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

风能是可再生能源结构的主要贡献者之一。在过去的15年里，这个全球行业的装机容量经历了显著的持续增长，尽管在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造成了涡轮机成本下降的趋势，然而，对分布式发电和次优站点（风速较低，湍流和/或切变较大的站点）的兴趣日益增加，需要准确和精确地预测风速和轮毂高度的涡轮机能量输出，这需要对风切变进行真实的估计。风切变的估计可以使用基于风速计数据和垂直外推的功率或对数律模型来产生，然后用于预测所建议的轮毂高度的风速。然而，人们普遍认识到，这些方法具有高度的不确定性，这影响了整个项目的不确定性。金融机构使用P90， P75或P50（分别达到预测年能源产出的90%，75%和50%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)建立负债率和(c)杠杆利率能力的因素。***目前的研究表明，为了提供这些概率，必须准确地表征涡轮机位置的风切变，然而，由于使用气象塔，这一领域的进步受到限制。在这种情况下，引入遥感风廓线仪，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，允许在整个转子盘上进行风切变计算。这项技术的潜力尚未被充分研究或用于风切变模型。本研究提出了基于SODAR数据、可观测站点特征和先进的多变量人工神经网络（ANN）建模的风切变模型。其基本原理是，风切变是许多复杂因素的函数，其中许多是非线性的，需要对其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，从而减少风能项目的不确定性和财务风险。预计这将降低风电场的成本，实现更大的场地多样化，增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.********