Sun, BoXu, YijunWang, QinlingLu, ShuaiYu, RuizhiGu, WeiMili, Lamine M.2024-01-222024-01-222023-12-040885-8950https://hdl.handle.net/10919/117506As a model reduction tool, coherency identification has been extensively investigated by power researchers using various model-driven and data-driven approaches. Model-driven approaches typically lose their accuracy due to linear assumptions and parameter uncertainties, while data-driven approaches inevitably suffer from bad data issues. To overcome these weaknesses, we propose a data-driven cumulant tensor-based approach that can identify coherent generators and detect anomalies simultaneously. More specifically, it converts the angular velocities of generators into a fourth-order cumulant tensor that can be decomposed to reflect the coherent generators. Also, using co-kurtosis in the cumulant tensor, anomalies can be detected as well. The simulations reveal its excellent performance.Pages 1-4application/pdfenIn CopyrightArticle - Refereedhttps://doi.org/10.1109/TPWRS.2023.3338958PP99Mili, Lamine [0000-0001-6134-3945]1558-0679