A general method to estimate wind farm power using artificial neural networks

An artificial neural network (ANN) is trained and validated using a large dataset of observations of wind speed, direction, and power generated at an offshore wind farm (Lillgrund in Sweden). In its traditional form, the ANN is used to generate a new two‐dimensional power curve, which predicts with high accuracy (bias ～?0.5% and absolute error ～2%) the power of the entire Lillgrund wind farm based on wind speed and direction. By contrast, manufacturers only provide one‐dimensional power curves (i.e., power as a function of wind speed) for a single turbine. The second innovative application of the ANN is the use of a geometric model (GM) to calculate two simple geometric properties to replace wind direction in the ANN. The resulting GM‐ANN has the powerful feature of being applicable to any wind farm, not just Lillgrund. A validation at an onshore wind farm (Nørrekær in Denmark) demonstrates the high accuracy (bias ～?0.7% and absolute error ～6%) and transfer‐learning ability of the GM‐ANN.     

Fuzzy modeling techniques and artificial neural networks to estimate annual energy output of a wind turbine

The purpose of this article is to develop a new method to estimate annual energy output for a given wind turbine in any region which should be easy to use and has satisfactory accuracy. To do this, hourly wind speeds of 25 different stations in Netherlands, output power curve of S47 wind turbine and fuzzy modeling techniques and artificial neural networks were used and a model is developed to estimate annual energy output for S47 wind turbine in different regions. Since this model has three inputs (average wind speed, standard deviation of wind speed, and air density of that region), this model is easy to use. The accuracy of this method is compared with the accuracy of conventional methods and it is shown that this new method performs better. Thereafter, we have shown that by making some small changes to this proposed model, other pitch control wind turbines could be modeled too. As an example, we have modeled E82 wind turbine based on the model developed for S47 and it is shown that this model has still satisfactory accuracy.     

Hybrid neurofuzzy wind power forecast and wind turbine location for embedded generation

Wind energy uptake in South Africa is significantly increasing both at the micro‐ and macro‐level and the possibility of embedded generation cannot be undermined considering the state of electricity supply in the country. This study identifies a wind hotspot site in the Eastern Cape province, performs an in silico deployment of three utility‐scale wind turbines of 60 m hub height each from different manufacturers, develops machine learning models to forecast very short‐term power production of the three wind turbine generators (WTG) and investigates the feasibility of embedded generation for a potential livestock industry in the area. Windographer software was used to characterize and simulate the net output power from these turbines using the wind speed of the potential site. Two hybrid models of adaptive neurofuzzy inference system (ANFIS) comprising genetic algorithm and particle swarm optimization (PSO) each for a turbine were developed to forecast very short‐term power output. The feasibility of embedded generation for typical medium‐scale agricultural industry was investigated using a weighted Weber facility location model. The analytical hierarchical process (AHP) was used for weight determination. From our findings, the WTG‐1 was selected based on its error performance metrics (root mean square error of 0.180, mean absolute SD of 0.091 and coefficient of determination of 0.914 and CT = 702.3 seconds) in the optimal model (PSO‐ANFIS). Criteria were ranked based on their order of significance to the agricultural industry as proximity to water supply, labour availability, power supply and road network. Also, as a proof of concept, the optimal location of the industrial facility relative to other criteria was X = 19.24 m, Y = 47.11 m . This study reveals the significance of resource forecasting and feasibility of embedded generation, thus improving the quality of preliminary resource assessment and facility location among site developers.     

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.     

A study into the accuracy of using meteorological wind data to estimate turbine generation output

Meteorological (met) station data is used as the basis for a number of influential studies into the impacts of the variability of renewable resources. Real turbine output data is not often easy to acquire, whereas meteorological wind data, supplied at a standardised height of 10 m, is widely available. This data can be extrapolated to a standard turbine height using the wind profile power law and used to simulate the hypothetical power output of a turbine. Utilising a number of met sites in such a manner can develop a model of future wind generation output. However, the accuracy of this extrapolation is strongly dependent on the choice of the wind shear exponent α. This paper investigates the accuracy of the simulated generation output compared to reality using a wind farm in North Rhins, Scotland and a nearby met station in West Freugh. The results show that while a single annual average value for α may be selected to accurately represent the long term energy generation from a simulated wind farm, there are significant differences between simulation and reality on an hourly power generation basis, with implications for understanding the impact of variability of renewables on short timescales, particularly system balancing and the way that conventional generation may be asked to respond to a high level of variable renewable generation on the grid in the future.     

A novel small scale efficient wind turbine for power generation

A ‘proof of concept’ study of a novel wind turbine that overcomes some of the deficiencies and combines the advantages of the conventional horizontal and vertical axis wind turbines is presented in this paper. The study conducted using computational fluid dynamics and wind tunnel tests clearly demonstrate that such a proposition is feasible and a low cost, low noise, safe and easy to operate but enhanced performance wind turbine for small scale power generation in low wind speed is viable.     

