电站锅炉制粉系统的仿真建模

依据电厂锅炉的中间储仓式制粉系统 ,建立仿真模型来模拟实际系统的运行过程 ,经测试 ,模型运行结果基本上与现场运行相吻合。此建模方法为复杂大系统的仿真提供了一个理想的建模途径     

基于CARIMA模型的自适应预测函数控制在锅炉燃烧系统中的应用

针对目前火电厂锅炉燃烧控制系统的大滞后、强耦合、变工况等突出问题,提出了一种基于卡尔曼(CARIMA)模型的自适应预测函数控制方法.该方法先利用预测模型得到系统未来时刻输出,然后将设定输出值和预测值间的预测误差变化率作为自适应控制器的输入,控制器利用最小二乘算法推理得到控制输出.当被控对象模型参数未知或渐时变时,该方法通过实时辨识过程模型的参数来实时修正预测函数控制器的参数,这样可以进一步提高预测函数控制方法的控制品质,提高锅炉的燃烧效率.仿真实验表明,自适应预测函数控制是一种计算简单、鲁棒性和适应性较强、控制精度高的控制方法.     

火电厂制粉系统爆燃原因分析及预防措施

新疆某电厂在冬季连续发生制粉系统爆燃问题，直接影响了该厂正常的生产工作。针对其制粉系统爆燃问题的试验分析和理论研究，阐述电站锅炉设计，改造以及运行方面的一些经验教训。     

火电厂球磨机制粉系统的自调整模糊控制

火电厂球磨机制粉系统是一个典型的3输入－3输出系统。其输入为再循环风门开度、热风门开度、给煤机转速,输出为磨入口负压、磨出口温度、磨前后差压。各变量之间存在严重耦合,利用PID控制很难达到理想效果。针对这些特点,提出了一种自调整模糊控制算法。该算法充分考虑了各变量之间的耦合关系,利用模型控制公式进行了有效解耦,并且可根据运行状况自动调整各变量的偏差及其变化率在算法中所占比重。对各种非正常工况采用不同控制方案,有效预防了正压跑粉、堵煤等各种故障的发生。仿真研究及现场实际应用表明,该算法运算简单,不涉及复杂的模糊控制规则,易于工程实现,用于实际电厂取得满意的控制效果。     

制粉系统球磨机控制方法综述

先进控制理论及计算机技术的发展,使得制粉系统球磨机这一较难控制的系统开始受到重视,并取得了许多理论和应用方面的研究成果。文章概述了解耦控制、自寻优控制、模糊控制、神经元控制等先进控制方法在这一系统中的研究和应用现状及发展前景。     

球磨机中储式制粉系统的数学模型与仿真

根据球磨机中储式制粉系统的物理过程机理，建立了该系统的动态数学模型，并利用模块化建模技术，对ＤＴＭ３５０／６００－Ⅲ型滚筒式钢球磨煤机组成的制粉系统建立了仿真模型。该模型已成功地应用于西北电业管理局３００ＭＷ机组全范围仿真机工程。     

电厂锅炉制粉系统优化调整试验

针对湛江电力有限公司3号锅炉制粉系统运行中存在的出力低、煤粉粗等问题,进行了制粉系统优化调整试验。给出了试验参数测量及计算方法,说明了优化调整试验内容与过程,对试验结果进行了相关分析,并采取了相应的对策,最后确定制粉系统最佳的运行方案。实践证明:研究成果对煤种变化适应性强,能够保证锅炉在安全运行情况下进行配煤燃烧,大大降低了发电成本,提高了运行的经济性。     

锅炉水位控制系统的串级广义预测控制

针对火电厂锅炉汽包水位对象的复杂非线性动态特性,为提高水位系统控制的可靠性和安全性,设计串级广义预测控制(CGPC)结构。内回路采用PID控制可以快速消除给水流量的扰动,外回路采用具有滚动优化和反馈校正功能的CGPC控制结构,有效克服了蒸汽流量的扰动。仿真结果表明,应用该方法得到的CGPC-PID控制系统具有较高的预测精度和良好的抗扰动性,提高了系统的静态和动态性能指标,CGPC-PID串级控制系统控制性能优于常规PID-PID串级控制系统。     

