Distributed dynamic state-input estimation for power networks of Microgrids and active distribution systems with unknown inputs

This paper proposes a joint input and state dynamic estimation scheme for power networks in microgrids active distribution systems with unknown inputs. The conventional of the transmission system relies on forecasting methods to obtain state-transition model variables. However, under highly conditions operation networks, this approach may become ineffective as accuracy is not guaranteed. To overcome such drawbacks, employs derived from physical equations branch currents. Specifically, network linear state-space model, which vector consists currents, bus voltages. estimate both variables, we propose Kalman-based filtering algorithms batch-mode regression form, considering cross-correlation between states For scalability proposed scheme, distributed implementation also presented. Complementarily, predicted vectors are leveraged bad data detection. Results carried out 13-bus microgrid real-time Opal-RT platform demonstrate effectiveness method comparison traditional weighted least square tracking methods.