, et al. ObserverBased output feedback integral control for coal-fired power plant:A three-time-scale perspective[J]. IEEE Transactions on Control Systems Technology,2018, 99:1-8.
[22] WANG Di, ZHOU Yunlong, LI Xiaoli. A dynamic model used for controller design for fast cut back of coal-fired boiler-turbine plant[J].Energy, 2018, 144:526-534.
[23] YANG Hang, ZHANG Yongxin, ZHENG Chenghang, et al. Energy consumption and energy-saving potential analysis of pollutant abatement systems in a 1000MW coal-fired power plant[J]. Journal of the Air&Waste Management Association, 2018, 68(9):920-930.