Multivariable Estimation of Wind Shear Profiles and Optimal Wind Turbine Energy Prediction

风能是可再生能源组合的主要贡献者之一。这个全球行业在过去15年里经历了装机容量的显著、持续增长，尽管它在2013年略有下降，但一些专家预测，到2020年，装机容量将翻一番。涡轮机制造商之间的竞争导致了涡轮机成本的下降趋势，然而，对分布式发电和次优地点(风速较低、湍流和/或切变较大)的兴趣日益浓厚，要求准确和准确地预测轮毂高度的风速和涡轮机的能量输出，这一因素需要准确估计风切变。风切变的估计可以使用基于风速计数据的幂或对数定律模型和垂直外推，然后用来预测拟议枢纽高度的风速。然而，人们普遍认识到，这种方法具有很高的不确定性，这影响了整个项目的不确定性。金融机构使用P90、P75或P50(分别在90%、75%和50%的情况下达到预测的年能源产出的概率)来评估与潜在项目相关的风险，这是一个影响(A)获得资金、(B)确定债务比率和(C)杠杆利率的能力的因素。 目前的研究表明，必须准确地表征涡轮机位置的风切变才能提供这些概率，然而，使用气象塔限制了这一领域的进展。遥感风廓线的引入，在这种情况下是声波探测和测距(SODAR)，提供了一系列高度的风速测量，包括远远超过当前气象塔的高度，从而允许计算整个旋翼盘的风切变。这项技术的潜力尚未被充分研究或用于风切变模型。这项研究提出了一种基于声达数据、可观测场地特征和利用人工神经网络(ANN)的高级多变量建模的风切变模型。其基本原理是，风切变作为一系列复杂因素的函数而变化，其中许多是非线性的，需要对垂直分布进行表征和建模。该模型的开发和验证是该项目预期的实际成果之一，有助于推进风能资源评估，这反过来将减少不确定性，降低风能项目的财务风险。预计这将导致风电场成本更低，实现更大的场地多样化，并加强分布式可再生能源，不仅创造环境效益，还创造社会和经济效益。

Wind energy is one of the major contributors to the renewable energy mix. This global industry has experienced significant, sustained growth in installed capacity over the past 15 years and although it experienced a slight decline in 2013, some experts predict that installed capacity will double by 2020. Competition between turbine manufacturers has created a downward trend in turbine costs, however increasing interest in distributed generation and sub-optimal sites (those with lower wind speeds, higher turbulence and/or shear), demand an accurate and precise prediction of wind speed and turbine energy output at hub height, a factor that requires a true estimation of wind shear. An estimate of wind shear can be produced using power or logarithmic law models based on anemometer data and vertical extrapolation then used to predict wind speed at the proposed hub height. It is generally recognized however that such methods suffer from high levels of uncertainty which impacts the overall project uncertainty. Financial institutions use P90, P75 or P50 (the probability of reaching predicted annual energy output 90%, 75% and 50% of the time respectively) to evaluate the risk associated with a potential project, a factor that impacts the ability to (a) obtain funding, (b) establish debt ratio and (c) leverage interest rates. Current research has shown that wind shear at the turbine location must be accurately characterized in order to provide these probabilities however advancement in this area is limited with the use of meteorological towers. The introduction of remote sensing wind profilers, in this case, Sonic Detection and Ranging (SODAR) provide wind speed measurements at a range of heights, including heights that far exceed current meteorological towers, permitting wind shear calculations across the entire rotor disk. The potential of this technology has not yet been fully investigated or utilized for wind shear models. This research proposes the development of a wind shear model based upon SODAR data, observable site characteristics and advanced multi-variable modeling utilizing an artificial neural network (ANN). The rationale is that wind shear varies as a function of a number of complexities, many of which are non-linear, with a vertical distribution that needs to be characterized and modeled. Development and validation of this model is one of the expected practical outcomes of this project, contributing to the advancement of wind resource assessment which in turn will reduce uncertainty, the financial risk of wind energy projects.  This is expected to result in less expensive wind farms, enabling greater site diversification and enhancing distributed renewable energy, creating not only environmental benefits but social and economic benefits too.