PEMFC发电系统的自适应模糊控制与动态分析

监测和控制燃料电池的过程中,需要获得各种实时数据.质子交换膜燃料电池(PEMFC)发电系统中的参数强耦合、高度非线性特性增加了对其控制的难度,传统的PI控制虽然对模型精确的系统有较好的控制效果,但对于参数波动的系统则无法获得较高的控制性能.针对以上情况,基于PEMFC发电系统的动态仿真模型,根据重整器在燃料电池发电系统中的作用,设计了自适应模糊控制器.利用模糊控制规则在线控制氢气摩尔流,从而控制PEMFC发电系统的输出功率.仿真结果表明,该动态模型能够预测输出电压.响应曲线显示出自适应模糊控制算法能够较好控制燃料电池有功和无功功率的输出.模型具有良好的负载跟踪特性.     

基于级联网络的电力系统短期负荷预测

电力系统短期负荷预测对电力系统运行设计具有十分重要的意义。因此，在分析了电力负荷运行曲线的基础上，提出了一种基于级联模糊神经网络的预测模型。该模型采用基于神经网路理论的模糊模型参数辨识方法，很适合于复杂系统的模糊预测和控制。详细地对输入量的选择和学习算法进行了分析。实例表明，此方法具有町靠、鲁棒性好和快速等特点，优于神经网络电力负荷预报方法。     

PEMFC的模糊辨识和非线性预测控制

质子交换膜燃料电池(PEMFC)是21世纪最有前景的发电技术之一。该文针对PEMFC等一类具有严重非线性的复杂被控对象,提出一种基于模糊模型的非线性预测控制算法对PEMFC系统进行建模与控制。在建模与控制过程中,采用模糊聚类和线性辨识方法在线建立PEMFC控制系统的T-S模糊预测模型,然后基于分支定界法的基本原理对控制量进行离散寻优,从而实现PEMFC的非线性预测控制。仿真和实验结果证明了该算法的有效性和优越性。     

Data-driven predictive control for solid oxide fuel cells

This paper is concerned with predictive control of solid oxide fuel cells (SOFC) based on a benchmark model commonly studied in the dynamic SOFC modeling/control literature. It has been shown in previous studies that control of SOFC is challenging owing to the slow response and tight operating constraints. In this paper, we apply a data-driven predictive control approach to solving the control problem of the SOFC system. The predictive control applied is completely data based. In addition, unlike other data-driven predictive control designs, the proposed approach can deal with systems without complete on-line measurement of all output variables. Simulation results have demonstrated the feasibility of the control application.     

多能源电动汽车中的Fuzzy控制策略及仿真

多能源电动汽车的能量存储系统由锌空电池、镍氢电池和超大电容三种能量存储元件组成。锌空电池为负载提供基本能量。镍氢电池工作在中级能量区，并回收下坡和刹车过程中的能量。超大电容工作在尖峰负载区，为大加速度过程提供能量，在短时间内可以实现能量回收。该文在多能源电动汽车的模型基础上，针对能量管理系统(EMS)提出了一种模糊控制策略。EMS模糊控制策略的输入包括所需功率、镍氢电池的SOC和超大电容的SOC，模糊控制策略的输出包括三个能量存储元件的分配功率因子，每个输入和输出有不同的模糊量。仿真结果表明：模糊控制策略比简单查表控制策略在续驶里程、燃料经济性和效率等方面均有所改善。     

基于启发式动态规划的固体氧化物燃料电池优化控制研究

固体氧化物燃料电池(SOFC)系统是一个非线性系统,现存的建模方法和优化控制算法很难对其进行精确的建模及优化控制;针对此问题,采用基于数据的建模方法,对固体氧化物燃料电池系统进行BP神经网络建模,然后在此基础上,首次采用启发式动态规划(HDP)算法对固体氧化物燃料电池系统中的各种气体分压、输出电压以及温度进行优化控制;Matlab仿真结果表明,基于BP神经网络的HDP优化算法具有收敛速度快、鲁棒性强、控制精度高等优点,并使固体氧化物燃料电池系统在负载变化时很快稳定输出电压,实现了优化控制,减少能耗。     

Disturbance rejection control of a fuel cell power plant in a grid-connected system

Interface of a fuel cell plant to power grid is challenging because of the high nonlinearities of the fuel cell plant and the power conditioning system (PCS). This paper focuses on the control of grid-connected solid-oxide fuel cell (SOFC) power plant that is subject to varying load and uncertain network parameters. To this end, Active Disturbance Rejection Control (ADRC) is utilized to improve the performance of the PCS consisting of a dc-dc converter and a dc-ac inverter. ADRC is used in the dc-dc converter to stabilize the dc link voltage and yield a robust performance against the nonlinearity. Used in the dc-ac inverter, ADRC eliminates the steady-state error and is insensitive to the high-frequency noise. Simulation results show that, for grid current control, ADRC achieves a more robust performance than the conventional proportional-integral (PI) controller. Moreover, the total harmonic distortions (THDs) of the output current controlled by ADRC are always below 5% in spite of the variation in the load demand and network parameters.     

