基于CFD和NCPSO的复杂地形风电场微观选址优化

针对复杂地形条件下风电场微观选址技术难度大的问题,提出一种基于数值计算结果和高效优化方法的微观选址优化算法。将测风数据按风向等分成12个扇区,并利用平均风速和CFD对复杂地形的每个扇区进行数值模拟,得到风电场各扇区的风资源分布,提取轮毂高度处的风速和风向分布。优化中风力机的尾流影响采用Jensen尾流模型,风电场风能计算中风速按照威布尔分布处理,并考虑每个扇区风速的大小、概率密度。目标函数为整个风电场的输出功率倒数的对数,自变量为风力机在给定风电场中的位置坐标,约束条件为地形边界和风力机之间的最小距离,优化算法采用该文提出的改进小生境粒子群算法(NCPSO),优化风力机组微观选址的最优解。该文提出优化算法得到的结果与基于高度的经验布置方法(EX-TH)、基于风能密度的经验布置方法(EX-PH)以及普通粒子群算法(PSO)进行比较,证明在复杂地形条件下所提出方法的可靠性与有效性,并可应用于工程实践。     

适用于核动力设备故障诊断的改进粒子群优化算法

提出一种基于改进粒子群优化(IPSO)算法的核动力设备故障诊断方法.利用已知核动力设备故障征兆集合,选用概率因果模型求解具有最大后验概率的故障集合;在传统粒子群优化(PSO)算法的基础上,利用佳点集原理对PSO算法进行初始化,优化了粒子群的初始化范围;借助自适应调整的惯性权重法,避免PSO算法未成熟收敛,加快了收敛速度.最后通过算例证明该方法的有效性.结果表明:基于改进粒子群优化算法的概率因果模型不受故障样本的限制,具有较好的通用性,且模型故障诊断精度较高、寻优速度快.     

基于改进的PSO算法的电力系统无功优化研究

电力系统无功优化是保证电力系统安全、经济运行的重要措施,粒子群优化算法（PSO）具有模型简单、收敛速度快、参数简洁等优点,但用于求解高维复杂优化问题时易陷入局部最优,针对此缺陷,在PSO算法的基础上提出了自适应随机变异粒子群优化算法（AMPSO）,将该算法用于求解电力系统无功优化问题,并以IEEE30标准节点系统为算例进行验证。结果表明,与PSO算法相比,AMPSO算法有效降低了系统网损,显现出良好的全局收敛特性。     

基于改进秃鹰算法的微电网群经济优化调度研究

传统优化算法相较于智能优化算法在求解微电网群优化调度问题中较难寻出可行解或最优解,对此提出一种基于融合反向学习和柯西变异改进的秃鹰算法（IBES）,在秃鹰搜索空间猎物阶段采用融合反向学习和柯西变异策略,使得秃鹰算法有效跳出局部最优,解决算法求解精度低等问题。通过与粒子群算法（PSO）、麻雀算法（SSA）、鲸鱼算法（WOA）进行对比,仿真结果表明IBES寻优精度更高,可有效减少微电网群系统的经济成本。     

基于NCPSO的微网群优化调度策略研究

传统的粒子群优化（PSO）算法因在微网优化中不易达到全局最优而导致微网运行成本过高,该文采用小生境混沌粒子群优化（NCPSO）算法对混合微网群的运行策略进行协同优化,以实现区域微网经济性最优、环境治理成本最低、风光等可再生能源利用率高等目的。根据所提出的调度策略,建立的优化调度模型包括动态电价下的负荷模型、经济收益模型以及成本模型等,使用NCPSO算法得到多微网在一个周期内的最佳运行状态,实现微网群系统综合能源的互动调控、空间互补。通过分析微网群的功率交互动态、可控能源的发电以及储能电池的荷电状态等,验证微网群的电力负荷响应动态电价,表明了NCPSO算法优化微网群运行的优越性、有效性。     

