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。

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.