基于改进的K-means风电机异常数据检测

Abnormal data detection of wind turbine based on improved K-means

风电机异常数据检测对维护风电设备的稳定运行有着重要意义,为解决K-means算法随机指定初始点聚类和风电机数据异常问题,提出一种改进K-means算法的风电机数据异常检测方法。改进之后的方法,首先选择数据样本中位数作为第一个初始聚类中心,在选取下一个聚类中心时,距离当前n个聚类中心越远的点会有更高的概率被选为第n+1个聚类中心,进而达到聚类中心互相距离较远的目的,以此对风电机运行数据进行聚类,检测出离群点及异常点,保障风电设备稳定运行。

Abnormal data detection of wind turbine is of great significance for maintaining the stable operation of wind power equipment.In order to solve the problem of clustering randomly assigned initial points of K-means algorithm and abnormal wind turbine data,this paper proposes an improved K-means algorithm to detect abnormal wind turbine data.The improved method first selects the median of the data sample as the first initial clustering center.When selecting the next cluster center,the point farther away from the current n cluster centers will have a higher probability of being selected as the n+1 cluster center.And then achieve the goal that the cluster centers are far away from each other.Based on this,the operation data of wind turbines are clustered to detect outliers and outliers,so as to ensure the stable operation of wind power equipment.