小波包能量熵和改进的LSSVM在风力机轴承故障诊断中的应用

针对极端复杂工况下风力机轴承运行状态监测中的故障诊断问题,提出一种基于小波包能量熵故障特征提取并结合鲸鱼算法(WOA)优化最小二乘支持向量机(LSSVM)进行故障分类识别的风力机轴承故障诊断方法。通过小波包分解提取各频带成分的能量熵值构建故障特征集,同时针对LSSVM参数的选取依赖人工选择的盲目性问题,采用鲸鱼优化算法寻找LSSVM中最优的2个关键参数正则化参数和核函数参数,以此提高故障诊断模型的分类精度。通过不同工况下的试验数据集测试,实现了对不同故障状态特征参数的准确分类。结果表明,所提方法诊断结果优于遗传算法(GA)和粒子群算法(PSO)分别优化的LSSVM.远优于传统的LSSVM算法。     

基于多策略改进PSO算法的微电网优化调度研究

为解决微电网中随机性、波动性和分布式电源出力管理困难问题,提出一种并网模式下微电网优化调度的综合经济模型。首先,考虑蓄电池和峰谷电价的影响,计及失负荷和蓄电池超容惩罚成本,建立以微电网运行成本和污染物排放成本最低的目标函数;其次,引入白鲨优化算法（WSO）的全局动态捕猎特性,对粒子群优化算法（PSO）进行了Tent混沌映射、动态时间因子和多项式变异的多策略改进,并将其应用于求解多目标多约束非线性的微电网优化问题。仿真结果表明,与PSO算法和麻雀搜索算法（SSA）相比,改进PSO算法用于微电网优化调度可降低微电网运行成本、减少环境污染并提高新能源供电的可靠性。     

遮阴条件下光伏MPPT自适应粒子群算法优化

针对在局部阴影情况下光伏阵列的功率-电压（P-U）特性曲线呈多峰特性,粒子群算法应用于局部阴影下的最大功率点跟踪（MPPT）跟踪,存在搜索速度慢、精度低的缺点。提出自适应惯性权重粒子群优化（PSO）算法的最大功率点跟踪算法,自动更新惯性权重w和学习因子C₁、C₂,通过仿真实验,优化前的全局最大功率点（GMPP）跟踪时间是0.045 s,输出功率为468 W。优化后的自适应粒子群算法GMPP跟踪时间为0.020 s,输出功率稳定在为480 W,光伏阵列的输出功率跟踪误差小于30%。在所搭建辐照度突变模型仿真中,在4.022 s突变到300 W/m²时经过0.05 s又重新跟踪到了新的最大功率点稳定在0.075 MW。最后通过实验平台验证,优化后的自适应粒子群优化算法与传统的粒子群优化算法相比,追踪时间减少了55.5%,误差小于5%,验证了该算法可行性和实用性。     

基于改进PSO的复杂环境下光伏MPPT控制

复杂环境下,传统算法实现光伏系统的最大功率点跟踪(MPPT)失效;常规粒子群优化算法(PSO)实现的MPPT控制易给系统引入较大跟踪振荡。针对上述问题,提出一种基于改进PSO的MPPT控制方法,通过引入线性调整因子,自适应调整环境突变时最大功率点电压的搜寻范围。结果表明:与常规PSO算法相比,改进的PSO算法跟踪速度更快、动态响应振荡更小、能量转换率更高,可实时准确跟踪复杂环境下的最大功率点。     

超超临界机组快速调峰磨煤机组合优化

基于磨煤机节能运行的控制需求,建立了某660MW机组磨煤机能耗模型,根据磨煤机实际运行状况建立相应的约束条件,并对标准粒子群优化(PSO)算法进行初始化改进、惯性权值改进和变异算子的引入后用于磨煤机组合出力的优化策略中.经试验确定各项参数后,对该策略进行Matlab仿真.结果表明:通过改进的PSO算法计算得出的磨煤机组合出力方式对磨煤机的节能运行起到了很大作用.     

