电气技术2025，Vol.26Issue(5)：39-47,57,10.
基于改进K均值聚类和加权动态时间规整的分布式光伏异常数据辨识方法
A distributed photovoltaic abnormal data identification method based on improved K-means clustering algorithm and weighted dynamic time warping
摘要
Abstract
The failure of photovoltaic power generation equipment and various factors such as external environment lead to a large number of abnormal data during the power generation process.In order to improve the accuracy and efficiency of data processing,this paper proposes a distributed photovoltaic abnormal data identification method based on improved K-means algorithm and weighted dynamic time warping(WDTW).Firstly,the distributed photovoltaic power generation data is analyzed,and the abnormal data is preliminary eliminated by means of the simultaneous power mean method,and a photovoltaic data similarity day partitioning method based on improved K-means algorithm is proposed by normalizing the light intensity data.Secondly,considering the variability and complexity of photovoltaic data in the time dimension,a data similarity analysis method based on WDTW is proposed by introducing the best time period and threshold factor for identifying abnormal data.The similarity is used to calculate the contour coefficient,and the residual abnormal photovoltaic power generation data is culled twice.The simulation results show that the proposed method has significant advantages in identifying distributed photovoltaic abnormal data.Compared with the existing quartile method,3-sigma method,and feature clustering method,the identification accuracy has been improved by 6.92%,9.00%,and 8.12%respectively,while the computational complexity is reduced.关键词
改进K均值聚类算法/加权动态时间规整(WDTW)/分布式光伏/异常数据辨识Key words
improved K-means clustering algorithm/weighted dynamic time warping(WDTW)/distributed photovoltaic/identification of abnormal data引用本文复制引用
杨旺霞,李本瑜,翟苏巍,石恒初,李银银..基于改进K均值聚类和加权动态时间规整的分布式光伏异常数据辨识方法[J].电气技术,2025,26(5):39-47,57,10.基金项目
云南电网公司科技项目(YNKJXM20230368) （YNKJXM20230368）
