Generating wind time series as a hybrid of measured and simulated data

Certain applications, such as analysing the effect of a wind farm on grid frequency regulation, require several years of wind power data measured at intervals of a few seconds. We have developed a method to generate days to years of non‐stationary wind speed time series sampled at high rates by combining measured and simulated data. Measured wind speed data, typically 10–15 min averages, capture the non‐stationary characteristics of wind speed variation: diurnal variations, the passing of weather fronts, and seasonal variations. Simulated wind speed data, generated from spectral models, add realistic turbulence between the empirical data. The wind speed time series generated with this method agree very well with measured time series, both qualitatively and quantitatively. The power output of a wind turbine simulated with wind data generated by this method demonstrates energy production, ramp rates and reserve requirements that closely match the power output of a turbine simulated turbine with measured wind data. Copyright © 2011 John Wiley & Sons, Ltd.     

Wind speed time series characterization by Hilbert transform

Predictions of wind energy potential in a given region are based on on‐location observations. The time series of these observations would later be analysed and modelled either by a probability density function (pdf) such as a Weibull curve to represent the data or recently by soft computing techniques, such as neural networks (NNs). In this paper, discrete Hilbert transform has been applied to characterize the wind sample data measured on ?zmir Institute of Technology campus area which is located in Urla, ?zmir, Turkey, in March 2001 and 2002. By applying discrete Hilbert transform filter, the instantaneous amplitude, phase and frequency are found, and characterization of wind speed is accomplished. Authors have also tried to estimate the hourly wind data using daily sequence by Hilbert transform technique. Results are varying. Copyright © 2005 John Wiley & Sons, Ltd.     

Improved synthetic wind speed generation using modified Mycielski approach

In this paper, novel approaches for wind speed data generation using Mycielski algorithm are developed and presented. To show the accuracy of developed approaches, we used three‐year collected wind speed data belonging to deliberately selected two different regions of Turkey (Izmir and Kayseri) to generate artificial wind speed data. The data belonging to the first two years are used for training, whereas the remaining one‐year data are used for testing and accuracy comparison purposes. The concept of distinct synthetic data production with correlation‐wise and distribution‐wise similar statistical properties constitutes the main idea of the proposed methods for a successful artificial wind speed generation. Generated data are compared with test data for both regions in the sense of basic statistics, Weibull distribution parameters, transition probabilities, spectral densities, and autocorrelation functions; and are also compared with the data generated by the classical first‐order Markov chains method. Results indicate that the accuracy and realistic behavior of the proposed method is superior to the classical method in the literature. Comparisons and results are discussed in detail. Copyright © 2011 John Wiley & Sons, Ltd.     

风道式风力发电机迎风及调速方法

为降低风道式风力发电机的成本,提出了一种风力发电机迎风及调速方法。在风道装置始终正对风向的条件下,由于偏心机构和凸轮机构的共同作用,在风速低时,风轮正对着风向,在风速超限时,风轮相应侧偏,以限制风轮转速。基于空气动力学基本理论,推导出其结构参数的表达式,并叙述了各结构参数的选取原则。该方法可有效地起到大风限速的作用,提高风力发电机工作的稳定性。该调速机构结构简单、成本低,安装维护方便。     

A novel approach for the forecasting of mean hourly wind speed time series

This paper presents a novel method for the forecasting of mean hourly wind speed data using time series analysis. The initial point for this approach is mainly the fact that none of the forecasting approaches for hourly data, that can be found in the literature, based on time series analysis or meteorological models, gives significantly lower prediction error than the elementary persistent approach. This was combined with the characteristics of the wind speed data, which are determined by the power spectrum values, distinguished by the spectral gap in intervals between 20 minutes and 2 hours. The finally proposed methodology is based on the multi-step forecasting of 10 minutes averaged data and the subsequent averaging to generate mean hourly predictions. When applied to two independent data sets, this approach outperformed by a factor of four, the conventional one which utilizes past mean hourly wind speed values as inputs to the forecasting models.     

Prediction of wind speed time series using modified Taylor Kriging method

Wind speed forecasting is critical for the operations of wind turbine and penetration of wind energy into electricity systems. In this paper, a novel time series forecasting method is proposed for this purpose. This method originates from TK (Taylor Kriging) model, but is properly modified for the forecasting of wind speed time series. To investigate the performance of this new method, the wind speed data from an observation site in North Dakota, USA, are adopted. One-year hourly wind speed data are divided into 10 samples, and forecast is made for each sample. In the case study, both the modified TK method and (ARIMA) autoregressive integrated moving average method are employed and their performances are compared. It is found that on average, the proposed method outperforms the ARIMA method by 18.60% and 15.23% in terms of (MAE) mean absolute error and (RMSE) root mean square error. Meanwhile, further theoretical analysis is provided to discuss why the modified TK method is potentially more accurate than the ARIMA method for wind speed time series prediction.     