考虑尾流效应的风电场有功功率控制策略研究

随着大型风电场的快速发展,由于尾流效应造成的风电场能量损失成为重要的问题。本文考虑风电场内的尾流效应,提出了优化的有功功率和桨距角曲线以降低独立机组的能量损失,从而达到风电场的总有功功率提升的目的。同时,通过挖掘风电机组有功出力和尾流效应的关系,给出基于有功控制的尾流优化方法,建立了风电场有功出力优化模型。最后,基于某风电场的实际数据建立仿真模型来检验控制策略的有效性,并引入传统单机MPPT方案进行比对,结果证明提出的新型控制策略大大提高了整个风电场的有功功率,并且计算量小,优化方法简单,具有一定的工程应用价值。     

基于风功率数据的风电机组性能预测与健康状态评估

为及时准确地预测风电机组整机性能,文章基于风功率数据提出了一种考虑工况波动的相似性度量算法,结合概率和模糊理论评估机组健康状态。该方法基于聚类思想对风功率数据进行数据预处理和工况划分,针对工况子空间的正常样本与测试样本,基于主成分分析法以第二主成分方向上投影变量的标准差作为样本相似性度量指标,依据概率不确定性融合得到综合健康状态指标,量化机组性能的退化程度。结合机组性能实际退化过程,依据模糊理论确定健康状态隶属度,基于隶属度最大原则和信度准则判断机组健康状态等级。将该方法应用于某具有小样本数据的实例中,可提前两周获知风电机组发生异常,对机组健康状态的过渡过程作出了准确评估,验证了所提方法的可行性。     

Electric power generation based on variable speed wind turbine under load disturbance

This study was interested in the management of an energy production unit. A variable speed wind turbine (VSWT) was used as a principal source and a supercapacitor (SC) module was used as an energy storage system. Both were connected through a direct current bus. This unit was supplying a three-phase load using an inverter and an inductor and capacitor filter. In order to regulate the direct current bus voltage, the SC storage state was controlled by using a buck-boost converter according to load instructions and wind speed fluctuations. Then, a resonant controller was established to avoid any disturbances and to control the alternating line-to-line voltages of the load which may be unbalanced. This study has shown that the stability of the three-phase voltage source depends on the direct current bus power management and also on the line-to-line voltage control. Simulation results are presented to validate the efficiency of the control strategies used.     

基于风电机组功率曲线的故障监测方法研究

为了最大限度地减少风机停机时间和提高风机发电量,基于风机功率曲线特性,结合多元统计Hotelling T~2控制图,提出了一种风力发电机性能及故障监测方法。首先,根据SCADA系统历史数据集,应用粒子群算法(PSO)寻优最小二乘支持向量机的模型,构造风电机组参考功率曲线。然后,计算风场各风机功率特性的多元峰度、多元偏度,将其偏离参考曲线的程度作为评估风力发电机性能的指标。最后,监测风机发生故障的时刻,引入用于监测风机的Hotelling T~2多变量质量控制图。将该方法用于某风场1.5 MW级风力发电机,实例表明,该算法可以有效地对风电机组状态及故障进行监测,为风电机组的故障识别及分析提供了一种新的方法。     

Intelligent approach to maximum power point tracking control strategy for variable-speed wind turbine generation system

To achieve maximum power point tracking (MPPT) for wind power generation systems, the rotational speed of wind turbines should be adjusted in real time according to wind speed. In this paper, a Wilcoxon radial basis function network (WRBFN) with hill-climb searching (HCS) MPPT strategy is proposed for a permanent magnet synchronous generator (PMSG) with a variable-speed wind turbine. A high-performance online training WRBFN using a back-propagation learning algorithm with modified particle swarm optimization (MPSO) regulating controller is designed for a PMSG. The MPSO is adopted in this study to adapt to the learning rates in the back-propagation process of the WRBFN to improve the learning capability. The MPPT strategy locates the system operation points along the maximum power curves based on the dc-link voltage of the inverter, thus avoiding the generator speed detection.     

Identifying and characterizing the impact of turbine icing on wind farm power generation

Wind park power production in cold climate regions is significantly impacted by ice growth on turbine blades. This can lead to significant errors in power forecasts and in the estimation of expected power production during turbine siting. A modeling system is presented that uses a statistical modeling approach to estimate the power loss due to icing, using inputs from both a physical icing and a numerical weather prediction model. The physical icing model is that of Davis et al., 1 with updates to the simulation of ice ablation. A new approach for identifying periods of turbine blade icing from power observations was developed and used to calculate the observed power loss caused by icing. The observed icing power loss for 2years at six wind parks was used to validate the modeling system performance. Production estimates using the final production loss model reduce the root mean squared error when compared with the empirical wind park power curve (without icing influence) at five of the six wind parks while reducing the mean bias at all six wind parks. In addition to performing well when fit to each wind park, the production loss model was shown to improve the estimate of power when fit using all six wind parks, suggesting it may also be useful for wind parks where production data are not available. Copyright © 2015 John Wiley & Sons, Ltd.     

