Machine learning for inferring phase connectivity in distribution networks
The connectivity model of a power distribution network can easily become outdated due to system changes occurring in the field. Maintaining and sustaining an accurate connectivity model is a key challenge for distribution utilities worldwide. This work focuses on inferring customer to phase connectivity using machine learning techniques. Using voltage time series measurements collected from customer smart meters as the feature set for training classifiers, we study the performance of supervised, semi-supervised and unsupervised techniques. We report analysis and field validation results based on real smart meter measurements collected from three feeder circuits of a large distribution network in North America.