上风向山头对风电机组的安全性影响研究

  目的  由于生态保护和风场边界等条件的限制,有些机位点的主风向方向存在明显山头障碍物遮挡,影响了机组的发电量和安全性能,文章旨在研究减小山头对机组影响的方法。  方法  基于STAR-CCM+软件平台对主风向上有山头遮挡的机位点附近地形进行了数值模拟,分析了扇区管理、提高轮毂高度、地形修整等方法对机组的安全影响。  结果  结果表明:扇区管理、提高轮毂高度和地形修整都能改善风机的安全性。但在该项目中,采用双平台的地形修整的方法对改善风机安全性更加有效。  结论  分析结果可为如何降低来风方向的山头对风机的影响提供方法参考。     

基于BP神经网络方法的风电场风速插补分析应用

[目的]准确的风资源数据对风场的风资源评估和发电量计算有着重大意义.由于机械故障、天气因素和人为影响等原因,风场内风速数据出现采集时间短、间断点多、数据失真等诸多问题,给风资源的评估带来不小的麻烦.[方法]现阶段风电行业内采用基于相关测量预测方法(MCP,Measure-Correlate Predict)(可称之为传...     

Site‐specific Design Optimization of Wind Turbines

This article reports results from a European project, where site characteristics were incorporated into the design process of wind turbines, to enable site‐specific design. Two wind turbines of different concept were investigated at six different sites comprising normal flat terrain, offshore and complex terrain wind farms. Design tools based on numerical optimization and aeroelastic calculations were combined with a cost model to allow optimization for minimum cost of energy. Different scenarios were optimized ranging from modifications of selected individual components to the complete design of a new wind turbine. Both annual energy yield and design‐determining loads depended on site characteristics, and this represented a potential for site‐specific design. The maximum variation in annual energy yield was 37% and the maximum variation in blade root fatigue loads was 62%. Optimized site‐specific designs showed reductions in cost of energy by up to 15% achieved from an increase in annual energy yield and a reduction in manufacturing costs. The greatest benefits were found at sites with low mean wind speed and low turbulence. Site‐specific design was not able to offset the intrinsic economic advantage of high‐wind‐speed sites. It was not possible to design a single wind turbine for all wind climates investigated, since the differences in the design loads were too large. Multiple‐site wind turbines should be designed for generic wind conditions, which cover wind parameters encountered at flat terrain sites with a high mean wind speed. Site‐specific wind turbines should be designed for low‐mean‐wind‐speed sites and complex terrain. Copyright © 2002 John Wiley & Sons, Ltd.     

Large‐eddy simulation of turbulent flow past wind turbines/farms: the Virtual Wind Simulator (VWiS)

A large‐eddy simulation framework, dubbed as the Virtual Wind Simulator (VWiS), for simulating turbulent flow over wind turbines and wind farms in complex terrain is developed and validated. The wind turbines are parameterized using the actuator line model. The complex terrain is represented by the curvilinear immersed boundary method. The predictive capability of the present method is evaluated by simulating two available wind tunnel experimental cases: the flow over a stand‐alone turbine and an aligned wind turbine array. Systematic grid refinement studies are carried out, for both single turbine and multi‐turbine array cases, and the accuracy of the computed results is assessed through detailed comparisons with wind tunnel experiments. The model is further applied to simulate the flow over an operational utility‐scale wind farm. The inflow velocities for this case are interpolated from a mesoscale simulation using a Weather Research and Forecasting (WRF) model with and without adding synthetic turbulence to the WRF‐computed velocity fields. Improvements on power predictions are obtained when synthetic turbulence is added at the inlet. Finally the VWiS is applied to simulate a yet undeveloped wind farm at a complex terrain site where wind resource measurements have already been obtained. Good agreement with field measurements is obtained in terms of the time‐averaged streamwise velocity profiles. To demonstrate the ability of the model to simulate the interactions of terrain‐induced turbulence with wind turbines, eight hypothetical turbines are placed in this area. The computed extracted power underscores the significant effect of site‐specific topography on turbine performance. Copyright © 2014 John Wiley & Sons, Ltd.     

