前缘结节对风力机气动性能影响的数值研究

基于座头鲸的鱼鳍前缘结节的流动特性,开展前缘结节对改造的Phase Ⅵ仿生风力机叶片性能及流动特性影响的数值研究。结果表明：在设计工况下（V_∞=10 m/s）,结节放置在叶片展向81%位置时,叶片根部的回流区域消除,但结节处的旋涡扰动会破坏叶片稳定流动,使叶片性能相对较低。在高风速下（V_∞=15、20、25 m/s）,由于前缘结节的结构特征,叶片表面产生旋涡,发生阻塞作用,叶片吸力侧压力减小,叶片正背面压差增大,升力增大,进而使仿生叶片的性能得到提升。     

基于载荷优化的大型风电机组μ-synthesis鲁棒控制

基于结构奇异值μ理论,针对大型风电机组柔性结构不确定性,提出动态载荷控制系统的鲁棒控制器设计方法。考虑结构的模态参数不确定性和高阶未建模动态,通过选取适当加权函数分离出系统的不确定部分,设计独立变桨距鲁棒μ控制器,用DK迭代算法求解控制器,以使闭环系统同时满足鲁棒性和稳定性要求,利用Matlab/S4WT软件进行仿真试验验证,仿真结果表明对载荷振动有很好的抑制效果。     

一种结合双重移相与鲸鱼算法的直流变换器回流功率控制策略

考虑双有源桥（DAB）流变换器在储能应用场景,建立双端口DAB等效数学模型;在不同移相条件下推导回流功率、传递功率与移相角之间的函数关系。根据适应度函数和惩罚函数,提出双重移相与鲸鱼算法结合的控制策略（DPS-WOA）,可抑制回流功率;该策略可获得全移相比范围内恒定传输功率情况下的最小回流功率移向比组合。DPS-WOA工作原理是：通过鲸鱼优化算法（WOA）对双重移相（DPS）控制方法下的DAB回流功率模型进行最小值求解。对比现有的单重移相（SPS）、DPS、三重移相（TPS）传统优化控制方案验证了控制策略具有更好的回流功率优化能力和全移向范围优化能力。     

针对风电波动抑制的电池储能SOC鲁棒优化模型

针对风电系统输出功率波动问题,研究了基于风电波动异常数据判断及波动幅度情景划分的,能够有效抑制风电波动的电池储能系统SOC鲁棒优化模型。研究电网调度需求、风电功率预测及风电波动数据特性,建立了风电波动异常数据鲁棒判别模型,为风电波动场景划分提供数据支持;研究不同风电波动幅度场景电池储能系统SOC适应性指标,建立了在不同风险容许度下的SOC鲁棒优化模型;研究基于风电波动数据及其对应的电池储能系统SOC历史数据的SOC鲁棒优化模型求解方法,建立了基于χ2散度函数的SOC鲁棒优化算法;根据风电出力波动及电池储能系统特性,建立了基于特定区域电网的电力系统仿真模型,并根据风电波动特性对电池储能系统SOC优化结果进行仿真分析。仿真结果表明,文章针对风电波动抑制建立的电池储能SOC鲁棒优化模型,对风电波动幅度具有明显的抑制效果,提升了电网运行稳定性。     

建筑室内人体太阳辐射得热特性及数值模拟

太阳辐射对建筑室内人体热舒适和建筑能耗有着显著影响。通过实测验证了daylight coefficient（DC）算法模拟太阳辐射强度的准确性。随后基于假人仿真模型采用DC算法计算室内人体平均辐射温度增量（Δ）,与SolarCal（SC）算法结果作对比,并对SC算法进行改进。相比原SC算法,改进SC算法与DC算法计算散射辐射ΔMRT最大差值从4.66 ℃优化到0.92 ℃。然后通过改进SC算法数值模拟案例分析了建筑室内人体全年太阳辐射得热特性,为评估被动太阳能建筑性能及改进暖通空调系统参数调控提供参考。     

