Correlation-Aided Robust Decentralized Dynamic State Estimation of Power Systems With Unknown Control Inputs

Abstract

This paper proposes a correlation-aided robust adaptive unscented Kalman filter (UKF) for power system decentralized dynamic state estimation with unknown inputs, termed as robust AUKF-UI. The temporal and spatial correlations among the unknown inputs are used to derive a vector auto-regressive (VAR) model in an adaptive manner. This VAR model is further integrated together with state transition and measurement models for joint state and unknown inputs estimation. This allows taking into account the implicit cross-correlations between the states and the unknown inputs. As a result, the rank requirement for unknown input vector estimation is relaxed and the local generator frequency measurement is not required. The temporal correlations of time series innovation vectors, predicted state and input vectors are also leveraged by the robust AUKF-UI to detect, identify and process bad data. Without these correlations, it is very challenging to address bad data with unknown inputs. Simulation results carried out on the IEEE 39-bus system demonstrate that, thanks to the use of hidden system temporal and spatial correlations, the proposed robust AUKF-UI has lower requirement of the number of measurements for dynamic state estimation while achieving better robustness against bad data as compared to the existing UKF methods with unknown inputs.