RT Conference Proceedings
T1 Learning-based State Estimation in Distribution Systems with Limited Real-Time Measurements.
A1 Gómez de la Varga, J.
A1 Pineda-Morente, Salvador
A1 Morales-González, Juan Miguel
A1 Porras, Álvaro
K1 Aprendizaje automático
AB 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.
YR 2024
FD 2024-01
LK https://hdl.handle.net/10630/32039
UL https://hdl.handle.net/10630/32039
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 23 ene 2026