Sun, BoXu, YijunGu, WeiCai, HuihuangLu, ShuaiMili, Lamine M.Yu, WenwuWu, Zhi2024-12-132024-12-132024-09-301949-3053https://hdl.handle.net/10919/123790As electric vehicles (EVs) gain popularity, their interaction with the power system cannot be overlooked. Therefore, there is a growing need for accurate EV load data to facilitate precise operation and control in power systems. However, in practice, due to the high cost of high-frequency measurement devices and limited data storage capacity, only low-resolution metered EV data are available. To address this, this paper proposed a tensor completion-based method for EV load data reconstruction. More specifically, we first reformulate the load data as high-dimensional tensors and consider unknown data to be recovered as missing entries. Subsequently, we leverage the low-rank properties of high-dimensional data to perform tensor completion. To achieve this, two optimization formulations are proposed: a nuclear norm minimization algorithm based on singular value thresholding (SVT) and a tensor rank approximation algorithm via parallel matrix factorization. Both approaches are based on the tensor train (TT) rank, thanks to its well-balanced matricization scheme. This enables us to cost-effectively reconstruct high-resolution EV data using only low-resolution measurements. Simulation results using real industrial data reveal the excellent performance of the proposed methods.application/pdfenIn CopyrightArticle - Refereedhttps://doi.org/10.1109/TSG.2024.347107799Mili, Lamine [0000-0001-6134-3945]1949-3061