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。 在过去的15年里，这一全球性行业的装机容量经历了显著的持续增长，尽管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已经造成了涡轮机成本的下降趋势，然而，对分布式发电和次优站点（具有较低风速、较高湍流和/或切变的站点）的兴趣增加，需要准确且精确地预测轮毂高度处的风速和涡轮机能量输出，这是需要对风切变进行真实估计的因素。 风切变的估计值可以根据风速计数据和垂直外推法，使用幂律或对数律模型产生，然后用于预测拟议轮毂高度的风速。然而，人们普遍认识到，这种方法具有高度的不确定性，影响到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%的时间内达到预测年能源产量的概率）来评估与潜在项目相关的风险，这是一个影响以下能力的因素：（a）获得资金，（B）建立债务比率和（c）杠杆利率。目前的研究表明，为了提供这些概率，必须准确地描述涡轮机位置处的风切变，但是，由于使用气象塔，这一领域的进展受到限制。遥感风廓线仪的引入，在这种情况下，声波检测和测距（SODAR）可以提供一系列高度的风速测量，包括远远超过当前气象塔的高度，从而可以计算整个转子盘上的风切变。这项技术的潜力尚未得到充分研究或用于风切变模型。本研究提出了一个风切变模式的发展SODAR数据的基础上，可观察的网站特性和先进的多变量建模，利用人工神经网络（ANN）。其基本原理是，风切变作为一个复杂的功能，其中许多是非线性的，与垂直分布，需要进行表征和建模。该模型的开发和验证是该项目的预期实际成果之一，有助于推动风力资源评估，从而降低风能项目的不确定性和财务风险。 预计这将导致更便宜的风力发电场，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.********