电厂锅炉制粉系统塞煤问题的分析及解决

分析了锅炉煤仓塞煤的原因和对锅炉运行的影响,指出了现有的清堵方法和措施存在的实际问题。考虑到某些自备电厂采用煤仓疏松机取得的效果,在借鉴其成功经验的基础上,对这一设备加以改进和推广应用。以中速磨正压直吹制粉系统为例,通过引入锅炉负荷信号,采取提前预判的控制策略,配合一种专门解决煤仓塞煤问题的新型专利产品——煤仓疏松机,不仅能从根本上解决煤仓塞煤问题,而且能够保证调节系统的灵敏度及疏松器的安全强度。     

多变量系统的广义预测控制解耦设计

针对双输入双输出机炉协调控制系统,首先将系统分解为两个双输入单输出系统,并用广义预测控制原理设计控制器,通过求解二元一次矩阵方程组,实现了多频预测的解耦控制。进行了仿真研究,仿真结果证实了算法的有效性。     

单元机组协调系统的模型预测控制

单元机组中的锅炉-汽轮机协调系统,具有滞后、耦合、时变以及模型不确定性等特点。针对该协调过程,设计了一种预测控制策略,它利用预测模型与滚动优化策略,减小了模型不确定性、时变以及滞后等因素的影响。最后通过300 MW单元机组协调控制系统的仿真实验,验证了所设计控制算法的性能。结果表明在机组工况发生变化或受到干扰时,被控系统具有快速的负荷适应性、良好的抗扰动能力和很强的鲁棒性,是一种具有实用价值的单元机组协调控制方法。     

Comparison between various control methods for thermal power plant main control system

In thermal power plants, it is important to improve the control accuracy of main steam pressure and temperature and so forth during load up/down. This paper focuses on temperature control, the most difficult aspect of control due to the nonlinearity and long dead time of power plants. We applied control methods such as MRAC, neural network, and long‐range predictive control to the power plant main control system. Each method was evaluated by a simulator using detailed physical models that represent accurately the power plant dynamics. We confirmed that each method can provide proper control, but long‐range predictive control is better than the two other methods. In addition, thermal power plants are so complex that further analysis (e.g., persistently exciting condition, learning method of neural networks) is necessary for the application of theoretical algorithms. © 1999 Scripta Technica, Electr Eng Jpn, 128(3): 72–81, 1999     

中速磨煤机制粉系统运行中的误区分析

在中速磨煤机制粉系统运行中，普遍存在着靠磨煤机入口风量挡板调整磨煤机通风量的大小．使磨煤机入口风量挡板开度很小，造成一次风压过高，一次风机的部分压能损失在挡板节流上。浪费了制粉电耗．这是中速磨煤机制粉系统运行中的一个误区。通过理论分析和对绥中发电厂800MW机组锅炉的试验实例．说明了中速磨煤机制粉系统正确运行方式，即在锅炉运行中应适当开大磨煤机入口挡板，将磨煤机入口挡板都控制在60％-65％，甚至可再开一些，以纠正中速磨煤机制粉系统运行中存在的误区，降低制粉系统电耗，提高机组经济性．     

Model predictive control of grid-connected PV power generation system considering optimal MPPT control of PV modules

Because of system constraints caused by the external environment and grid faults, the conventional maximum power point tracking (MPPT) and inverter control methods of a PV power generation system cannot achieve optimal power output. They can also lead to misjudgments and poor dynamic performance. To address these issues, this paper proposes a new MPPT method of PV modules based on model predictive control (MPC) and a finite control set model predictive current control (FCS-MPCC) of an inverter. Using the identification model of PV arrays, the module-based MPC controller is designed, and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature. An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors, the optimal voltage vector is selected according to the optimal value function, and the corresponding optimal switching state is applied to power semiconductor devices of the inverter. The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified, and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink. The results show that MPC has better tracking performance under constraints, and the system has faster and more accurate dynamic response and flexibility than conventional PI control.     

Model predictive control of grid-connected PV power generation system considering optimal MPPT control of PV modules

Because of system constraints caused by the external environment and grid faults, the conventional maximum power point tracking (MPPT) and inverter control methods of a PV power generation system cannot achieve optimal power output. They can also lead to misjudgments and poor dynamic performance. To address these issues, this paper proposes a new MPPT method of PV modules based on model predictive control (MPC) and a finite control set model predictive current control (FCS-MPCC) of an inverter. Using the identification model of PV arrays, the module-based MPC controller is designed, and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature. An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors, the optimal voltage vector is selected according to the optimal value function, and the corresponding optimal switching state is applied to power semiconductor devices of the inverter. The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified, and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink. The results show that MPC has better tracking performance under constraints, and the system has faster and more accurate dynamic response and flexibility than conventional PI control.