A novel dynamic state estimation algorithm for distribution networks using different forecasting methods
Keywords:
State estimation, distribution networks, forecastingAbstract
This paper presents a State Estimation Algorithm for Distribution Networks based on the Unscented
Kalman Filter (UKF). Instead of nodal voltages, this method uses nodal active and reactive power as
state variables in the dynamic model. Different demand forecast algorithms to assist the prediction step
were tested and compared. In addition, the algorithm includes the software package OpenDSS as a
calculation engine.
We used the 13-node IEEE distribution network to test the methodology by simulating several scenarios
of measurement generation. These simulations were based on the measured values of a local energy
distribution company. The results exposed different performance levels depending on the nature of the
scenario and the selected forecast method.
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Copyright (c) 2024 Lautaro E. Valenzuela, Matias Antunez, Mariano A. Risso, Pablo A. Lotito, Aldo Rubiales
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright of their work and grant the journal the right of first publication under the Creative Commons CC-BY Attribution License, which permits unrestricted use, distribution, and reproduction provided the original authorship and the journal’s first publication are acknowledged.
