A Novel Approach for Phase Identification in Smart Grids Using Graph Theory and Principal Component Analysis
This addresses a specific issue for distribution companies in managing consumer phase connectivity, but appears incremental as it builds on existing methods like PCA with graph theory.
The paper tackled the problem of identifying phase connectivity in smart grids, which is crucial for efficient operation, by proposing a data-driven approach using Principal Component Analysis and graph theory, and demonstrated the algorithm on simulated data.
Consumers with low demand, like households, are generally supplied single-phase power by connecting their service mains to one of the phases of a distribution transformer. The distribution companies face the problem of keeping a record of consumer connectivity to a phase due to uninformed changes that happen. The exact phase connectivity information is important for the efficient operation and control of distribution system. We propose a new data driven approach to the problem based on Principal Component Analysis (PCA) and its Graph Theoretic interpretations, using energy measurements in equally timed short intervals, generated from smart meters. We propose an algorithm for inferring phase connectivity from noisy measurements. The algorithm is demonstrated using simulated data for phase connectivities in distribution networks.