Maximum Power Point Tracking Control for Non-Gaussian Wind Energy Conversion System by Using Survival Information Potential

In this paper, a wind energy conversion system is studied to improve the conversion efficiency and maximize power output. Firstly, a nonlinear state space model is established with respect to shaft current, turbine rotational speed and power output in the wind energy conversion system. As the wind velocity can be descried as a non-Gaussian variable on the system model, the survival information potential is adopted to measure the uncertainty of the stochastic tracking error between the actual wind turbine rotation speed and the reference one. Secondly, to minimize the stochastic tracking error, the control input is obtained by recursively optimizing the performance index function which is constructed with consideration of both survival information potential and control input constraints. To avoid those complex probability formulation, a data driven method is adopted in the process of calculating the survival information potential. Finally, a simulation example is given to illustrate the efficiency of the proposed maximum power point tracking control method. The results demonstrate that by following this method, the actual wind turbine rotation speed can track the reference speed with less time, less overshoot and higher precision, and thus the power output can still be guaranteed under the influence of non-Gaussian wind noises.     

Kolmogorov spectrum of renewable wind and solar power fluctuations

With increasing the contribution of renewable energies in power production, the task of reducing dynamic instability in power grids must also be addressed from the generation side, because the power delivered from such sources is spatiotemporally stochastic in nature. Here we characterize the stochastic properties of the wind and solar energy sources by studying their spectrum and multifractal exponents. The computed power spectrum from high frequency time series of solar irradiance and wind power reveals a power-law behaviour with an exponent ～ 5/3 (Kolmogorov exponent) for the frequency domain 0.001?Hz < f < 0.05?Hz, which means that the power grid is being fed by turbulent-like sources. Our results bring important evidence on the stochastic and turbulent-like behaviour of renewable power production from wind and solar energies, which can cause instability in power grids. Our statistical analysis also provides important information that must be used as a guideline for an optimal design of power grids that operate under intermittent renewable sources of power.     

永磁同步风力发电机随机分岔现象的全局分析

永磁同步风力发电机在运行过程中不可避免地会受到风能的随机干扰,本文建立了在输入机械转矩存在随机干扰情况下永磁同步风力发电机的数学模型,采用胞映射方法分析了随机干扰强度变化时系统全局结构的演化行为,并通过数值模拟对理论分析进行验证.研究结果表明,随着随机干扰强度的增大,系统中会出现随机内部激变和随机边界激变,即由于随机吸引子与其吸引域内的随机鞍发生碰撞而产生的随机分岔现象和由于随机吸引子与其吸引域边界发生碰撞而产生的随机分岔现象.研究结果揭示了随机干扰对永磁同步风力发电机运行性能影响的机理,为永磁同步风力发电系统的运行和设计提供了理论依据.     

应用随机模型方法预测汽车风噪声

采用随机模型方法,对简化汽车头部外形进行风噪声数值模拟.将计算区域分为声源区域和传播区域,在声源区域采用随机模型构造湍流脉动速度场,传播区域通过求解带源项的线化欧拉方程实现声波向外传播得到声场解.同直接模拟方法相比,该方法具有计算量小、计算所需内存少等优点.数值模拟结果与实验数据吻合较好,验证了该方法预测汽车风噪声的可行性,为研究实际汽车外形的风噪声问题打下基础.     

基于优化核极限学习机的风电功率时间序列预测

针对时间序列预测,在单隐层前馈神经网络的基础上,基于进化计算的优化策略,提出了一种优化的核极限学习机(optimized kernel extreme learning machine,O-KELM)方法.与极限学习机(extreme learning machine,ELM)方法相比,核极限学习机(kernel extreme learning machine,KELM)方法无须设定网络隐含层节点的数目,以核函数表示未知的隐含层非线性特征映射,通过正则化最小二乘算法计算网络的输出权值,它能以极快的学习速度获得良好的推广性.在KELM的基础上,分别将遗传算法、模拟退火、微分演化三种进化算法用于模型的结构输入选择、正则化系数以及核参数的优化选取,以进一步提高网络的性能.将O-KELM方法应用于标准Mackey-Glass混沌时间序列预测及某地区的风电功率时间序列预测实例中,在同等条件下,还与优化的极限学习机(optimized extreme learning machine,O-ELM)方法进行比较.实验结果表明,所提出的O-KELM方法在预测精度上优于O-ELM方法,表明了其有效性.     

