A new strategy for wind speed forecasting using artificial intelligent methods

A new strategy in wind speed prediction based on fuzzy logic and artificial neural networks was proposed. The new strategy for fuzzy logic not only provides significantly less rule base but also has increased estimated wind speed accuracy when compared to traditional one. Meanwhile, applying the proposed approach to artificial neural network leads to less neuron numbers and less learning time process along with accurate wind speed prediction results. The experimental results demonstrate that the proposed method not only provides less computational time but also a better wind speed prediction performance.     

A new method for improved hub height mean wind speed estimates using short-term hub height data

The estimation of the wind resource at the hub height of a wind turbine is one of the primary goals of site assessment. Because the measurement heights of meteorological towers (met towers) are typically significantly lower than turbine hub heights, a shear model is generally needed to extrapolate the measured wind resource at the lower measurement height to the hub height of the turbine. This paper presents methods for improving the estimate of the hub height wind resource from met tower data through the use of ground-based remote sensing devices. The methods leverage the two major advantages of these devices: their portability and their ability to measure at the wind turbine hub height. Specifically, the methods rely on augmenting the one year of met tower measurements with short-term measurements from a ground-based remote sensing device. The results indicate that the methods presented are capable of producing substantial improvements in the accuracy and uncertainty of shear extrapolation predictions. The results suggest that the typical site assessment process can be reevaluated, and alternative strategies that utilize ground-based remote sensing devices can be incorporated to significantly improve the process.     

Day-ahead wind speed forecasting using f-ARIMA models

With the integration of wind energy into electricity grids, it is becoming increasingly important to obtain accurate wind speed/power forecasts. Accurate wind speed forecasts are necessary to schedule dispatchable generation and tariffs in the day-ahead electricity market. This paper examines the use of fractional-ARIMA or f-ARIMA models to model, and forecast wind speeds on the day-ahead (24 h) and two-day-ahead (48 h) horizons. The models are applied to wind speed records obtained from four potential wind generation sites in North Dakota. The forecasted wind speeds are used in conjunction with the power curve of an operational (NEG MICON, 750 kW) turbine to obtain corresponding forecasts of wind power production. The forecast errors in wind speed/power are analyzed and compared with the persistence model. Results indicate that significant improvements in forecasting accuracy are obtained with the proposed models compared to the persistence method.     

Extraction of synoptic pressure patterns for long-term wind speed estimation in wind farms using evolutionary computing

In this paper we present an evolutionary approach for the problem of discovering pressure patterns under a quality measure related to wind speed and direction. This clustering problem is specially interesting for companies involving in the management of wind farms, since it can be useful for analysis of results of the wind farm in a given period and also for long-term wind speed prediction. The proposed evolutionary algorithm is based on a specific encoding of the problem, which uses a dimensional reduction of the problem. With this special encoding, the required centroids are evolved together with some other parameters of the algorithm. We define a specific crossover operator and two different mutations in order to improve the evolutionary search of the proposed approach. In the experimental part of the paper, we test the performance of our approach in a real problem of pressure pattern extraction in the Iberian Peninsula, using a wind speed and direction series in a wind farm in the center of Spain. We compare the performance of the proposed evolutionary algorithm with that of an existing weather types (WT) purely meteorological approach, and we show that the proposed evolutionary approach is able to obtain better results than the WT approach.     

A novel method based on Weibull distribution for short-term wind speed prediction

Wind speed prediction (WSP) is essential in order to predict and analyze efficiency and performance of wind-based electricity generation systems. More accurate WSP may provide better opportunities to design and build more efficient and robust wind energy systems. Precious short-term prediction is difficult to achieve; therefore several methods have been developed so far. We notice that the statistics of the alterations, which occur between sequential values of the predicted wind speed data, may differ significantly from observed wind statistics. In this study, we investigate these alterations and compare them and, accordingly, propose a novel method based on Weibull and Gaussian probability distribution functions (PDF) for short-term WSP. The proposed method stands on an algorithm, which examines comparison of the statistical features of the observed and generated wind speed in order to achieve more accurate estimation. We have examined this method on the wind speed data set observed and recorded in Ankara in 2013 and in 2014. The obtained results show that the new algorithm provides better wind speed prediction with an enhanced wind speed model.     

