Clustering methodology for classifying distribution feeders

The screening process for DG interconnection procedures needs to be improved in order to increase the penetration of PV systems on the distribution grid. A significant improvement in the current screening process could be achieved by finding a method to classify the feeders in a utility service territory and determine the sensitivity of particular groups of distribution feeders to the impacts of high PV deployment levels. This paper presents a method for separating a utility's distribution feeders into unique clusters using the k-means clustering algorithm. An approach for determining the feeder variables of interest for use in a clustering algorithm is also described. The Cubic Clustering Criterion is used as a quality metric for determining the optimum number of clusters in a large dataset of over 3000 feeders from western utilities. An approach is illustrated for choosing the feeder variables to be utilized in the clustering process and a method is identified for determining the optimal number of representative clusters.