基于QM-DBSCAN的风力机数据清洗方法

针对风电场风速-功率异常数据难以清洗的问题,提出一种基于QM-DBSCAN算法的风电场数据清洗方法.首先选取最能代表风力机运行状况的风速-功率数据作为研究对象,根据异常数据的分布特征进行分类;然后分别利用四分位法、标准DBSCAN算法及基于QM-DBSCAN方法识别和剔除异常数;最后通过spearman系数进一步验证所提方法的有效性.研究结果表明:QM-DBSCAN方法的剔除效果最好,较四分位法和标准DBSCAN法的spearman系数分别提高0.003 5和0.004 7.

A novel cleaning method for wind turbine data based on QM-DBSCAN

For the issue of wind Speed-Power data hard cleaning in wind farms, a novel method based on QM-DBSCAN is proposed. Firstly, the wind condition which can best represent the operating state of the wind turbine is selected as the research object, and the anomalous data are classified according to the distribution characteristics. Then, the quartile method, standard DBSCAN algorithm and QM-DBSCAN method were used to identify and eliminate the abnormal data. The Spearman correlation coefficient was adopted to verify the effectiveness of the proposed method. The results indicated that the QM-DBSCAN method had the best elimination effect, which was 0.003 5 and 0.004 7 higher than the quartile method and the DBSCAN method, respectively.