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This paper proposes a joint input and state dynamic estimation scheme forpower networks in microgrids and active distribution systems with unknowninputs. The conventional dynamic state estimation of power networks in thetransmission system relies on the forecasting methods to obtain thestate-transition model of state variables. However, under highly dynamicconditions in the operation of microgrids and active distribution networks,this approach may become ineffective as the forecasting accuracy is notguaranteed. To overcome such drawbacks, this paper employs the power networksmodel derived from the physical equations of branch currents. Specifically, thepower network model is a linear state-space model, in which the state vectorconsists of branch currents, and the input vector consists of bus voltages. Toestimate both state and input variables, we propose linear Kalman-based dynamicfiltering algorithms in batch-mode regression form, considering thecross-correlation between states and inputs. For the scalability of theproposed scheme, the distributed implementation is also presented.Complementarily, the predicted state and input vectors are leveraged for baddata detection. Results carried out on a 13-bus microgrid system in real-timeOpal-RT platform demonstrate the effectiveness of the proposed method incomparison with the traditional weighted least square and tracking stateestimation methods.

Distributed Dynamic State-Input Estimation for Power Networks of Microgrids and Active Distribution Systems with Unknown Inputs