基于ROLS算法的RBF神经网络燃料电池电特性建模

提出了一种基于ROLS算法的RBF神经网络辨识建立直接甲醇燃料电池(DMFC)电特性模型的新方法。以电池的工作温度为输入量,电池的电压／电流密度为输出量,利用1200组实验数据作为训练和测试样本,建立了在不同工作温度下,电池的电压／电流密度动态响应模型,仿真结果表明采用RBF神经网络辨识建模的方法是有效的,建立的模型精度较高。     

应用模糊辨识方法对DMFC电堆的温度特性建模

从甲醇燃料电池（DMFC）电堆实际应用的角度出发,利用模糊技术对DMFC电堆非线性系统进行模型辨识和预测。以阴阳极燃料的流速为的输入量,电堆的工作温度为输出量,利用1000组实验数据作为样本,建立了不同燃料流速下电堆工作温度的动态响应模型。仿真结果证明采用模糊辨识建模的方法是有效的,建立的模型精度较高,从而为设计DMFC电堆实时控制系统奠定了基础。     

基于PSO-RBF神经网络的微电网等效建模

提出一种改进粒子群优化的RBF神经网络微电网动态等效模型及建模方法,利用RBF人工神经网络的非线性映射特性解决微电网系统并网接入的等效建模问题。基于微电网公共接入点(PCC)的电压、电流、功率等量测数据构建RBF神经网络等效模型,将接入点电压和电流分别作为神经网络的输入和输出,使神经网络的输入输出更具独立性。将混沌优化的全局遍历性引入粒子群优化算法中,构建基于全局最优解的变邻域混沌搜索提高粒子群算法的全局搜索能力,利用改进粒子群算法优化RBF神经网络模型参数提高模型计算精度。最后通过微电网并网仿真实验验证本文提出等效模型的准确性和建模方法的合理性。     

面向综合负荷的燃料电池等效模型及参数灵敏度分析

基于Matlab/Simulink建立了固体氧化物燃料电池(Solid Oxide Fuel Cell,SOFC)及其并网系统的动态模型,研究了SOFC发电系统的外特性。从外特性等效拟合的角度出发,忽略SOFC并网系统的无功功率,提出了SOFC发电系统的等效模型。该模型结构简单,参数少,易于辨识,能够有效拟合电网在大扰动和连续小扰动情况下的燃料电池发电系统外特性,与其他模型相比能够模拟燃料电池的出力极限问题;分析了该模型的参数的灵敏度,模型的两个参数的灵敏度虽不高但易于辨识;用本文提出的SOFC等效模型并联传统的综合负荷模型(Synthesis Load Model,SLM)构建广义负荷模型,对含SOFC发电系统的配电网负荷特性进行辨识建模。算例表明,该广义负荷模型能够有效描述含燃料电池发电系统配电网负荷特性,且模型的参数辨识结果具有较好的稳定性。     

基于广义回归神经网络的转子系统质量不平衡辨识

提出了一种基于广义回归神经网络(GRNN)模型的转子系统质量不平衡定量辨识方法:采用拉丁超立方抽样和转子系统的有限元模型获取训练样本,随后采用优化的GRNN模型建立不平衡参数与各测点振动响应的对应关系,降低反演优化迭代过程中冗繁的有限元计算;构建用于求解质量不平衡参数的目标函数,借助建立的GRNN关联模型,采用PSO算法寻找满足符合目标函数的全局最优解,从而达到质量不平衡定量识别的目的。此外,为了降低噪声的影响,采用零相位带通滤波器进行实测振动信号的去噪滤波。仿真结果表明该方法能有效地辨识出转子系统中的质量不平衡参数,辨识结果的准确度较高。     

低催化剂载量直接甲醇燃料电池性能实验研究

利用自行设计的直接甲醇燃料电池实验系统研究了电池温度、阴极侧压力、甲醇溶液浓度和流量、氧气流量对低催化剂载量（阳极Pt-Ru载量为0．4mg／cm^2；阴极Pt载量为0．4mg／cm^2）直接甲醇燃料电池性能的影响。重点分析了运行参数对电池内部传质的影响。实验结果表明提高甲醇溶液浓度和流量不仅会强化甲醇向阳极催化剂层的传递，也会加剧甲醇窜流。另外，还研究了电流变化时电池电压的动态响应，结果表明甲醇窜流对电池动态响应起关键作用。     

