风电机组在运行过程中受计划外停机、限负荷运行、极端天气等因素影响，功率曲线存在大量的横向、离散分布的异常数据，而现有数据清洗方法受算法自身条件限制，在单独应用过程中无法准确识别局部分散堆积型数据，或受限于样本数据特征，算法不能直接应用。为此，通过将基于密度的聚类算法与拉依达准则优势组合，提出一种适用于风电机组功率曲线异常数据清洗的方法。实例验证表明，该方法可高质量识别风机处于异常工况下的离散数据，泛化能力较强，在风电机组功率曲线数据清洗方面有较好应用。可作为风电机组数据分析与数据挖掘的基础，为后续高效利用风能，开展风电机组提质增效、策略优化等一系列工作提供有效数据保障。

Due to the influence of unplanned shutdown, load limiting operation, extreme weather and other factors in the operation of wind turbines, there are a large number of horizontal and discrete abnormal data in the power curve. However, the existing data cleaning methods are limited by the conditions of the algorithm itself, and the local scattered accumulation data cannot be accurately identified in a single application process, or the characteristics of sample data are limited, so the algorithm cannot be directly applied. Therefore, by combining the advantages of DBSCAN(density-based spatial clustering of applications with noise) clustering algorithm and Laida criterion, a suitable method for cleaning abnormal data of wind turbine power curve is proposed. The example shows that the method can identify the discrete data of the wind turbine under abnormal working conditions with high quality, and has strong generalization ability, and has good application in the power curve data cleaning of wind turbine. It can be used as the basis for data analysis and data mining of wind turbines, and provide effective data guarantee for subsequent efficient use of wind energy, quality and efficiency improvement of wind turbines, strategy optimization and other work.