空冷型PEMFC发电系统实时最优温度自适应逆控制

针对空冷型PEMFC发电系统工作温度对输出性能影响问题,通过实验手段研究工作温度对系统输出性能的最优特性。由于空冷型PEMFC发电系统温度控制对象所具有的非线性、时变等特点,提出基于自适应逆控制的空冷型PEMFC发电系统实时最优温度控制。其中,递推最小二乘在线辨识算法用于对非线性控制对象进行建模和在线校正,最小均方算法用于在线调整逆控制器的参数。在搭建的空冷型PEMFC发电系统测控实验平台上对控制方法的性能进行验证。实验结果表明:提出的控制方法能够在不同负载条件下实现对电堆最优温度实时跟踪。而且,提出的控制方法对PEMFC参数不敏感,可以应用于类似的空冷型PEMFC发电系统。     

基于PEMFC分布式发电系统的动态特性分析

分析了质子交换膜燃料电池(PEMFC)的机理模型,在此基础上运用MATLAB的Simulink仿真工具,建立了PEMFC发电系统带负载模型。通过仿真,分析了负载对PEMFC电堆的各项动态特性(燃料的流量、效率、输出电压等)的影响,以及DC/DC、负载端的电压响应。仿真结果中负载电压呈三相交流正弦波形,表明搭建的整个PEMFC发电系统是基本正确的,为实现PEMFC并网的实时分析和动态优化提供了理论依据和参考方法。     

基于神经网络逆的双馈发电励磁控制

将神经网络逆控制方法应用于双馈发电励磁系统.根据并网型双馈发电机数学模型,进行系统可逆性分析,确定神经网络逆系统模型.合理选择逆系统输入、输出变量,在适当激励信号作用下,对原系统状态进行采样及数值计算,获得原始输入样本集和期望输出样本集,利用BP算法训练神经网络.用神经网络实现逆控制,将系统分解为有功功率和无功功率的两个单变量线性子系统,通过线性系统综合方法,设计了由PI调节器组成的有功功率和无功功率线性单闭环子系统.仿真结果表明,采用神经网络逆系统控制的双馈发电系统具有较好的动态性能,不仅能够方便实现有功功率的控制,而且可独立提供电网所需的无功功率.     

基于逆系统内模算法的交流励磁发电机

在建立交流励磁发电机空间状态方程的基础上,运用逆系统方法将交流励磁发电机精确线性化成有功功率和无功功率两个一阶线性子系统,并运用内模控制算法对其进行优化以提高系统的稳定性。提出了交流励磁发电机的基于逆系统内模控制策略,并进行了仿真实验。仿真结果表明,该方法能够实现对发电输出功率的解耦控制,并具有更高的控制精确度和稳定性。     

基于PSOEM和神经网络的光伏电站功率预测

分析光伏发电输出功率预测的影响因素,确定了基于BP神经网络的功率预测模型,针对BP神经网络本身易陷入局部极值、收敛速度慢等问题,采用粒子群优化算法（PSO）和带扩展记忆粒子群优化算法（PSOEM）这2种群智能算法来优化BP神经网络的初始值和阈值,分别建立了基于PSO-BP神经网络和基于PSOEM-BP神经网络的光伏电站输出功率预测模型。根据某光伏电站2月1日—6月30日的光伏发电历史数据,利用所提3种模型对光伏发电系统进行了功率预测。误差对比结果表明,基于PSOEM-BP神经网络的功率预测精度明显高于基于PSO-BP神经网络的功率预测精度,故采用PSOEM优化后BP神经网络模型进行光伏功率预测,具有一定的理论和实用价值。     

基于BP神经网络光柴孤网运行优化控制研究

摘要： 光柴互补发电独立微电网系统,主要包括：光伏电池组发电、柴油机发电和负载等,光伏发电模型采用MPPT控制。在确保光伏最大功率输出的基础上,对原动机及其速度/功率反馈系统、同步发电机及其励磁控制系统进行分析,建立相应的数学模型。设计基于BP神经网络算法PID控制器,控制原动机的转速调整其输出功率,维持微网系统频率的恒定。仿真结果表明：该控制策略实现光伏最大功率输出,提高太阳能利用率。同时,保证微网系统的频率恒定。     