测风数据间隔对风速预测精度影响的研究

风速预测对风电场控制和电网调度具有十分重要的意义。文章以不同时间间隔的测风数据为基础,采用时间序列法和人工神经网络法对风速进行预测,通过比较风速预测绝对平均误差,说明时间间隔较短时,采用BP神经网络预测精度较高;当时间间隔增大时,采用时间序列法预测精度较高;时间间隔过大,即风速数据太少时,两种预测方法误差都较大,须谨慎使用。该研究结果对风电机组控制系统的设计以及电网调度计划的制定具有参考价值。     

A comparison of various forecasting techniques applied to mean hourly wind speed time series

This paper presents a comparison of various forecasting approaches, using time series analysis, on mean hourly wind speed data. In addition to the traditional linear (ARMA) models and the commonly used feed forward and recurrent neural networks, other approaches are also examined including the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Neural Logic Networks. The developed models are evaluated for their ability to produce accurate and fast forecasts.     

双馈风力发电机无速度传感器矢量控制

针对风力发电系统中的双馈电机提出一种转子感应电势定向矢量控制方法。通过调节双馈电机转子侧的瞬时有功电流和无功电流,实现对电机力矩和转子侧励磁电流的调节,进而实现双馈电机无功功率调节。在控制过程中只需检测交流侧电流电压,不需要位置传感器,所以可以应用无速度传感器。最终通过仿真试验证明该方法的正确性和实用性。     

Stochastic generation of hourly mean wind speed data

Use of wind speed data is of great importance in civil engineering, especially in structural and coastal engineering applications. Synthetic data generation techniques are used in practice for cases where long wind speed data are required. In this study, a new wind speed data generation scheme based upon wavelet transformation is introduced and compared to the existing wind speed generation methods namely normal and Weibull distributed independent random numbers, the first- and second-order autoregressive models, and the first-order Markov chain. Results propose the wavelet-based approach as a wind speed data generation scheme to alternate the existing methods.     

Time series modelling of power output for large‐scale wind fleets

Simulations of power systems with high wind penetration need to represent the stochastic output of the wind farms. Many studies use historic wind data directly in the simulation. However, even if historic data are used to drive the realized wind output in scheduling simulations, a model of the wind's statistical properties may be needed to inform the commitment decisions for the dispatchable units. There are very few published studies that fit models to the power output of nation‐sized wind fleets rather than the output at a single location. We fitted a time series model to hourly, time‐averaged, aggregated wind power data from New Zealand, Denmark and Germany, based on univariate, second‐order autoregressive drivers. Our model is designed to reproduce the asymptotic distribution of power output, the diurnal variation and the volatility of power output over timescales up to several hours. For the cases examined here, it was also found to provide a generally good representation of the overall distribution of power output changes and the variation of volatility with power output level, as well as an acceptable representation of the distribution of calm periods. Copyright © 2011 John Wiley & Sons, Ltd.     

Short‐term forecasting of surface layer wind speed using a continuous random cascade model

This paper describes a statistical method for short‐term forecasting (1–12 h ahead) of surface layer wind speed using only recent observations, relying on the notion of continuous cascades. Inspired by recent empirical findings that suggest the existence of some cascading process in the mesoscale range, we consider that wind speed can be described by a seasonal component and a fluctuating part represented by a ‘multifractal noise’ associated with a random cascade. Performances of our model are tested on hourly wind speed series gathered at various locations in Corsica (France) and the Netherlands. The obtained results show that a better modeling of the noise term based on cascade process enhances the forecast; furthermore, there is a systematic improvement in the prediction as compared with reference models. Copyright © 2011 John Wiley & Sons, Ltd.     

风力发电系统中组合风速的建模及仿真

在实验室进行风力发电系统模拟,风速模拟是其中重要的一个环节,正确的风速模型不仅可以反应风速实际变化情况,而且能给风力发电系统的模拟研究提供准确的参数。文章采用4分量组合风速模型,用Matlab/Simulink对组合风速进行建模仿真。仿真结果表明,该数学模型能够较精确地反映风速的实际突变性、渐变性及随机性等特点,适用于风力发电系统的模拟研究工作。     

Regime‐switching models and multiple causal factors in forecasting wind speed

This study evaluates two types of models for wind speed forecasting. The first is models with multiple causal factors, such as offsite readings of wind speed and meteorological variables. These can be estimated using either regressions or neural networks. The second is state transition and the closely related class of regime‐switching transition models. These are attractive in that they can be used to predict outlying fluctuations or large ramp events. The regime‐switching model uses a persistence forecast during periods of high wind speed, and regressions for low and intermediate speeds. These techniques are tested on three databases. Two main criteria are used to evaluate the outcomes, the number of high and low states than can be predicted correctly and the mean absolute percent error of the forecast. Neural nets are found to predict the state transitions somewhat better than logistic regressions, although the regressions do not do badly. Three methods all achieve about the same degree of forecast accuracy: multivariate regressions, state transition and regime‐switching models. If the states could be predicted perfectly, the regime‐switching model would improve forecast accuracy by an additional 2.5 to 3 percentage points. Analysis of the density functions of wind speed and the forecasting models finds that the regime‐switching method more closely approximates the distribution of the actual data. Copyright © 2009 John Wiley & Sons, Ltd.     