A reactive scheduling and control framework for integration of renewable energy sources with a reformer-based fuel cell system and an energy storage device

The main technological barrier in relying solely on renewable energy resources is that the sources such as wind and solar are highly intermittent in availability and result in uncertainty in demand satisfaction. This paper focuses on the integration of these uncertain renewable energy sources along with relatively deterministic energy sources such as reformer based fuel cell and battery. The power mix scenario between these multiple renewable energy sources along with the reformer based fuel cell system, coupled with an energy storage option is envisaged in this paper to ensure undisrupted power supply, to combat the possible intermittent nature of these renewable sources. An appropriate scheduling layer which provides a detailed plan of the optimum contribution of the various available power sources is considered over one week (7 days) duration. A model predictive control (MPC) scheme is deployed at the lower level control layer that receives a measurement of the possible fluctuations or uncertainties in the renewable power sources and maintains a smooth operation of the power generation system through appropriate decisions on generation via the reformer based fuel cell or by exploiting the battery storage, to ensure a delay-free delivery of power to the external load. During real-time operation of the plant, due to the uncertainties in the contribution from solar and wind sources, the power demanded from the fuel cell and the battery is varied accordingly by the MPC layer to meet the overall power demand. The performance of the designed MPC to maintain a smooth delivery of power in both the absence and presence of uncertainties in the renewable energy sources, with and without a reactive feedback between the scheduling and control layers, is illustrated using case studies.     

Economical Optimization of Grid Power Factor Using Predictive Data

We present an electrical grid optimization method for economical benefit. After simplifying an IEEE feeder diagram, we build a compact smart grid system including a photovoltaic-inverter system, a shunt capacitor, an on-load tapchanger (OLTC) and transmission lines. The system power factor (PF) regulation and reactive power dispatching are indispensable to improve power quality. Our control method uses predictive weather and load data to decide engaging or tripping the shunt capacitor, or reactive power injection by the photovoltaic-inverter system, ultimately to keep the system PF in a good range. From the perspective of economics, the economical model is considered as a decision maker in our predictive data control method. Capacitor-only control strategy is a common photovoltaic (PV) regulation method, which is treated as a baseline case. Simulations with GridLAB-D on profiled loads and residential loads have been carried out. The comparison results with baseline control strategy and our predictive data control method show the appreciable economical benefit of our method.      

Nonlinear fuzzy model predictive iterative learning control for drum-type boiler–turbine system

Advanced control strategy is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant. Model predictive control (MPC) has been widely used for controlling power plant. Nevertheless, MPC needs to further improve its learning ability especially as power plants are nonlinear under load-cycling operation. Iterative learning control (ILC) and MPC are both popular approaches in industrial process control and optimization. The integration of model-based ILC with a real-time feedback MPC constitutes the model predictive iterative learning control (MPILC). Considering power plant, this paper presents a nonlinear model predictive controller based on iterative learning control (NMPILC). The nonlinear power plant dynamic is described by a fuzzy model which contains local liner models. The resulting NMPILC is constituted based on this fuzzy model. Optimal performance is realized within both the time index and the iterative index. Convergence property has been proven under the fuzzy model. Deep analysis and simulations on a drum-type boiler–turbine system show the effectiveness of the fuzzy-model-based NMPILC     

Modeling and cycling control of carbonate fuel cell power plants

A mathematical process model for an internal reforming molten carbonate fuel cell power plant is discussed in this paper. The dominant thermal and chemical dynamic processes are modeled for the cell stack array and balance-of-plant, including cathode gas preparation, heat recovery, and fuel processing. Physical data is obtained from a 2 MW system design that was a precursor to a demonstration plant operated at the City of Santa Clara, CA, USA. Steady state validation for several load points is provided for the cell stack array and a load cycling control system is described and tested under ramping operation between load points.     

安全操控下5 kW固体氧化物燃料电池电堆预测控制器设计

基于5 kW固体氧化物燃料电池(SOFC)电堆,考虑建模仿真—2温度层模型在模型精度与复杂度上做了更好的折中,可以更有效地应用于控制器设计.本文首先对2温度层模型在常用稳态工作点附近采用泰勒级数展开,获得其状态空间方程.然后考虑其安全操作特性,设计了两种带约束的预测控制器:即面向SOFC电堆的快速负载跟踪与燃料亏空控制器与面向SOFC电堆温度安全的控制器.重点分析了不同切换速率工况下的温度及其梯度、功率以及燃料亏空特性,使得系统在快速进行功率跟踪的同时工作在安全范围以内.结果发现随着电流调节速率的增大,跟踪过程虽然加快,但其存在安全风险也相应增大;此外,安全指标相对避免燃料亏空指标而言,对电流调节速率的要求更加苛刻,在控制器设计时必须综合进行考虑.     