风电功率时间序列混沌特性分析及预测模型研究

为揭示风电功率序列内在的动态特性, 利用非线性方法对风电时间序列混沌特性进行识别, 为对风电功率进行预测提供了基础.首先对某风电场的风电功率时间序列的日相关性进行了分析;然后在相空间重构的基础上计算了风电序列的最大Lyapunov指数, 验证了风电时间序列的混沌特性;由于采用Volterra滤波器多步预测法对风电功率进行超短期预测误差较大, 利用局域多步预测法以及最大Lyapunov指数法的预测结果并结合加权马尔科夫链和有序算子对Volterra滤波器的预测结果进行校正.最后以某实际风电场的风电功率预测为算例, 仿真结果表明校正预测模型有效的提高了预测精度, 其为利用Volterra滤波器多步法进行风电预测提供了有益的参考.     

Level-crossing statistics of the horizontal wind speed in the planetary surface boundary layer

The probability density of the times for which the horizontal wind remains above or below a given threshold speed is of some interest in the fields of renewable energy generation and pollutant dispersal. However there appear to be no analytic or conceptual models which account for the observed power law form of the distribution of these episode lengths over a range of over three decades, from a few tens of seconds to a day or more. We reanalyze high resolution wind data and demonstrate the fractal character of the point process generated by the wind speed level crossings. We simulate the fluctuating wind speed by a Markov process which approximates the characteristics of the real (non-Markovian) wind and successfully generates a power law distribution of episode lengths. However, fundamental questions concerning the physical basis for this behavior and the connection between the properties of a continuous-time stochastic process and the fractal statistics of the point process generated by its level crossings remain unanswered. (c) 2001 American Institute of Physics.     

The Use of Hydrogen as a Fuel for Engines in the Energy Cycle of Remote Production Facilities

The approach to using hydrogen as fuel, which ensures the smooth operation of autonomous power systems that use renewable energy sources (wind or solar power installations) with the stochastic mode of power generation, has been presented. The fundamental possibility of implementing the nondetonation combustion of hydrogen via the addition of ecologically clean components or a small percentage of methane has been demonstrated by methods of mathematical modeling.

     

A stochastic approach to cable dynamics with moving rivulets

This article constructs a stochastic model for the response of stay cables of cable-stayed bridges to the combined effect of wind and rain. It describes a spring-mounted section model of a stay cable in a steady wind where aerodynamic forces are modified by the dynamics of a mobile liquid rivulet. The motion of the rivulet is described by a simple stochastic process that, together with aerodynamic forces, models the complex fluid-structure interaction. Based on measured data for drag and lift coefficients and a static rivulet location, an analysis of the model suggests a new stochastic excitation mechanism for the rain-wind induced vibrations of stay cables.     

Construction of a Langevin model from time series with a periodical correlation function: Application to wind speed data

A Langevin-type equation for stochastic processes with a periodical correlation function is introduced. A procedure of reconstruction of the equation from time series is proposed and verified on simulated data. The method is applied to geophysical time series–hourly time series of wind speed measured in northern Italy–constructing the macroscopic model of the phenomenon.     

随机中子动力学及其数值模拟研究进展

随机中子动力学是核动力设计和核反应堆安全中的重要课题,本文从随机中子动力学的基础概念和研究方法出发,介绍随机中子动力学研究的历史发展和研究现状.裂变中子与光子的多重性是反应堆零功率中子噪声主要来源,对中子涨落的方程描述及其求解,演化出零功率中子噪声与功率反应堆噪声的随机理论.随机中子动力学的重要应用包括反应性微观测量、功率反应堆噪声测量和分析、核临界漂移分析和核材料识别与检测等.在半个多世纪的研究中,以脉冲堆点火过程的脉冲爆发等待时间分布为代表的随机性,一直缺乏定量分析方法和工具.直到近几年,模拟随机中子动力学过程的广义半马尔科夫过程模拟方法取得了重要进展,很好地揭示了脉冲堆实验中子点火规律.最后讨论随机中子动力学研究中有待解决的研究课题.     

基于流固耦合偏航工况下风力机输出功率的研究

为探索双向流固耦合与未耦合方法对偏航工况风力机输出功率的影响机制,以风力机偏航的Glauert动量定理为基础,基于双向流固耦合方法,采用理论推导与轴向流动过程相结合对输出功率进行分析,揭示了耦合与未耦合数值方法在压力场模拟的差异性、量化了轴向诱导因子对偏航工况输出功率的影响程度、总结了轴向速度分布情况.结果表明:未耦合...     

Granular ratchets

We study the statistics of the horizontal component of atmospheric boundary layer wind speed and introduce a stochastic process which has similar properties. Motivated by the non-stationarity of wind velocity data, we describe statistical methods to verify the picture of natural atmospheric boundary layer turbulence to be composed of successively occurring close to ideal turbulence phases with different parameters. We focus on the fluctuation of wind speed around its mean behaviour and show that there is linear relationship between the standard deviation of the fluctuation and the current mean wind speed. Additionally, we analyse the increment statistics and investigate the time dependence of the parameters describing the increment distribution. Noting that the first order geometric auto regressive process has similar statistical properties, we investigate this similarity and analyse the extent to which this stochastic process is a suitable model for wind speed simulation.     

