2026-06-04T05:45:13Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/320392026-02-03T12:20:29Zcom_10630_2254col_10630_37959

00925njm 22002777a 4500 dc Gómez de la Varga, J. author Pineda-Morente, Salvador author Morales-González, Juan Miguel author Porras, Álvaro author 2024-01 The task of state estimation in active distribution systems faces a major challenge due to the integration of different measurements with multiple reporting rates. As a result, distribution systems are essentially unobservable in real time, indicating the existence of multiple states that result in identical values for the available measurements. Certain existing approaches utilize historical data to infer the relationship between real-time available measurements and the state. Other learning-based methods aim to estimate the measurements acquired with a delay, generating pseudo-measurements. Our paper presents a methodology that utilizes the outcome of an unobservable state estimator to exploit information on the joint probability distribution between real-time available measurements and delayed ones. Through numerical simulations conducted on a realistic distribution grid with insufficient real-time measurements, the proposed procedure showcases superior performance compared to existing state forecasting approaches and those relying on inferred pseudo-measurements. https://hdl.handle.net/10630/32039 Aprendizaje automático Learning-based State Estimation in Distribution Systems with Limited Real-Time Measurements.