输电塔-覆冰导线耦合体系非线性动力响应分析

为了全面准确的了解输电线路塔线耦合体系的动力特性以及覆冰导线的非线性舞动响应,建立了“三塔两线”的空间有限元模型.选用风工程界广泛应用的Davenport脉动风速谱,叠加相应位置的平均风速,模拟风速时程.运用现有的新月形覆冰计算模型,采用时程分析法分别对单塔、无覆冰塔线体系以及不同攻角下的输电塔-覆冰导线体系进行了风荷...     

基于GNSS-R的反演海面风速技术的研究

对利用全球导航定位系统的海洋反射信号(GNSS-R)反演海面风速的方法进行了研究。GNSS-R技术作为一种新型的、低成本的海洋微波遥感测风技术,与其他测风技术相辅相成,弥补了某些测风手段的不足。文中还讨论了散射信号相关功率模型中的散射截面、多普勒区、等延时区、天线覆盖区四部分函数的定义和性质。使用Elfouhaily海浪谱模型,数值模拟了机载高度下散射信号相关功率的理论波形,在此基础上,又结合机载高度下获得的实测数据反演得到海面风速,反演得到的风速的均值与试验时浮标数据所对应的风速的均值比较相差1.4 m/s,误差在可接受的范围内,反演得到的风速与浮标数据相一致。     

气象回波特征随机过程的模拟

文章在Matlab环境中模拟了同时具有气象回波一阶统计特征和二阶统计特征的随机过程.首先,利用反变换法、同分布随机过程合成方法、变量变换法、Matlab函数直接生成法模拟了具有气象回波特征随机过程的一阶特征.在模拟气象回波特征的二阶特性时,选择AR模型模拟高斯功率谱,在阶数的选择上采用了一种优化简便的方法,实现了更好的模拟效果.模拟立方功率谱时,提出了在Matlab环境中采用频率采样法达到了更好的模拟效果.     

基于AR模型的特殊路面的路面谱测量及分析

提出了一种特殊路面的路面谱的测量方法,在运输车辆车桥上安装加速度传感器,通过测量该处的时域加速度信号来间接计算路面功率谱;运用DASP和MATLAB等软件对测试数据进行处理,并对特殊路面不平度建立AR模型和进行谱估计,得到了多种特殊路面的位移功率谱密度曲线图;结果表明,该实验方法是可行的,同时提出了一种基于AR模型的特殊路面的路面谱测量手段,并验证了用AR模型法对特殊路面进行功率谱分析的可靠性和可行性。     

基于海浪谱的Gerstner波浪模拟

本文从海洋学现有的观测和研究成果出发,结合海浪的Gerstner模型,提出一种风力作用下的基于海浪谱的三维Gerstner海浪模型。首先,通过分析Gerstner模型各个参数的随机性,利用海浪谱和方向谱相关公式,获得固定风速下模型各随机参数值,建立固定风速下的海浪模型;然后,在此基础上进一步构造了基于海浪谱的风力模型,模拟了风力作用下的海浪;最后,采用基于视点的网格拓扑结构,实现了海浪的实时绘制。实验结果表明,该模型不仅能更真实地模拟海浪,还具有较好的交互性。     

基于AR模型的多普勒散射计回波功率谱估计

当采样点数少或流速较小时,采用周期图法估计的多普勒散射计(Doppler scatterometer, DopSCAT)回波功率谱分辨率低,多普勒频移提取精度低。对此,提出了一种基于自回归(autoregressive, AR)模型的DopSCAT回波功率谱估计方法。该方法为DopSCAT回波信号建立含有未知参数的AR模型,采用赤池信息量准则自适应确定模型的最优阶数;对定阶后的AR模型采用Burg算法计算模型参数,利用得到的AR模型估计回波功率谱;对功率谱进行峰值检测提取多普勒频移计算径向流,采用两个观测方位向的径向流合成得到海面流场。利用OSCAR海流数据进行了回波功率谱估计与海流反演实验。分析结果表明,与周期图法相比,该方法能够显著提高回波功率谱分辨率和多普勒频移的提取精度,进而提高了海流的反演精度。     

随机风作用下高速列车动力学参数的可靠性优化设计

建立随机风作用下高速列车动力学参数的可靠性优化设计方法．首先考虑自然风的脉动特性，采用Cooper理论和谐波叠加法模拟随车移动点的脉动风速，给出随机风作用下高速列车非定常气动载荷的计算方法．然后建立高速列车车辆系统动力学模型，计算高速列车的运行安全性，并基于可靠性理论，给出随机风作用下高速列车失效概率的计算方法．在此基础上，以高速列车动力学参数为优化设计变量，以失效概率和轮轴横向力为优化目标，采用多目标遗传算法NSGA—II进行动力学参数的自动寻优，建立随机风作用下高速列车动力学参数的可靠性优化设计模型．经可靠性优化计算，高速列车的失效概率由原始的0．4884降低为0．1406，轮轴横向力由原始的45．13kN降低为43．01kN．通过优化高速列车动力学参数可以显著改善随机风作用下高速列车的运行安全性．     

