With the development of advanced metering infrastructure(AMI)

the observability of power utility information is promoted

but lots of bad data exist in the vast amounts of measurement data due to communication and other reasons. A load data identification and correction method with an improved fuzzy C-means(FCM)clustering algorithm is proposed. With the rapid mountaineering technique

the proposed method has overcome the shortcomings of the existing standard FCM clustering algorithm

which is unable to determine the number of clusters in advance and the random choice of the initial cluster centers. Based on the load data clustering obtained

the feasible region matrix and the load characteristic curve can be calculated for identification and correction of the new load data. Finally

a case of actual load measuring data is analyzed and compared with similar cases treated by the standard FCM clustering algorithm

the proposed method proved fast and efficient.