基于改进混合差分进化算法的水轮机调速系统空载PID参数优化

为优化水轮发电机组调速系统空载PID相关参数,提出一种改进混合差分进化算法(IHDE),即先根据某电站混流式水轮发电机组建立相应空载工况数学模型,其次引入IHDE对空载工况数学模型PID参数进行优化,由于DE算法易出现早熟收敛问题,对该算法缩放因子的取值进行优化;同时,为了增强差分进化算法(DE)的全局搜索性能,在DE的选择阶段加入模拟退火算法的个体更新机制进行选择操作;然后,提出一种改进的综合ITAE指标用于评价空载扰动的仿真结果,使得目标函数的评价方式更加准确且符合相关标准要求;最后,通过试验仿真与不同算法进行对比,验证了IHDE优化水轮发电机组调速系统PID参数的有效性。     

含分布式发电并网虚拟发电厂鲁棒优化经济调度方法

文章基于鲁棒优化理论建立了虚拟发电厂最优经济调度模型。首先以虚拟发电厂发电净收益最大为目标函数,计及出力计划约束、机组运行约束、机组启停约束、储能运行约束等必要约束条件建立系统优化运行模型;然后考虑风光出力区间不确定性,以风光出力为自然决策者,以虚拟发电厂为系统决策者,分别制定博弈策略和支付,建立虚拟发电厂最优经济调度鲁棒优化模型,并对其Nash均衡点进行分析;基于两阶段松弛法将所建立的鲁棒优化模型转化为有限可解的步骤;最后通过一个算例验证了所建立的模型在制定虚拟发电厂运行计划方面的经济优势。     

基于智能算法的电厂机组负荷分配问题研究

通过对火力发电厂DCS数据采集系统中不同时刻、不同机组输出功率所对应的煤耗数据和NO、SO_2的排放数据进行分析,通过二次拟合的方法分别得到以功率P为自变量,以煤耗量和污染物排放为因变量的2个单目标函数。利用目标函数对电厂经济性能的权重影响不同的原理得到权重系数,进而求得综合加权模型。分别用模拟退火算法(SA)、基因遗传算法(GA)、遗传模拟退火算法(GA-SA)对于模型进行求解,并分别通过鲁棒性能、寻优效率、优化效果3个方面对3种算法进行评价。分析得出,在解决机组负荷优化分配问题时,使用动态加权方法简化模型,选用GA-SA算法求解模型能够获得理想的负荷分配。     

无源网络接入B2B型MMC-HVDC的Lyapunov控制策略研究

模块化多电平变流器（MMC）用于高压直流输电（HVDC）系统中时,通常采用PI控制方法,但这种策略存在参数选取繁杂、动态性能较差的缺点。为了提高高压直流输电（HVDC）系统的动态性能、简化参数选取,提出背靠背（B2B）型模块化多电平变流器-高压直流输电（MMC-HVDC）系统的Lyapunov控制策略。首先,建立B2B型MMC-HVDC数学模型;然后,设计Lyapunov函数控制策略;其次,在控制部分加入环流抑制、移相载波调制等,以完整地实现系统功能;最后,在Matlab/Simulink平台上验证了Lyapunov控制策略具有参数选择容易、动态性能优良的特点。     

考虑电转气消纳风电的电-气综合能源系统两阶段鲁棒协同调度

针对传统随机规划方法和区间优化方法处理风电出力不确定性的不足之处,该文提出含电转气设备的电力-天然气综合能源系统两阶段鲁棒协同调度模型,并考虑天然气网络运行约束对燃气轮机和电转气设备调度出力及备用配置的影响。模型以风电基准场景下系统的日前调度运行成本及最劣风电场景下实时调度成本之和为目标函数,建立具max-min结构的双层优化模型,并在主/子问题求解框架下采用列约束生成（C&CG）方法进行求解。最后,在Matlab平台下构建仿真算例验证所提鲁棒协同调度模型的有效性。     