Optimization of wind turbine micro-siting for reducing the sensitivity of power generation to wind direction

In the optimization of wind turbine micro-siting of wind farms, the major target is to maximize the total energy yield. But considering from the aspect of the power grid, the sensitivity of wind power generation to varying incoming wind direction is also an essential factor. However, most existing optimization approaches on wind turbine micro-siting are focused on increasing the total power yield only. In this paper, by employing computational fluid dynamics and the virtual particle model for the simulation of turbine wake flow, a sensitivity index is proposed to quantitatively evaluate the variation of power generation under varying wind direction. Typical turbine layouts obtained by existing power optimization approaches are evaluated for stability. Results indicate that regularly arranged turbine layouts are not suitable for stable power production. Based on solutions from the power optimization, a second-stage optimization using Particle Swarm Optimization algorithm is presented. The proposed optimization method adjusts the positions of the turbines locally, aiming at increasing the stability of wind farm power generation without damaging its advantage of high power yield. Case studies on flat terrain and complex terrain both demonstrate the effectiveness of the present local adjustment optimization method.     

基于粒子群优化BP神经网络的风电机组齿轮箱故障诊断方法

提出了一种基于粒子群优化BP神经网络风电机组齿轮箱故障诊断方法。粒子群算法不需要计算梯度,可以兼顾全局寻优和局部寻优。利用粒子群算法对BP网络权值和偏置进行优化,减少了BP神经网络算法陷入局部最优解的风险,提高了神经网络的训练效率,加快了网络的收敛速度。考虑风电齿轮箱振动信号的不确定性、非平稳性和复杂性,提取功率谱熵、小波熵、峭度、偏度、关联维数和盒维数作为故障特征。经测试,算法诊断结果正确,表明了PSO优化BP神经网络用于风电机组齿轮箱故障诊断的有效性和实用性。     

基于CFD的复杂地形风电机组机位微地形风资源数值模拟研究

  [目的]  在复杂地形进行风电机组建设时,机位点周围易形成高边坡地形,改变了风资源分布特征,影响机组发电量和安全性能,文章旨在研究高边坡地形的影响因素和预防其对机组的危害。  [方法]  基于STAR-CCM+软件平台对实际风电场机组高边坡地形进行了数值模拟,分析了立机位置选择、坡度、开挖深度等因素对机位处风资源参数的影响。  [结果]  结果表明:高边坡改变了原地形风的加速效应,产生大幅度的流体分离。当高边坡位于机组上风向时,机组下叶尖易产生较大的风切变,湍流强度超标,而高边坡在机组下风向时,机位处风资源参数则正常;高边坡的坡度大小对坡前立机风参数无显著影响;当下叶尖高度低于边坡高度时,机组位置下叶尖风速很小,切变值易超标。  [结论]  分析结果可为高边坡地形机组施工平台设计和塔筒高度选择提供参考。     

复杂地形下风力机尾流数值模拟研究和激光雷达实验对比

以CFD数值计算和实验相结合的方法,对处于中国西南某多山地区陆上风电场的尾流特性进行研究,验证不同数值方法在复杂地形的适用性。首先采用2台激光雷达,测量目标风力机一个月内的自由来流风速和尾流廓线,在地形上坡加速效应下,不同大气稳定度下目标风力机的自由来流风速廓线均呈负梯度。然后分别采用经典致动盘和改进致动盘法,模拟目标风力机在主风向下的尾流发展。不同于只有风速与压降关系的经典致动盘法,改进致动盘法更考虑了叶片几何和气动参数(尺寸信息、攻角、桨距角、升阻力系数等)。通过与后置激光雷达尾流测试结果对比,这2种基于CFD技术的数值模拟方法,计算网格相同,计算时间相当,且均能较好地模拟因为复杂地形而引起的尾流偏转;其中改进致动盘的尾流形状与激光雷达相似,速度亏损也更接近激光雷达结果。因此,改进致动盘法更适合于复杂地形条件下风场模拟,较好平衡了计算的效率与精度。     

聚风导流型立轴风力机设计及气动性能分析

针对传统的立轴风力机风能利用率低的问题,应用正交优化法和流场数值模拟技术对聚风导流型立轴风力机的结构设计参数进行了优化设计。在同尺度下与传统立轴风力机进行了对比分析。结果表明,聚风导流型立轴风力机叶轮的输出功率、风能利用率及自启动特性均得到了显著提高。