Local models-based regression trees for very short-term wind speed prediction

This paper evaluates the performance of different types of Regression Trees (RTs) in a real problem of very short-term wind speed prediction from measuring data in wind farms. RT is a solidly established methodology that, contrary to other soft-computing approaches, has been under-explored in problems of wind speed prediction in wind farms. In this paper we comparatively evaluate eight different types of RTs algorithms, and we show that they are able obtain excellent results in real problems of very short-term wind speed prediction, improving existing classical and soft-computing approaches such as multi-linear regression approaches, different types of neural networks and support vector regression algorithms in this problem. We also show that RTs have a very small computation time, that allows the retraining of the algorithms whenever new wind speed data are collected from the measuring towers.     

Transfer learning for short-term wind speed prediction with deep neural networks

As a type of clean and renewable energy source, wind power is widely used. However, owing to the uncertainty of wind speed, it is essential to build an accurate forecasting model for large-scale wind power penetration. Numerical weather prediction (NWP) and data-driven modeling are two typical paradigms. NWP is usually unavailable or spatially insufficient. Data-driven modeling is an effective candidate. As to some newly-built wind farms, sufficient historical data is not available for training an accurate model, while some older wind farms may have long-term wind speed records. A question arises regarding whether the prediction model trained by data coming from older farms is also effective for a newly-built farm. In this paper, we propose an interesting trial of transferring the information obtained from data-rich farms to a newly-built farm. It is well known that deep learning can extract a high-level representation of raw data. We introduce deep neural networks, trained by data from data-rich farms, to extract wind speed patterns, and then finely tune the mapping with data coming from newly-built farms. In this way, the trained network transfers information from one farm to another. The experimental results show that prediction errors are significantly reduced using the proposed technique.     

汽车制动器惯性台架中冷却风速控制的新方法

汽车制动器惯性台架是目前测试制动器性能的主要试验设备。针对制动器惯性试验台架的风速控制不准确、信号干扰大等问题,结合实际情况,介绍一种采用串口通讯与数字PID相结合的控制方法,并论述具体实现方法。     

Improved week-ahead predictions of wind speed using simple linear models with wavelet decomposition

Simple linear methods are widely used for time series modelling and prediction and in particular for the forecast of wind speed variations. Linear prediction models are popular for their simplicity and computational efficiency, but their prediction accuracy generally deteriorates beyond a few time steps. In this paper we demonstrate that the prediction accuracy of simple auto-regressive (AR) models can be significantly improved, by as much as 60.15% for day-ahead predictions and up to 18.25% for week-ahead predictions, when combined with suitable time series decomposition. The comparison with new reference forecast model (NRFM) also shows similar accuracy gain of week ahead predictions. The combined model is capable of forecasting wind speed up to 7 days ahead with an average root mean square error less than 3 m/s. We also compare the performance of AR and f-ARIMA models in wind speed prediction and observe that the f-ARIMA model is no better than the AR model when used in combination with time series decomposition.     

Multivariable control strategy for variable speed, variable pitch wind turbines

Reliable and powerful control strategies are needed for wind energy conversion systems to achieve maximum performance. A new control strategy for a variable speed, variable pitch wind turbine is proposed in this paper for the above-rated power operating condition. This multivariable control strategy is realized by combining a nonlinear dynamic state feedback torque control strategy with a linear control strategy for blade pitch angle. A comparison with existing strategies, PID and LQG controllers, is performed. The proposed approach results in better power regulation. The new control strategy has been validated using an aeroelastic wind turbine simulator developed by NREL for a high turbulence wind condition.     

A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation

In this paper, a fuzzy model is suggested for the prediction of wind speed and the produced electrical power at a wind park. The model is trained using a genetic algorithm-based learning scheme. The training set includes wind speed and direction data, measured at neighboring sites up to 30 km away from the wind turbine clusters. Extensive simulation results are shown for two application cases, providing wind speed forecasts from 30 min to 2 h ahead. It is demonstrated that the suggested model achieves an adequate understanding of the problem while it exhibits significant improvement compared to the persistent method.     

