风电机组功率曲线建模是风电功率预测、状态监测、性能评估的关键环节。文章提出了一种基于logistic函数和分位数回归的风电机组功率曲线建模算法。为解决风电功率的不确定性，文中在logistic函数中嵌入了分位回归损失函数，建立了分位数回归logistic模型（quantile regression logistic function, QRLF），并采用了三种优化算法进行优化；为降低原始数据中异常值的影响，提出了基于QRLF算法的自适应异常筛选方法；在三个风电场的SCADA(supervisory control and data acquisition)数据中进行了实例验证。文中采用五种评价指标对所提方法进行评估。结果表明，相比传统的风电机组功率曲线建模方法，文中所提方法可以同时提供较好的确定性功率曲线和概率性功率曲线结果。
英文摘要:
The wind turbine power curve (WTPC) modeling is of great significance for wind power forecasting, condition monitoring, and performance assessment. This paper proposes a novel WTPC modelling method with logistic functions based on quantile regression (QRLF). Quantile regression logistic functions (QRLF) is embedded in the logistic function, and the QRLF model was established, so that the proposed method can describe the uncertainty of wind power. In order to reduce the effect of outliers in original data, an adaptive outlier filtering method is developed based on QRLF. Supervisory control and data acquisition (SCADA) data collected from wind turbines in three wind farms are used to evaluate the performance of the proposed method. Five evaluation metrics are applied for the comparative analysis. Compared with typical WTPC models, QRLF has better fitting performance in both deterministic and probabilistic power curve modeling.