Un nuevo algoritmo de estimación de estado dinámico para redes de distribución utilizando diferentes métodos de pronóstico

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.