固体氧化物燃料电池的神经模糊控制策略研究

固体氧化物燃料电池(Solid Oxide Fuel Cell,SOFC)具有多输入多输出、强耦合的特点,为了使其输出电压稳定设计了高效控制器,采用神经模糊控制方法对其输出电压进行控制。通过机理分析和实验数据拟合方法分别建立SOFC的机理模型和神经网络模型,在此基础上采用模糊控制策略对SOFC的输出电压进行控制,并应用神经模糊控制方法进一步提高了控制精度。通过MATLAB/Simulink仿真实验发现,SOFC神经网络模型得到的预测电压与实际电压之间的误差小于0.008 V,较其机理模型更加准确,所提出的控制策略能有效控制SOFC的输出电压。     

风力机系统的神经网络模型辨识

应用人工神经网络的建模方法,采用多层感知器的模型结构,利用自适应学习速率的BP学习算法,辨识出风力机系统的功能模型,并把辨识模型的仿真结果与系统实验测量数据相对比,开展了与经典系统辨识方法的比较研究,以检验神经网络模型的可靠性.实验结果表明,这种新的风力机系统建模方法具有很高的精度.     

管式固体氧化物燃料电池的数值模拟

通过耦合速度场、温度场、电势场和组分浓度场，建立了以纯氢气为燃料的管式固体氧化物燃料电池（SOFC）数学模型，并对西门子．西屋公司阴极支撑型（AES）管式SOFC进行了轴向二维模拟。模拟结果表明，组分浓度和电流密度的分布与SOFC的运行工况密切相关。在所模拟的电压范围内，欧姆极化起主要作用，提高固体氧化物燃料电池的平均工作温度、改善多孔电极的微观结构、使用纯氧代替空气作为氧化剂可改善电池性能。图8参10     

风力机系统的神经网络模型辨识

应用人工神经网络的建模方法，采用多层感知器的模型结构，利用自适应学习速率的ＢＰ学习算法，辨识出风力机系统的功能模型，并把辨识模型的仿真结果与系统实验测量数据相对比，开展了与经典系统辨识方法的比较研究，以检验神经网络模型的可靠性。实验结果表明，这种新的风力机系统建模方法具有很高的精度。     

Application of adaptive neuro-fuzzy inference system techniques and artificial neural networks to predict solid oxide fuel cell performance in residential microgeneration installation

This study applies adaptive neuro-fuzzy inference system (ANFIS) techniques and artificial neural network (ANN) to predict solid oxide fuel cell (SOFC) performance while supplying both heat and power to a residence. A microgeneration 5 kW_el SOFC system was installed at the Canadian Centre for Housing Technology (CCHT), integrated with existing mechanical systems and connected in parallel to the grid. SOFC performance data were collected during the winter heating season and used for training of both ANN and ANFIS models. The ANN model was built on back propagation algorithm as for ANFIS model a combination of least squares method and back propagation gradient decent method were developed and applied. Both models were trained with experimental data and used to predict selective SOFC performance parameters such as fuel cell stack current, stack voltage, etc.     

Transient analysis of a solid oxide fuel cell unit with reforming and water-shift reaction and the building of neural network model for rapid prediction in electrical and thermal performance

This study investigates the effect of reforming reaction, water-shift reaction, and operating parameters on the transient performance of a solid oxide fuel cell unit, because the transient analysis is necessary and helpful for the applications of a SOFC with cross-flow configuration. The primary results show that all properties approach the steady state at similar time except the cell temperature. The reforming and water-shift reaction obviously promote the average current density by 5%, and lower the maximum cell temperature by 20 K. The molar flow rate variation deduces about 15 K difference of maximum cell temperature. The effect of inlet temperature and operating voltage on the average current density and maximum cell temperature is more obvious than the molar flow rate effect. Moreover, this study builds a neural network model to predict the steady average current density and maximum cell temperature rapidly and correctly, which is helpful for the control of a SOFC.     