Distributed generation system control strategies with PV and fuel cell in microgrid operation

Control strategies of distributed generation (DG) are investigated for different combination of DG and storage units in a microgrid. In this paper the authors proposed a microgrid structure which consists of a detailed photovoltaic (PV) array model, a solid oxide fuel cell (SOFC) and various loads. Real and reactive power (PQ) control and droop control are developed for microgrid operation. In grid-connected mode, PQ control is developed by controlling the active and reactive power output of DGs in accordance with assigned references. Two PI controllers were used in the PQ controller, and a novel heuristic method, artificial bee colony (ABC), was adopted to tune the PI parameters. DGs can be controlled by droop control both under grid-connected and islanded modes. Droop control implements power reallocation between DGs based on predefined droop characteristics whenever load changes or the microgrid is connected/disconnected to the grid, while the microgrid voltage and frequency is maintained at appropriate levels. Through voltage, frequency, and power characteristics in the simulation under different scenarios, the proposed control strategies have demonstrated to work properly and effectively. The simulation results also show the effectiveness of tuning PI parameters by the ABC.     

插电式混合动力汽车模型预测控制策略研究

针对传统插电式混合动力汽车智能控制策略计算量大,难以实现实时最优控制的问题,提出了基于蓄电池充放电管理的插电式混合动力汽车预测控制策略.利用实测通勤插电式混合动力汽车车速信息,以蓄电池荷电状态为系统状态变量,以蓄电池充放电功率为系统控制变量,插电式混合动力汽车燃油消耗量最低为系统性能指标,设计了插电式混合动力汽车的模型预测控制智能优化算法,运用连续广义最小残量方法求解最优控制问题.在Matlab/Simulink与GT-POWER联合仿真平台上进行仿真,实验结果验证了所设计的模型预测控制算法不仅可以大幅度提高混合动力汽车的燃油经济性,而且能够满足实时控制的要求.     

风电介入下的多区域互联电力系统分布式经济模型预测负荷频率控制

针对风电介入下的多区域互联电力系统,提出一种分布式经济模型预测负荷频率控制策略.通过将大规模互联电力系统分解成若干个动态耦合的子系统,这些子系统能够利用网络交流并共享信息,使得各区域的控制器实现各自优化问题的求解.同时,在满足状态约束和控制输入约束的前提下,遵循传统火力发电优先、风力发电配合的原则,通过在线求解优化问题,实现风电介入下的多区域互联电力系统的负荷频率控制.为了提高系统整体运行经济性,所提出的分布式经济模型预测控制器将负荷调频成本、燃料消耗成本以及风力发电成本等经济性指标考虑在内.仿真结果表明,在阶跃负荷扰动下,所设计的控制器不仅可以满足调频要求,在降低计算负担和提高经济性能方面也具有一定优势.     

Dynamic modeling,simulation, and MIMO predictive control of a tubular solid oxide fuel cell

Solid oxide fuel cells are a promising option for distributed energy stationary power generation that offers efficiencies up to 50% in stand-alone applications, 70% in hybrid gas turbine applications and 80% in cogeneration. To advance SOFC technology sufficiently for widespread market penetration, the SOFC must demonstrate improved cell lifetime from the status quo. Much research has been performed to improve SOFC lifetime using advanced geometries and materials, and in this research, we suggest further improving lifetime by designing an advanced control algorithm based upon preexisting mechanical stress analysis [1]. Control algorithms commonly address SOFC lifetime related operability objectives using unconstrained, SISO control algorithms that seek to minimize thermal transients. While thermal fatigue may be one thermal stress driver, these studies often do not consider maximum radial thermal gradients or critical absolute temperatures in the SOFC. In addition, researchers often discuss hot-spots as a critical lifetime reliability issue, but as previous stress work demonstrates, the minimum cell temperature is the primary thermal stress driver in tubular SOFCs modeled after the Siemens Power Generation, Inc. design. In this work, we present a dynamic, quasi-two-dimensional model for a high-temperature tubular SOFC combined with ejector and prereformer models. The model captures dynamics of critical thermal stress drivers and is used as the physical plant for closed-loop simulations with a constrained, MIMO model predictive control algorithm. Closed-loop simulation results demonstrate effective load-following, operability constraint satisfaction, and disturbance rejection.     

多变量模糊自校正控制器及其应用

本文介绍了表达ＭＩＳＯ动态系统的模糊模型，并提出了有关参数和结构的在线辨识算法。根据辨识的模糊模型，利用Ｃｌａｒｋｅ的单变量广义预测控制（ＧＰＣ）原理［１］设计了多变量模糊自校正控制器。仿真研究表明，该模糊自校正控制方法应用于火电机组负荷系统的控制，可以收到良好的效果。     

100W质子膜燃料电池应急供电系统

提出一种基于质子膜燃料电池(PEMFC)和锂电池的混合联供的应急供电系统。该系统由PEM燃料电池电堆、锂电池、控制系统、氢气储存及管路系统组成,控制系统利用模糊控制算法将锂电池的SOC、负载大小以及燃料经济性及PEMFC的最佳状态作为输入变量,将锂电池和燃料电池的输出功率配比作为输出,使应急供电系统的输出各部件工作于最佳状态。研制了样机,并应用于野外应急情况。实际测试和应用表明,系统各项指标满足战术技术要求,是抢险救灾应付突发事件的理想应急供电装备。