分散式风力发电机组的电压控制

风电的分散式开发不同于大规模开发和分布式开发,由于分散风电靠近负荷中心,直接接入配电网,且不加装无功补偿调节装置SVC,配网中较大的电压波动给分散风电的并网运行带来影响。文章讨论了配网对分散风电的电压控制特点和要求,结合风电机组无功控制能力,并推导出满足配网电压调节要求的风电机组无功控制范围和对机组设备的要求。     

Short-term wind power forecasting in Portugal by neural networks and wavelet transform

This paper proposes artificial neural networks in combination with wavelet transform for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. Results from a real-world case study are presented. A comparison is carried out, taking into account the results obtained with other approaches. Finally, conclusions are duly drawn.     

Very short-term wind power forecasting with neural networks and adaptive Bayesian learning

This article presents an adaptive very short-term wind power prediction scheme that uses an artificial neural network as predictor along with adaptive Bayesian learning and Gaussian process approximation. A set of recent wind speed measurements samples composes the predictor’s inputs. The predictor’s parameters are adaptively optimized so that, at a given time t, its outputs approximate the future values of the generated electrical power. An evaluation of this prediction scheme was conducted for two tests cases; the predictor was set to simultaneously estimate the values of the wind power for the following prediction horizons: 5 min, 10 min and 15 min for test case n°1 and for the test case n°2, the prediction horizons were 10 min, 20 min and 30 min . The neural predictor performs better than the persistent model for both test cases. Moreover, the Bayesian framework also permits to predict, for a specified level of probability, the interval within which the generated power should be observed.     

风力发电前景广阔

被称为“蓝天白煤”的风力资源,是一种取之不尽,又不会产生任何污染的可再生能源。人类早在远古时代便开始利用风力,但直到１９世纪末丹麦才建成全球第一个风力发电装置。由于风力发电与火电、核电、水电等其它发电方式相比有诸多优点,所以,本世纪８０年代以来,世界风电装机容量迅猛增长。１９８１年为１５兆瓦,１９９２年已达２６５２兆瓦,１３%的年增长率使风力发电成为世界上增长速度最快的发电方式之一,目前仍保持着快速发展的势头。１９９９年１０月５日,欧洲风能协会在布鲁塞尔发表了一项国际能源研究报告。报告称,风力发电到２０２０…     

A variable speed wind turbine power control

To optimize the power in a wind turbine, the speed of the turbine should be able to vary with the wind speed. A simple control scheme is proposed that will allow an induction motor to run a turbine at its maximum power coefficient. The control uses a standard V/Hz converter and controls the frequency to achieve the desired power at a given turbine speed     

风 风能 风力发电——21世纪新型清洁能源

一风的一般属性１风的形成风是人们非常熟悉的一种自然现象，人人都能感觉到它的存在。春风和煦，给万物带来生机；夏风吹拂，使人心旷神怡；秋风送爽，带来丰收的喜悦；冬风呼啸，迎来漫天飞雪。那么风是怎样形成的呢？众所周知，人类生活的地球表面被大气所包围，来自太阳的辐射不断传送到地球表面，因太阳辐射受热情况不同，地球表面各处的气温不同。在影响气压高低的因素中，气温起着最重要的作用。温度高的地区空气受热上升，气压减小；温度低的地方，空气下降，气压增大，于是产生了气压差。和水往低处流一样，空气也从气压高处向气压…     

风电机组叶片加装涡流发生器提升发电能力试验研究

涡流发生器是风电机组中控制流动分离的一种有效方式。以目标风场的具体空气密度、风况等作为主要输入,对拟加装的涡流发生器的形状和安装位置进行模拟仿真,获得理论最优解,仿真计算发电量可提升1%。涡流发生器在风电场进行现场安装试验,对运行整1年的运行数据进行分析,分别对加装涡流发生器的机组和相邻对照组从实际发电量、实际功率曲线估算应发电量等方面评估发电量增益情况。结果表明:在年平均风速为5.06 m/s时,通过加装涡流发生器,发电量提升约为1%,与前期仿真计算值相符合;此外,由于湍流、偏航误差等的存在,加装涡流发生器后,功率值提升的风速范围较理论值有所拓宽。     

Alternative wind power : A next generation wind power technology?

Present day wind turbines (large blade Props on Pole or POPs) have continued to evolve to ever larger sizes requiring massive blades, gearboxes or large, complex customized generators. This continuous increase in size has subsequently increased the costs and complexity of manufacturing, transporting, constructing and maintaining these systems. In this article, Alfred L. Weisbrich, ENECO Texas LLC, USA discusses an alternative wind power technology design, the Wind Amplified Rotor Platform (WARP^TM) which uses many identical smaller vertically integrated modules as opposed to one large generator. The modular design can be easily and relatively inexpensively volume produced and implemented both in traditional electric utility scale wind farms as well as in marine based offshore wind farms sites.