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866. Learning-based State Estimation in Distribution Systems with Asynchronized Measurements
Invited abstract in session MA-19: Learning-assisted Optimization in Energy Problems, stream OR in Energy.
Monday, 8:30-10:00Room: 44 (building: 116)
Authors (first author is the speaker)
| 1. | Jose Gomez de la Varga
|
| OASYS Group / Electrical Engineering, University of Malaga | |
| 2. | Salvador Pineda Morente
|
| Electrical Engineering, University of Málaga | |
| 3. | Juan Miguel Morales
|
| Applied Mathematics, University of Málaga | |
| 4. | Álvaro Porras Cabrera
|
| OASYS Group, University of Málaga |
Abstract
The task of state estimation in distribution systems faces a major challenge due to the integration of different measurements with multiple reporting rates and asynchronization. As a result, distribution power 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 electricity network with asynchronized measurements, the proposed procedure showcases superior performance compared to existing state forecasting approaches and those relying on inferred pseudo-measurements.
Keywords
- Machine Learning
- OR in Energy
- Forecasting
Status: accepted
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