Kalman滤波在Yule-Walker谱估计中的应用

在现代谱估计中,由于Yule-Walker沃克谱估计算法中平稳随机序列的长度n在某些情况下偏小,使计算出的ARMA随机过程的功率谱密度不能精确逼近真实值,所以我们在用自相关法估计AR模型参数时加入了kalman滤波器。将估计的AR模型系数及高斯白噪声作为滤波器的输入及部分参数,对最终估计的功率谱进行修正。实验结果表明,...     

风轮机桨距角的数据驱动预测控制

数据驱动预测控制方法是数据驱动和预测控制相结合而产生的一种新型控制策略.传统的模型预测控制(MPC)方法使用的预测模型一般都是近似线性系统预测模型.在具有强非线性的工业过程中,数据驱动预测控制方法对系统模型要求更低,控制效果更好.针对额定风速以上风轮机桨距角的控制问题,利用输入输出数据在线设计控制器,将数据驱动预测控制方法较好的应用于风力发电系统中,实现额定风速以上系统的恒功率控制.仿真结果表明该方法能够成功控制风轮机桨距角,达到系统恒功率输出的控制目标,同时保持转速在额定值附近.     

雷达杂波建模与仿真方法研究

为了模拟雷达杂波环境.本文采用统计模型对雷达杂波进行了模拟.时服从一定幅度分布和功率谱分布的杂波序列产生方法进行了分析,给出了每种统计模型的模拟步骤,并根据零记忆非线性变换法(zMNL)和球不变随机过程(SIRP)对杂波进行了计算机仿真,给出了有价值的仿真结果.     

A real multi-parent tri-hybrid evolutionary optimization method and its application in wind velocity estimation from wind profiler data

A real-coded multi-parent tri-hybrid evolutionary algorithm (EA) for problem optimization is presented. The hybrid EA algorithm combines the features of Simplex, stochastic relaxation (SR) and multi-parent EA reproduction in a model that encourages competition among the best individual solutions from various operations. Its strength has been evaluated using standard test functions and shown to do better than other methods. The algorithm’s ability to handle noise is evident when applied to experiments involving resolution of overlapping wind profiler (WP) data. Results obtained using raw data closely matched those obtained with data preprocessed by a low-pass FFT filter. Resolution of low-speed wind and clutter signals in various degrees of overlap is made possible, thereby allowing the determination of wind velocity and variance to be executed with ease.     

Minimum energy storage for power system with high wind power penetration using p-efficient point theory

Minimum energy storage （ES） and spinning reserve （SR） for day-ahead power system scheduling with high wind power penetration is significant for system operations. A chance-constrained energy storage optimization model based on unit commitment and considering the stochastic nature of both the wind power and load demand is proposed. To solve this proposed chance-constrained model, it is first converted into a deterministic-constrained model using p-efficient point theory. A single stochastic net load variable is developed to represent the stochastic characteristics of both the wind power and load demand for convenient use with the p-efficient point theory. A probability distribution function for netload forecast error is obtained via the Kernel estimation method. The proposed model is applied to a wind-thermal-storage combined power system. A set of extreme scenarios is chosen to validate the effectiveness of the proposed model and method. The results indicate that the scheduled energy storage can effectively compensate for the net load forecast error, and the increasing wind power penetration does not necessarily require a linear increase in energy storage.     

大型海上风力发电高塔系统一体化分析建模研究

大型海上风力发电高塔系统的精细化建模与整体可靠性设计对保障风能资源开发的安全性与经济性日益重要.本文结合柔体动力学与有限元建模,考虑气动耦合效应、桩土相互作用、塔体前后运动控制和非线性转矩控制,建立了大型海上风力发电高塔系统一体化分析模型(Sto DRAOWT模型).与国际上通用的风机分析软件分析结果对比表明,本文建立的Sto DRAOWT模型分析结果可信,建模更为全面合理,计算效率更高.在此基础上,实现了风浪联合作用下海上风力发电高塔整体系统的动力响应分析,为大型海上风力发电高塔系统的随机响应分析与可靠性设计奠定了基础.     

Vector model of wind for performance analysis of stand-alone wind power systems

A statistical model of speed of horizontal wind in the atmosphere, intended for estimating the efficiency of stand-alone wind power systems treated as nonlinear stochastic dynamic systems, is developed. The model is based on the representation of wind as a two-dimensional Gaussian–Markov random process. The method of calculating model parameters by using observation results of the wind magnitude and direction on the land-based weather station is presented. The model parameters are calculated for several locations in Russia. More precise and simplified variants of the model are proposed. The model adequacy is analyzed by comparing the system’s efficiency indexes with their reference values.     