弱电网下光伏并网逆变器的数字H∞鲁棒控制器设计

针对弱电网下光伏并网逆变器可能发生的失稳现象,提出一种光伏并网逆变器的数字H_∞鲁棒控制器设计方法。首先建立传统控制策略下光伏并网逆变器的控制模型,并分析其在弱电网条件下的稳定性。为了提高LCL型光伏并网逆变器对电网阻抗变化的鲁棒性,考虑数字控制延时的影响,通过建立离散域下光伏并网逆变器的标准H_∞控制模型,并设计加权函数,求得光伏并网逆变器的数字H_∞控制器。与传统的双闭环控制策略相比,理论分析与仿真和实验结果均表明,所设计的数字H_∞控制器可显著增强弱电网条件下光伏并网逆变器的稳定性。     

Parameter identification of an SOFC model with an efficient,adaptive differential evolution algorithm

An efficient, adaptive differential evolution (DE) algorithm is proposed in which DE parameter adaptation is implemented. A ranking-based vector selection and crossover rate repairing technique are also presented. The method is referred to as IJADE (Improved Jingqiao Adaptive DE). To verify the performance of IJADE, the parameters of a simple SOFC electrochemical model that is used to control the output performance of an SOFC stack are identified and optimized. The SOFC electrochemical model is built to provide the simulated data. The results indicate that the proposed method is able to efficiently identify and optimize model parameters while showing good agreement with both simulated and experimental data. Additionally, when compared to other DE variants and other evolutionary algorithms, IJADE obtained better results in terms of the quality of the final solutions, robustness, and convergence speed.     

基于LMI的直流微电网分布式鲁棒H∞控制

针对直流微电网中分布式发电单元（DGU）投入或退出引起的结构不确定性,采用分布式鲁棒H∞控制,使微电网电压稳定。采用区域极点配置约束控制器参数,使闭环系统的极点落在区域D中。通过线性矩阵不等式的H∞优化求解,得到鲁棒H∞-γD控制器。控制器的设计仅需DGU和与之连接的线路参数,而无需整个微电网参数。给每个DGU分别配置相应的控制器,嵌入DGU中,可以实现分布式控制。仿真结果表明,该控制方法有较好的鲁棒性。     

Simultaneous Retrieval of Parameters in a Transient Conduction-Radiation Problem Using a Differential Evolution Algorithm

This article deals with the application of the differential evolution (DE) algorithm for the inverse analysis of a transient conduction-radiation heat transfer problem. Thermophysical properties and/or optical properties of the medium are simultaneously retrieved with a known temperature field. The conducting-radiating planar enclosed medium bounded by diffuse-gray boundaries is absorbing, emitting, and scattering. In both the direct and inverse methods, the energy equations are solved using the lattice Boltzmann method (LBM), and the finite volume method (FVM) is used to compute the radiative information. In the inverse method, the objective function is minimized using the DE algorithm. Any two sets of parameters, viz., the extinction coefficient, the scattering albedo, emissivity and conduction-radiation parameter, are simultaneously retrieved. The effects of key DE algorithm parameters, such as the weighting factor and the crossover constant on the quality of solutions, are studied. Measurement errors are accounted. The accuracy of the DE algorithm is compared with the genetic algorithm. The DE algorithm is significantly faster, and it yields the global optimum for a wide range of parameters.     