风电场代表年风速计算方法的分析

在对风电场进行风资源评估时,常采用气象站与测风塔的相关关系,将现场测风数据订正为一套反映风电场长期平均水平的代表性数据进行风资源分析,而对代表年风速订正是否合理是影响风资源评估误差的重要因素。文章以内蒙古地区某风电场风资源分析为例,探讨采用常规方法和改进方法对代表年风速的订正所产生的误差情况,结果表明,通过改进方法进行修正得到的代表年平均风速的变化规律与气象站多年的变化规律一致,此方法弥补了常规方法中的一些不确定因素对代表年修正结果的影响,减小了误差范围。     

Short term wind speed forecasting for wind turbine applications using linear prediction method

In this paper a new method, based on linear prediction, is proposed for wind speed forecasting. The method utilizes the ‘linear prediction’ method in conjunction with ‘filtering’ of the wind speed waveform. The filtering eliminates the undesired parts of the frequency spectrum (i.e. smoothing) of the measured wind speed which is less effective in an application, for example, in a wind energy conversion system. The linear prediction method is intuitively explained with some easy to follow case studies to clarify the complex underlying mathematics. For verification purposes, the proposed method is compared with real wind speed data based on experimental results. The results show the effectiveness of the linear prediction method.     

Comprehensive control strategy for a variable speed cage machine wind generation unit

A comprehensive control strategy, that addresses all three control objectives in a wind generation system, i.e. control of the local bus voltage to avoid voltage rise, capture of the maximum power in the wind and minimization of the power loss in the induction generator is proposed. The control signals are the desired current wave shapes (instantaneous three-phase currents) of the rectifier and the inverter in a double-sided PWM converter system connected between the wind generating unit and the grid. Studies performed on a complete model for a variable speed cage machine wind generation unit, including wind profile, wind turbine, induction generator, PWM converter, local load and transmission line, show that even as the wind speed changes randomly, the proposed control strategy leads the system to the optimum operating conditions.     

基于小波分解和极限学习机的短期风速组合预测研究

提出一种基于小波分解(Wavelet Decomposition,WD)和极限学习机(Extreme Learning Machine,ELM)的新型短期风速组合预测模型。首先,采用小波分解将风速序列分解成不同频段的分量,以降低序列的非平稳性;其次,为避免极限学习机输入维数选取的随意性等问题,先对各分量进行重构相空间,再使用改进的极限学习机对各分量分别建模预测;最后,将各分量预测结果叠加得到最终预测结果。实验结果表明,文章所提的组合预测模型具有较高的预测精度。     

Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction

This paper presents the hybridization of the fifth generation mesoscale model (MM5) with neural networks in order to tackle a problem of short-term wind speed prediction. The mean hourly wind speed forecast at wind turbines in a wind park is an important parameter used to predict the total power production of the park. Our model for short-term wind speed forecast integrates a global numerical weather prediction model and observations at different heights (using atmospheric soundings) as initial and boundary conditions for the MM5 model. Then, the outputs of this model are processed using a neural network to obtain the wind speed forecast in specific points of the wind park. In the experiments carried out, we present some results of wind speed forecasting in a wind park located at the south-east of Spain. The results are encouraging, and show that our hybrid MM5-neural network approach is able to obtain good short-term predictions of wind speed at specific points.     

基于PSO优化LS-SVM的短期风速预测

提出了一种基于粒子群(PSO)算法优化最小二乘支持向量机(LS-SVM)的风电场风速预测方法。以相关性较高的历史风速序列作为输入,建立预测模型,并用粒子群算法优化模型参数。在对未来1 h风速进行预测时,文章所提出的模型比最小二乘支持向量机模型及BP神经网络模型具有较高的预测精度和运算速度。算例结果表明,经粒子群优化的最小二乘支持向量机算法是进行短期风速预测的有效方法。     

基于优化CFD模型的风电场风速数值模拟

为了提高风资源普查的精度,更好地针对我国地形及风况,文章优化了现有风资源计算流体力学模型,并编写了相应计算模块。优化模型包括:(1)贴合复杂山地地形的网格化分器,可以对任意地形进行网格划分;(2)通过分析测风数据自动计算湍流模型系数;(3)增加温度运输方程,将大气边界层热稳定度耦合到动量方程和湍流模型中;(4)与实际大气边界层热稳定度分层效应一致的入口条件及壁面函数。为了验证优化后的风资源计算方法的精度,文章对一待开发风电场进行了风资源计算。计算结果显示,使用优化的模块可以更精确地计算风速,与优化前相比,可以将误差至少降低10%。