基于电流保护约束的逆变型分布式电源最大接入容量与接入位置选择

摘要： 逆变型分布式电源接入配电网后,传统配电网采用的三段式电流保护可能会发生不正确动作。为了解决这一问题,提出了基于电流保护约束的逆变型分布式电源最大接入容量和接入位置选择的确定方法。当配电网发生故障时,根据零电压穿越控制的逆变型分布式电源的故障输出特性,分析逆变型分布式电源并网点电压及输出的故障电流随逆变型分布式电源输出功率和接入位置变化而变化的规律。发现逆变型分布式电源的并网点电压及电流随逆变型分布式电源输出功率的增大呈非线性变化、随接入位置远离系统电源而逐渐减小的特征。根据该变化特征,以传统配电网电流保护在不改变原有整定值的情况下能正确动作为条件,确定了逆变型分布式电源的接入容量及接入位置。仿真验证了该方法的有效性。     

基于Tabu-PSO的含分布式发电配电网重构

针对分布式发电对配电网重构的影响,对包含分布式发电的配电网进行数学建模;应用改进的粒子群优化算法提高算法的搜索速度;把分布式发电作为并网节点上的功率值为负的负荷。研究结果表明,对含有分布式发电的配电网优化重构后,系统的网损有较大降低,系统电压有所提高。     

面向分布式负载的模块化三端口功率系统

面向具有复杂分布式用电负载需求的航天器,提出一种基于三端口变换器(TPC)的供电系统架构及其功率控制策略。该系统以TPC为基本单元,通过将各TPC模块的输入端口和双向功率端口分别并联连接,实现输入能源、储能装置和多个分布式负载之间的供电。针对各分布式负载输出功率不同且单个TPC含有3条功率路径,可能导致系统中各传输路径功率不确定的问题,提出一种混合型系统功率控制策略以实现各模块各功率路径可控及系统功率优化管理,具体而言,即在储能装置充电工况下均衡储能装置的充电电流,在储能装置放电工况下按输出需求分配输入功率。分析和实验表明,系统可以在各工作状态下稳定运行并在各工作状态之间自由切换。     

基于改进BA算法的光伏发电系统MPPT优化控制

为优化光伏阵列在部分遮蔽情况下的多峰值MPPT控制,保证光伏发电系统实时功率的最大输出,提出了基于改进BA算法的最大功率追踪控制方法,即在基本BA算法的基础上,融入了小生境技术的共享机制与排挤策略,减少相似个体数量,从而增加了BA算法在迭代过程中的种群多样性,提高了BA算法在MPPT控制中的全局搜索能力,增强了最大功率追踪的稳定性,并将该算法与PSO、PO算法在不同光照及温度条件下的MPPT控制效果进行了仿真试验对比。结果表明,与传统算法相比,改进的BA算法具有更好的追踪效果,不仅避免了光伏系统在遮蔽情况下输出功率陷入局部最大值的问题,且提高了发电效率。     

Fractional order modeling and two loop control of PEM fuel cell for voltage regulation considering both source and load perturbations

The growing demand for renewable energy sources has favored attention towards fuel cell and in particular towards Polymer Electrolyte Membrane Fuel Cell (PEMFC) as an alternative energy source. Despite the advantage of possessing high current density, standalone isolated fuel cell operate at low voltage and the output is heavily dependent on the operating condition. This demands the integration of fuel cells with suitable power conditioning units. The present work aims at designing a controller which achieves the objective of regulated output voltage irrespective of variation in both load and source operating condition. The design and integration of the converter with PEMFC necessitates the development of a mathematical model, which can represent the PEMFC dynamics under different operating conditions. PEMFCs are known to exhibit distributed dynamics and possess long term memory, which are more accurately represented by fractional calculus. In this regard, a hybrid optimization based approach for fractional order modeling of PEMFC has been proposed. Further using the model, a fractional order Proportional Integral (FOPI) controller has been designed for regulating the load voltage. The presence of an extra tuning parameter in FOPI allows greater flexibility in achieving the system specification as compared to the classical Integer Order Proportional Integral (IOPI) controller. The effectiveness of the proposed FOPI controller for PEMFC fed PWM DC/DC converter has been validated under varying operating condition of the PEMFC and load perturbations in real time environment.     

