针对因风电场机组异常数据而导致风电功率预测精度下降的问题,文章提出一种基于密度噪声应用空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法加上最小绝对残差(least absolute residual,LAR)法的风电场数据清洗方法.首先利用DBSCAN算法识别分散型异常数据,然后基于LAR方法构建堆积型异常数据识别模型,分别实现对风电场分散型异常数据和堆积型异常数据的清洗,最后通过 Pearson 相关系数和反向传播神经网络预测模型验证所提方法的效果.结果表明,基于DBSCAN+LAR的风电场数据清洗方法能有效减小风电功率预测误差.
A Data Cleansing Method for Wind Power Farm Based on DBSCAN+LAR
Aiming at the problem of low wind power prediction accuracy due to abnormal data of wind turbines,a wind power farm data cleansing method based on density-based spatial clustering of applications with noise(DBSCAN)algorithm coupled with least absolute residual(LAR)method is proposed.Firstly,DBSCAN is used to identify dispersed abnormal data,and then LAR is used to construct a model for identifying stacked abnormal data,which realizes the cleansing of dispersed abnormal data and stacked abnormal data of wind power farms.Finally,the effect of the proposed method is verified by Pearson correlation coefficient and back propagation neural network prediction model.The results show that the wind power farm data cleansing method based on DBSCAN+LAR can effectively reduce the wind power prediction error.
wind power farmabnormal dataDBSCANLARdata cleansing
万方数据
维普
