Power System Robust Decentralized Dynamic State Estimation Based on Multiple Hypothesis Testing

This paper proposes a fast and robust unscented Kalman filter based decentralized dynamic state estimator (DSE) for power system online monitoring and control. The proposed robust DSE is able to detect, identify, and suppress three types of outliers, namely the observation, innovation, and structural outliers. Observation outliers refer to the received PMU measurements providing unreliable metered values due to gross errors or cyber attacks; innovation outliers are typically caused by impulsive system process noise, whereas structural outliers are induced by incorrect parameters of the generators or its associated controllers, such as exciters and speed governors. To enable the fast estimation of generator states of large-scale power systems in a decentralized manner, two model decoupling approaches are presented and compared. It is shown that the generator decoupling approach presented in this paper achieves higher statistical efficiency than the ones proposed in the literature in the presence of both small and large measurement noise. To detect and distinguish three types of outliers, projection statistics based multiple hypothesis testing approach is proposed. Specifically, three hypotheses corresponding to the occurrence of three types of outliers are assumed by constructing three innovation matrices; these matrices are made up by time-correlated innovation vectors, and/or predicted states, and/or measurements; then projection statistics are applied to each of the innovation matrix and its calculated projection values are checked by a statistical test to validate the assumed hypothesis. The identified outliers are further suppressed by a generalized maximum-likelihood-type estimator. Numerical results carried out on the IEEE 39-bus system demonstrate the effectiveness and robustness of the proposed method.