An adaptive sliding mode observer based near-optimal OER tracking control approach for PEMFC under dynamic operation condition

Tracking control of oxygen excess ratio (OER) is crucial for dynamic performance and operating efficiency of the proton exchange membrane fuel cell (PEMFC). OER tracking errors and overshoots under dynamic load limit the PEMFC output power performance, and also could lead oxygen starvation which seriously affect the life of PEMFC. To solve this problem, an adaptive sliding mode observer based near-optimal OER tracking control approach is proposed in this paper. According to real time load demand, a dynamic OER optimization strategy is designed to obtain an optimal OER. A nonlinear system model based near-optimal controller is designed to minimize the OER tracking error under variable operation condition of PEMFC. An adaptive sliding mode observer is utilized to estimate the uncertain parameters of the PEMFC air supply system and update parameters in near-optimal controller. The proposed control approach is implemented in OER tracking experiments based on air supply system of a 5 kW PEMFC test platform. The experiment results are analyzed and demonstrate the efficacy of the proposed control approach under load changes, external disturbances and parameter uncertainties of PEFMC system.     

Fuel-cell sharing for a distributed hybrid power system

This paper investigates the benefits of sharing a proton exchange membrane fuel cell (PEMFC) in a distributed hybrid power system. The PEMFC is usually used as backup power in stationary hybrid power systems; however, in that scenario, it might be working only 2% of the time while incurring 20% of the system expenses. Therefore, this paper examines the potential of sharing a PEMFC among multiple power systems. We develop a distributed hybrid power system that comprises several immovable power stations and a fuel-cell vehicle (FCV). Each power station is equipped with solar panels and batteries, while the FCV contains a PEMFC module and can move among the stations to provide sustainable power as needed. We propose power management strategies and show that the total system costs can be significantly reduced by 10.83% and 17.89% when sharing one FCV between three and twelve power stations, respectively. We also design experiments to demonstrate the feasibility of the proposed distributed hybrid power system. In the future, the developed model can be extended to provide further cost reductions by optimizing distributed hybrid power systems with multiple FCVs.     

Real-time AC voltage control and power-following of a combined proton exchange membrane fuel cell,and ultracapacitor bank with nonlinear loads

The nonlinear loads create a wide range of current harmonics in the system. Such loads can make distortions on the output voltage profile, influence on the fuel cell (FC) performance, and endanger safe operation of the FC unit. In this paper, new strategies for power-following and AC voltage control have been developed. The proposed system consists of the ultracapacitor (UC) bank and proton exchange membrane fuel cell (PEMFC) supplying nonlinear AC loads. The power tracking strategy is based on the Fourier analysis of total load demand. The Fourier analysis is used as an effective tool to eliminate destructive effect of current harmonics on the PEMFC output current. To supply the nonlinear AC loads under sinusoidal voltage with the fast response, a dynamic model for the inverter control loop is also presented. This model is used to enhance the input reference tracking and reject input/output disturbances. The simulation outcomes confirm the desirable PEMFC performance against nonlinear load disturbances. In addition, the output AC voltage is kept sinusoidal and has low deviations under nonlinear load variations.     

Collaborative simulation for dynamical PEMFC power systems

In this paper a collaborative simulation platform for proton exchange membrane fuel cell (PEMFC) power systems is presented, where the stack is simulated by a two-phase distributed parameter model and the auxiliary units by lumped parameter models. By exchanging the dynamic data between the external load/auxiliary units and PEMFC stack, dynamic simulation of PEMFC stack has been carried out during the load changes for various states associated with different characteristic variables. The internal states of the stack can be observed due to variation of external load/auxiliary units. Numerical experiments are provided for a special case with multiple cycles of load changes derived from an acceleration mode of a fuel cell vehicle. The numerical results demonstrate that the “undershoot” of output voltage is due to the response lag of the auxiliary units and liquid water accumulation in the fuel cell stack.     