A robust stochastic stability analysis approach for power system considering wind speed prediction error based on Markov model

This paper proposes a robust stochastic stability analysis approach with partly unknown transition probability by considering the wind speed prediction error in power system. Firstly, taking this prediction error into account, based on Markov modeling theory, the stochastic dynamic model of wind power system with uncertain transition probability is developed. Secondly, according to the stochastic stability theory of Markov jump system, the transition probability of wind power system mode is divided into three cases: fully known, only known upper and lower bounds, and completely unknown. Then, by using linear matrix inequality (LMI) technology, a robust stochastic stability criterion with disturbance attenuation is obtained. Finally, test results show that the proposed analysis approach does not need to obtain the trajectory of the actual system operation parameters, and has the advantages of high computational efficiency.     

Multi-model predictive control for wind turbine operation under meandering wake of upstream turbines

In wind farm operation, the performance and loads of downstream turbines are heavily influenced by the wake of the upstream turbines. Furthermore, the actual wake is more challenging due to the dynamic phenomenon of wake meandering, i.e. the turbine wake often demonstrates dynamic shift over time. To deal with the time-varying characteristics of wake meandering, a multiple model predictive control (MMPC) scheme is applied to the individual pitch control (IPC) based load reduction. The coherence function in the spectral method is used to generate the stochastic wind profile including wake meandering at upstream turbine, and a simplified wake meandering model is developed to emulate the trajectory of the wake center at downstream turbine. The Larsen wake model and Gaussian distribution of wake deficit are applied for composing wind profiles across the rotor of downstream turbines. A set of MMPC controllers are designed based on different linearized state-space models, and are applied in a smooth switching manner. Simulation results show significant reduction in the variation of both rotor speed and blade-root flapwise bending moment using the MMPC based IPC by including the wake meandering, as compared to a benchmark PI controller designed by NREL.     

Design of adaptive robust guaranteed cost controller for wind power generator

According to the increasing requirement of the wind energy utilization and the dynamic stability in the variable speed variable pitch wind power generation system, a linear parameter varying (LPV) system model is established and a new adaptive robust guaranteed cost controller (AGCC) is proposed in this paper. First, the uncertain parameters of the system are estimated by using the adaptive method, then the estimated uncertain parameters and robust guaranteed cost control method are used to design a state feedback controller. The controller’s feedback gain is obtained by solving a set of linear matrix inequality (LMI) constraints, such that the controller can meet a quadratic performance evaluation criterion. The simulation results show that we can realize the goal of maximum wind energy capture in low wind speed by the optimal torque control and constant power control in high wind speed by variable pitch control with good dynamic characteristics, robustness and the ability of suppressing disturbance.     

Joint operation of wind farms and pump-storage units in the electricity markets: Modeling,simulation and evaluation

Renewable power plants generation scheduling and unit commitment construct new problems of the power systems. In this paper, optimal scheduling of the joint operation (JO) and uncoordinated operation (UO) of wind farms and pump-storage plants in the energy and ancillary service markets are studied. For this purpose, a new method for modeling, simulation and evaluation of these units is presented. Since there are uncertainties in wind power generation (WPG) and the market prices, the scheduling problem is modeled by a stochastic optimization problem. Optimal bidding strategy of units is determined by solving this stochastic optimization problem. For this purpose, uncertainties are modeled by a scenario tree method. In order to evaluate the performance of the results of JO and UO of the plants, the value at risk (VaR) and the profit of the plants are compared. With JO of pump-storage plants and wind units, the profit of these plants in comparison with their UO will be increased. The results for pump-storage and wind farms of IEEE 118-bus standard system, verify that the JO of these units, improves the profit and VaR of the system.     

Data-driven and adaptive control applications to a wind turbine benchmark model

Wind turbines are complex dynamic systems forced by stochastic wind disturbances, as well as gravitational, centrifugal, and gyroscopic loads. Since their aerodynamics are nonlinear, wind turbine modelling is thus challenging. Moreover, accurate models should contain many degrees of freedom to capture the most important dynamic effects. Therefore, the design of control algorithms for wind turbines should account for these complexities. However, these algorithms must capture the most important turbine dynamics without being too complex and unwieldy. The main purpose of this study is thus to give two examples of viable and practical designs of control schemes with application to a wind turbine prototype model. Extensive simulations on the benchmark process and Monte-Carlo analysis are the tools for assessing experimentally the main features of the proposed control schemes, in the presence of modelling and measurement errors. These developed control methods are also compared with other different approaches, in order to evaluate advantages and drawbacks of the considered solutions. Finally, Hardware-In-the-Loop simulations serve to highlight the potential application of the proposed control strategies to real wind turbines.