双稳系统随机共振的反馈控制

将双稳系统的输出反馈到输入端再作用于系统,提出了采用反馈来控制随机共振的方法.以典型双稳系统为对象,并以信噪比和功率谱放大率作为度量随机共振效应的可观察变量,分别研究了采用线性或非线性反馈函数所产生的随机共振现象.理论分析和数值仿真结果表明随机共振是可控制的.该方法特别适用于系统参数固定或难以改变的系统. 关键词： 双稳系统 随机共振 反馈控制 共振效应     

加权最大似然波达方向估计算法及其应用研究

针对阵列信号处理中空间相干性低、频域有色且通道间具有相同功率谱的噪声模型,分别基于确定性信号模型与随机性信号模型,提出一种加权最大似然(Weighted Maximum Likelihood,WML)波达方向估计算法。数据仿真实验表明,该算法提高了由空间非相干且一致有色噪声引起的低信噪比条件下的波达方向估计精度。户外实验验证了该算法在风噪声条件下的有效性。     

Multifractal description of wind power fluctuations using arbitrary order Hilbert spectral analysis

The objectives are to study and model the aggregate wind power fluctuations dynamics in the multifractal framework. We present here the analysis of aggregate power output sampled at 1 Hz during three years. We decompose the data into several Intrinsic Mode Functions (IMFs) using Empirical Mode Decomposition (EMD). We use a new approach, arbitrary order Hilbert spectral analysis, a combination of the EMD approach with Hilbert spectral analysis (or Hilbert–Huang Transform) and the classical structure-function analysis to extract the scaling exponents or multifractal spectrum ζ(q)$\zeta \left(q\right)$: this function provides a full characterization of a process at all intensities and all scales. The application of both methods, i.e. structure-function and arbitrary-order Hilbert spectral analyses, gives similar results indicating that the aggregate power output from a wind farm, possesses intermittent and multifractal properties. In order to check this result, we generate stochastic simulations of a Multifractal Random Walk (MRW) using a log-normal stochastic equation. We show that the simulation results are fully compatible with the experimental results.     

Tomographic reconstruction of atmospheric turbulence with the use of time-dependent stochastic inversion

Acoustic travel-time tomography allows one to reconstruct temperature and wind velocity fields in the atmosphere. In a recently published paper [S. Vecherin et al., J. Acoust. Soc. Am. 119, 2579 (2006)], a time-dependent stochastic inversion (TDSI) was developed for the reconstruction of these fields from travel times of sound propagation between sources and receivers in a tomography array. TDSI accounts for the correlation of temperature and wind velocity fluctuations both in space and time and therefore yields more accurate reconstruction of these fields in comparison with algebraic techniques and regular stochastic inversion. To use TDSI, one needs to estimate spatial-temporal covariance functions of temperature and wind velocity fluctuations. In this paper, these spatial-temporal covariance functions are derived for locally frozen turbulence which is a more general concept than a widely used hypothesis of frozen turbulence. The developed theory is applied to reconstruction of temperature and wind velocity fields in the acoustic tomography experiment carried out by University of Leipzig, Germany. The reconstructed temperature and velocity fields are presented and errors in reconstruction of these fields are studied.     

Ocean acoustic hurricane classification

Theoretical and empirical evidence are combined to show that underwater acoustic sensing techniques may be valuable for measuring the wind speed and determining the destructive power of a hurricane. This is done by first developing a model for the acoustic intensity and mutual intensity in an ocean waveguide due to a hurricane and then determining the relationship between local wind speed and underwater acoustic intensity. From this it is shown that it should be feasible to accurately measure the local wind speed and classify the destructive power of a hurricane if its eye wall passes directly over a single underwater acoustic sensor. The potential advantages and disadvantages of the proposed acoustic method are weighed against those of currently employed techniques.     

Stochastic resonance, reverse-resonance and stochastic multi-resonance in an underdamped quartic double-well potential with noise and delay

The stochastic resonance in an underdamped quartic double-well potential with time delayed feedback is studied numerically. The signal power amplification is employed to characterize the stochastic resonance of the system. Simulation results indicate that: (i) for moderate frequency of the periodic driving, the stochastic resonance is decreased monotonically by increasing the delay time, but at high frequency, the reverse-resonance is induced to transform into a stochastic resonance by time delay; (ii) the damping coefficient has a critical value for which the stochastic resonance is optimum; (iii) a stochastic multi-resonance emerges when the signal power amplification is a function of the driving frequency.