CMAC逆模型用于电站负荷协调控制的研究

在常规PID负荷协调控制回路中加入CMAC神经网络模型。利用神经网络的非线性映射能力，能很好地解决负荷协调控制对象的动态特性具有非线性、时变性、参数可变等问题。仿真对比试验表明：负荷协调控制系统引入CMAC逆模型后，系统的跟踪速度加快了大约1个周期，调节精度提高。CMAC逆模控制器有较好的适应性、鲁棒性。图6表1参9     

Load governor based on constrained extremum seeking for PEM fuel cell oxygen starvation and compressor surge protection

Polymer Electrolyte Membrane Fuel Cells (PEMFC) are extremely sensitive to load variations. A rapid load variation can damage the system in many ways. For this reason, a Load Governor (LG) based on constrained Extremum Seeking (ES) approach has been proposed in this paper. The LG controller ensures that the physical constraints of the system are not violated and the ES controller varies the LG bandwidth parameter to optimize the net power output. The proposed controller has been tested on a Hardware-In-Loop (HIL) emulation test bench, based on a commercial centrifugal compressor and a real time PEMFC emulator. Experimental results show a good performance of the proposed scheme during load variations.     

A coupled and interactive influence of operational parameters for optimizing power output of cleaner energy production systems under uncertain conditions

The mechanisms in proton‐exchange membrane fuel cells (PEMFCs) cannot be explicitly represented by a mathematical function because the PEMFC system is multi‐dimensional and complex and represents uncertainty in operation variables, which cannot be modeled by experiments or by trial‐and‐error approach. Therefore, this work proposes to study the coupled and interactive influence of stack current (SC), stack temperature (ST), oxygen excess ratio (OER), hydrogen excess ratio (HER), and inlet air humidity (IAH) for optimizing the power output of PEMFC. The data obtained from the experiments have been inserted into architecture of automated neural‐network search, which automates the selection of error function, activation function, uncertainties in inputs and number of hidden neurons in formulation of a robust and accurate model for power density as a function of five operational variables. Among the operational variables, the correlation coefficient between the SC and the output power is the highest, followed by OER, and the ST. However, for HER and IAH, the power output follows negative nonlinear relation. The optimization converged at 130^th iteration results in maximum power output of 3410 W for an optimum value of SC (51A), ST (59°C), OER (3:2), HER (1:10), and IAH (0.8).     

A fuzzy logic based power system stabilizer with learning ability

A fuzzy logic-based power system stabilizer (PSS) with learning ability is proposed in this paper. The proposed PSS employs a multilayer adaptive network. The network is trained directly from the input and the output of the generating unit. The algorithm combines the advantages of artificial neural networks (ANNs) and fuzzy logic control (FLC) schemes. Studies show that the proposed adaptive network-based fuzzy logic PSS (ANF PSS) can provide good damping of power systems over a wide range of operating conditions and improve the dynamic performance of the power system     

The optimal design for PEMFC modeling based on Taguchi method and genetic algorithm neural networks

This paper has presented a new approach to estimate the output voltage of proton exchange membrane fuel cell (PEMFC) accurately by combining the use of a genetic algorithm neural networks (GANN) model and the Taguchi method. Using the PEMFC experimental data measured from performance test equipment of PEMFC, the GANN model could be trained and constructed for obtaining the steady state output voltage of PEMFC. Furthermore, in order to determine the important parameters in GANN, the Taguchi method is used for parameter optimization, with the goal of reducing the estimation error. The test equipment of PEMFC is accurate enough for acquiring the output voltage of PEMFC, and is quite useful for teaching purpose. However, taking the high cost, complicated operation procedure and environment safety into consideration, it is necessary to develop a simulation model of PEMFC to benefit teaching and R&D. Therefore, this paper will present an approach for constructing a GANN model with precise accuracy for the output voltage of PEMFC. For achieving the GANN model with high precision, a troublesome work has to be taken care of, that is, to determine all the parameters required in GANN. We will introduce Taguchi method to solve this problem as well. Finally, to show the superiority of proposed model, this approach has compared the estimation values of output voltage for PEMFC from GANN and BPNN models without using Taguchi method. One can easily find that the error of the proposed method is much smaller than that of the GANN model without Taguchi method and of the BPNN model; that is, the proposed approach has better performance on estimation for PEMFC output voltages.