Abstract：Wind power curve is an important basis for assessing the power generation performance of wind turbines, and is of great significance to wind farm management and power system scheduling. The equipment failures and control factors will cause a large number of outliers in wind speed-power curve during actual operation, which will directly affect the subsequent applications of the wind power curve. Based on the analysis of the characteristics of outliers, this paper divided the outliers into four categories according to their spatial distribution and shape features, including the bottom, middle and upper stacked outliers as well as scattered outliers around the curve. A combined strategy and its process for eliminating outliers were proposed based on the change point grouping and quartile method. Compared with the quartile-change point grouping method and the local outlier factor (LOF) algorithm, the results show that the proposed method and the combined strategy can eliminate four types of outliers with good effect, high efficiency and strong versatility.
沈小军, 付雪姣, 周冲成, 王伟. 风电机组风速-功率异常运行数据特征及清洗方法[J]. 电工技术学报, 2018, 33(14): 3353-3361.
Shen Xiaojun, Fu Xuejiao, Zhou Chongcheng, Wang Wei. Characteristics of Outliers in Wind Speed-Power Operation Data of Wind Turbines and Its Cleaning Method. Transactions of China Electrotechnical Society, 2018, 33(14): 3353-3361.
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