P Satya Jayadev

Satya Jayadev P., Shankar Narasimhan, Nirav Bhatt

A challenging problem in complex networks is the network reconstruction problem from data. This work deals with a class of networks denoted as conserved networks, in which a flow associated with every edge and the flows are conserved at all non-source and non-sink nodes. We propose a novel polynomial time algorithm to reconstruct conserved networks from flow data by exploiting graph theoretic properties of conserved networks combined with learning techniques. We prove that exact network reconstruction is possible for arborescence networks. We also extend the methodology for reconstructing networks from noisy data and explore the reconstruction performance on arborescence networks with different structural characteristics.

P Satya Jayadev, Aravind Rajeswaran, Nirav P Bhatt et al.

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.