Microgrid Dynamic Performance Improvement Using a Doubly Fed Induction Wind Generator

This paper describes a control approach applied on a doubly fed induction generator (DFIG) to provide both voltage and frequency regulation capabilities, and hence, an improvement in the dynamic behavior of a microgrid system. The microgrid system is assumed to be a portion of a medium voltage distribution feeder and is supplied by two distributed generation (DG) units, i.e., a gas-turbine synchronous generator and a variable-speed wind turbine with DFIG. A control approach algorithm is proposed for the DFIG unit to improve both voltage and primary frequency controls. Two distinct operation modes, i.e., grid-connected and islanding mode, are used in the proposed approach for proper transfer from normal to islanding operation. Case studies are simulated based on both planned and unplanned islanding scenarios to evaluate the performance of the control approach. The study results show that the proposed control approach for DGs in the microgrid increase the microgrid system's dynamic performance, reduce frequency changes, and improve bus voltages regulation during islanding and autonomous operations.      

风光柴储微电网容量优化配置的运行策略

设计合理的运行策略是微电网容量优化配置过程中的关键因素之一。以典型独立型风光柴储微电网为研究对象,基于分布式电源的特征,将微电网运行工况划分为7种状态,进而找出设计运行策略的关键问题：当储能（BES）、柴油发电机（DE）的可输出最大功率之和大于净负荷功率时,如何分配净负荷。针对该关键问题提出首选BES分配净负荷、首选DE分配净负荷、DE趋向额定功率分配净负荷3种运行策略。基于解算多目标优化模型的计算结果分析3种运行策略的特点,指出各自的适用场合。     

A critical evaluation of EA computational methods for Photovoltaic cell parameter extraction based on two diode model

Due to its ability to handle nonlinear functions regardless of the derivatives information, evolutionary algorithms (EA) are envisaged to be very effective for extracting parameter of photovoltaic (PV) cell. This paper presents critical evaluation of the parameters extraction of two diode PV model using three EA methods, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE). For DE, two variations are proposed: (1) boundary based differential evolution (B-DE) and (2) penalty based differential evolution (P-DE). The performance of each method is evaluated based on several factors: accuracy and consistency of solution; speed of convergence; computational efficiency and the required number of control parameters. Comparisons are carried out using synthetic data and are validated by six PV modules of different types (multi-crystalline, mono-crystalline, and thin-film) from various manufacturers. Information derived from these critical evaluations can be useful to determine the best computational method to build an efficient and accurate PV system simulator.     

A genetic evolving ant direction DE for OPF with non-smooth cost functions and statistical analysis

This paper proposes an evolving ant direction differential evolution (EADDE) algorithm for solving the optimal power flow problem with non-smooth and non-convex generator fuel cost characteristics. The EADDE employs ant colony search to find a suitable mutation operator for differential evolution (DE) whereas the ant colony parameters are evolved using genetic algorithm approach. The Newton–Raphson method solves the power flow problem. The feasibility of the proposed approach was tested on IEEE 30-bus system with three different cost characteristics. Several cases were investigated to test and validate the robustness of the proposed method in finding the optimal solution. Simulation results demonstrate that the EADDE provides superior results compared to a classical DE and other methods recently reported in the literature. An innovative statistical analysis based on central tendency measures and dispersion measures was carried out on the bus voltage profiles and voltage stability indices.     

基于神经网络的最优带宽风电并网自抗扰控制

为了提高风能转换系统在故障穿越期间直流母线电压的暂态特性,解决线性自抗扰控制（LADRC）中固定的带宽带来的观测器响应速度和系统抗干扰性之间的矛盾,在线性自抗扰控制（LADRC）的基础上提出一种基于神经网络的最优带宽线性自抗扰控制（LADRC-OB）策略。首先分析带宽对系统性能的影响,然后根据系统已知模型设计LADRC控制器,并利用BP神经网络算法通过直流母线电压的参考值与实际值之间的偏差调整网络的输出。而神经网络的输出为LADRC的2个重要参数——观测器带宽ω₀和控制器带宽ω_c,这可解决LADRC的参数整定问题。最后,将LADRC-OB应用于1.5 MW的风能转换系统仿真模型中,并与采用双闭环PI时的控制效果进行对比,验证LADRC-OB具有更好的控制特性。此外,还对LADRC-OB的稳定性进行分析。