论文标题
一种基于替代模式的方法，用于估计海上风能的一阶和二阶时刻
A Surrogate-model-based Approach for Estimating the First and Second-order Moments of Offshore Wind Power
论文作者
论文摘要
电力曲线在风能行业中广泛用于估算功率输出以进行计划和运营目的。 Existing methods for power curve estimation have three main limitations: (i) they mostly rely on wind speed as the sole input, thus ignoring the secondary, yet possibly significant effects of other environmental factors, (ii) they largely overlook the complex marine environment in which offshore turbines operate, potentially compromising their value in offshore wind energy applications, and (ii) they solely focus on the first-order properties of wind power, with little (or null) information关于平均行为的变化，这对于确保可靠的网格整合，资产健康监测和能源存储等很重要。这项研究研究了几个与风和波浪相关因素对海上风能变异性的影响，其最终目标是准确预测其前两个时刻。我们的方法与高斯流程（GP）回归伴侣伴侣，揭示了管理离岸天气转换的潜在关系。我们首先发现，捕获风速，方向和空气密度的综合影响的多输入功率曲线可以提供相对于单变量方法的两位数改进，该方法依赖于风速作为唯一的解释变量（例如，垃圾箱的标准方法）。发现与波浪相关的变量对于预测平均功率输出并不重要，但有趣的是，在描述围绕其平均值的近海功率波动时似乎非常相关。在纽约/新泽西湾收集的实际数据测试中，我们提议的多输入模型在预测海上风能的前两瞬时表现出了很高的解释能力，证明了它们对海上风能行业的潜在价值。
Power curve is widely used in the wind industry to estimate power output for planning and operational purposes. Existing methods for power curve estimation have three main limitations: (i) they mostly rely on wind speed as the sole input, thus ignoring the secondary, yet possibly significant effects of other environmental factors, (ii) they largely overlook the complex marine environment in which offshore turbines operate, potentially compromising their value in offshore wind energy applications, and (ii) they solely focus on the first-order properties of wind power, with little (or null) information about the variation around the mean behavior, which is important for ensuring reliable grid integration, asset health monitoring, and energy storage, among others. This study investigates the impact of several wind- and wave-related factors on offshore wind power variability, with the ultimate goal of accurately predicting its first two moments. Our approach couples OpenFAST with Gaussian Process (GP) regression to reveal the underlying relationships governing offshore weather-to-power conversion. We first find that a multi-input power curve which captures the combined impact of wind speed, direction, and air density, can provide double-digit improvements relative to univariate methods which rely on wind speed as the sole explanatory variable (e.g. the standard method of bins). Wave-related variables are found not important for predicting the average power output, but interestingly, appear to be extremely relevant in describing the fluctuation of the offshore power around its mean. Tested on real-world data collected at the New York/New Jersey bight, our proposed multi-input models demonstrate a high explanatory power in predicting the first two moments of offshore wind generation, testifying their potential value to the offshore wind industry.