静止无功发生器在风电场电网故障时作用的研究

分析了双馈异步发电机（DFIG）在电网电压跌落时的暂态过程,通过人工接地短路试验测试了风电场升压站内110 kV母线电压和风机出口处电压、电流的故障波形。针对风电场在故障时存在的问题,对静止无功发生器（SVG）在提高DFIG低电压穿越能力中的作用进行了仿真,并通过现场试验对仿真的结论进行了验证。     

Predictive control of SOFC based on a GA-RBF neural network model

Transients in a load have a significant impact on the performance and durability of a solid oxide fuel cell (SOFC) system. One of the main reasons is that the fuel utilization changes drastically due to the load change. Therefore, in order to guarantee the fuel utilization to operate within a safe range, a nonlinear model predictive control (MPC) method is proposed to control the stack terminal voltage as a proper constant in this paper. The nonlinear predictive controller is based on an improved radial basis function (RBF) neural network identification model. During the process of modeling, the genetic algorithm (GA) is used to optimize the parameters of RBF neural networks. And then a nonlinear predictive control algorithm is applied to track the voltage of the SOFC. Compared with the constant fuel utilization control method, the simulation results show that the nonlinear predictive control algorithm based on the GA-RBF model performs much better.     

4×4阵列管式固体氧化物燃料电池（SOFC）堆温度场和流场初步分析

通过对实验中管式SOFC堆的数学建模仿真方法，研究实验中的百瓦级4×4管式电池堆内部的流体流动、传热和组分浓度等特性，分析电池参数对电池内部气体流速、温度和浓度分压分布。计算结果和实验测试发现：流场和压力场基本均匀，温度场变化在±34．7K，而阵列电池管开路电压测试值在1．0～1．15vg间，基本满足电堆工作要求。     

电网电压故障下向无源网络供电的MMC电力电子变压器的控制策略

电力电子变压器(PET)具有瞬时功率调节、谐波抑制等优点,输电线路经过PET向无源网络供电是柔性输电的一个重要应用领域.将模块化多电平变换器(MMC)技术与PET相结合,使得PET应用于高电压、大容量的输电和配电系统成为了可能.首先,本文针对MMC-PET输入级在电网故障时产生的正负序电流,推导出基于欧拉-拉格朗日(E...     

Nonlinear model predictive control of SOFC based on a Hammerstein model

To protect solid oxide fuel cell (SOFC) stack and meet the voltage demand of DC type loads, two control loops are designed for controlling fuel utilization and output voltage, respectively. A Hammerstein model of the SOFC is first presented for developing effective control strategies, in which the nonlinear static part is approximated by a radial basis function neural network (RBFNN) and the linear dynamic part is modeled by an autoregressive with exogenous input (ARX) model. As we know, the output voltage of the SOFC changes with load variations. After a primary control loop is designed to keep the fuel utilization as a steady-state constant, a nonlinear model predictive control (MPC) based on the Hammerstein model is developed to control the output voltage of the SOFC. The performance of the MPC controller is compared with that of the PI controller developed in [Y.H. Li, S.S. Choi, S. Rajakaruna, An analysis of the control and operation of a solid oxide fuel-cell power plant in an isolated system, IEEE Trans. Energy Convers. 20 (2) (2005) 381–387]. Simulation results demonstrate the potential of the proposed Hammerstein model for application to the control of the SOFC, while the excellence of the nonlinear MPC controller for voltage control of the SOFC is proved.     

一种基于BOA-SAE-EELM的光伏阵列故障诊断方法

光伏阵列非线性输出的特性以及最大功率点跟踪算法,会影响光伏阵列保护设备的工作.为了正确辨识光伏阵列的运行状态,本研究提出一种基于贝叶斯优化算法(BOA)、堆栈自动编码器(SAE)以及集成极限学习机(EELM)相结合的故障诊断方法.首先,将光伏阵列的时序波形进行标准化处理;接着,使用SAE对标准化后的时序波形进行特征自动...