A robust initialization algorithm for k-means clustering in power distribution networks with PMU-based adaptive protection system

The K-Means clustering is one of the most popular and influential algorithms in data categorizing methods. K-Means simple and straightforward formulation made it a widely acceptable method in many fields and applications. This simplicity comes with some prices such user defined number of clusters, uniformly sized clusters and different final clusters as of being sensitive to initial centroids. K-means sensitiveness to initial centroids leads to different clusters per execution with different and relatively long iteration numbers. Different applications have their own initialization and improvement techniques for k-means relying on their particular data traits. Power systems recently have been involving with data mining and clustering due to fast increase in PMU uses for supervisory, control and protection goals in smart grids. Large amount of data streaming by PMU demands quite simple method with minimum computational burden to meet delay tolerance for various working phases and expectations. This article presents an approach significantly improving k-means clustering algorithm by pre-analyzing the data and finding best initial centroids. Extensive experiments have been made to verify the approach robustness in reducing the number of iterations and resulting in unique clusters in all executions.