Model-Free State Estimation Using Low-Rank Canonical Polyadic Decomposition

As electric grids experience high penetration levels of renewable generation, fundamental changes are required to address real-time situational awareness. This letter utilizes unique traits tensors devise a model-free awareness and energy forecasting framework for distribution networks. formulates the state network at multiple time instants as three-way tensor; hence, recovering full information is tantamount estimating all values tensor. Given measurements received from μphasor measurement units and/or smart meters, recovery unobserved quantities carried out using low-rank canonical polyadic decomposition tensor-that is, estimation task posed tensor imputation problem utilizing observed patterns in measured (sampled) quantities. Two structured sampling schemes considered, namely, asynchronous slab fiber sampling. For both schemes, we present sufficient conditions on number sampled slabs fibers that guarantee identifiability factors Numerical results demonstrate ability proposed achieve accuracy scenarios.