Transient stability of a fixed speed wind farm

A typical fixed speed wind farm connected to a simple grid is modelled. Using this model, a three-phase fault is applied close to the wind farm, and cleared by disconnecting the affected line. The effect of several electric, mechanical and operational parameters on the critical fault-clearing time of this base case is evaluated and discussed. The studied parameters are the short-circuit power at the connection bus, the reactive power compensation, the distance to the fault, the rotor inertia, the hub-generator resonant frequency, the wind speed and the power output. For each parameter, the relationship between its value and the critical fault-clearing time is shown graphically. The results help to understand the transient stability phenomena in fixed speed wind farms, and could help to design fixed speed wind farms attending to transient stability requirements.     

适合于风力发电系统的电压跌落发生器

为满足在故障情况下的风力发电机控制策略的研究需要,给出了一种电压跌落发生器的拓扑结构,PC机与单片机通过串行通信来实现对电压跌落模式和跌落时间的控制.试验表明,该设备可以实现国外昂贵的电压跌落发生器的基本功能.     

风特性对风力发电机组输出功率的影响

为了准确研究不同类型的风速对并网风电机组输出功率的影响,建立了并网风力发电机模型和不同类型风速的模型。在不同类型风速的扰动下,对异步风电机输出功率的波动进行了仿真分析;针对引起功率振荡最严重的阵风,进行了不同频率的阵风扰动下风电机组功率振荡的比较,对阵风扰动下风电机组间的相互影响进行了仿真分析。结果表明,在相同幅值的扰动下,阵风引起的风电机组功率振荡最严重,机组功率振荡情况与阵风扰动的频率有关,单台机组在阵风扰动下产生的振荡对其他机组也会产生一定的影响。     

Transient and dynamic control of a variable speed wind turbine with synchronous generator

In this article, a controller for dynamic and transient control of a variable speed wind turbine with a full‐scale converter‐connected high‐speed synchronous generator is presented. First, the phenomenon of drive train oscillations in wind turbines with full‐scale converter‐connected generators is discussed. Based on this discussion, a controller is presented that dampens these oscillations without impacting on the power that the wind turbine injects into the grid. Since wind turbines are increasingly demanded to take over power system stabilizing and control tasks, the presented wind turbine design is further enhanced to support the grid in transient grid events. A controller is designed that allows the wind turbine to ride through transient grid faults. Since such faults often cause power system oscillations, another controller is added that enables the turbine to participate in the damping of such oscillations. It is concluded that the controllers presented keep the wind turbine stable under any operating conditions, and that they are capable of adding substantial damping to the power system. Copyright © 2007 John Wiley & Sons, Ltd.     

Modelling and control of variable speed wind turbines for power system studies

Modern wind turbines are predominantly variable speed wind turbines with power electronic interface. Emphasis in this paper is therefore on the modelling and control issues of these wind turbine concepts and especially on their impact on the power system. The models and control are developed and implemented in the power system simulation tool DIgSILENT. Important issues like the fault ride‐through and grid support capabilities of these wind turbine concepts are addressed. The paper reveals that advanced control of variable speed wind turbines can improve power system stability. Finally, it will be shown in the paper that wind parks consisting of variable speed wind turbines can help nearby connected fixed speed wind turbines to ride‐through grid faults. Copyright © 2009 John Wiley & Sons, Ltd.     

Correlated synthetic time series generation for energy system simulations using Fourier and ARMA signal processing

As the contribution of renewable energy grows in electricity markets, the complexity of the energy mix required to meet demand grows, likewise the need for robust simulation techniques. While decades of wind, solar, and demand profiles can sometimes be obtained, this is too few samples to provide a statistically meaningful analysis of a system with baseload, peaker, and renewable generation. To demonstrate the viability of an energy mix, many thousands of samples are needed. Synthetic time series generation presents itself as a suitable methodology to meet this need. For a synthetic time series to be statistically viable, several conditions must be met. The series generator must produce independent, identically distributed samples, each having the same fundamental properties as the original signal without duplicating it exactly. One approach for such a generator is training a surrogate model using Fourier series decomposition for seasonal patterns and autoregressive moving average (ARMA) models to describe time-correlated statistical noise about the seasonal patterns. When combined, the Fourier plus ARMA (FARMA) model has been shown to provide an infinite set of independent, identically distributed sample time series with the same statistical properties as the original data [1]. When considering an energy mix with renewable electricity production, several time series of energy, grid, and weather measurements are needed for each synthetic year modeled to statistically comprehend the efficiency of any given energy mix. This includes measurements of solar exposure, air temperature, wind velocity, and electricity demand. These cannot be considered independent series in a given synthetic year; for example, in summer months demand may be higher when solar exposure and air temperature are high and wind velocity is low. To capture and reproduce the correlations that might exist in the measured histories, the ARMA can further be extended as a Vector ARMA (VARMA). In the VARMA algorithm, covariance in statistical noise is captured both within a history as part of the autoregressive moving average and with respect to the other variables in the time series. In this work, the implementation of the Fourier VARMA in the RAVEN uncertainty quantification and risk analysis software framework [2] is presented, along with examples of correlated synthetic history generation. Finally, methods to extend synthetic signals to multiyear samples are presented and discussed.