Robust state estimation in power systems using pre-filtering measurement data

State estimation is the foundation of any control and decision-making in power networks. The first requirement of a secure network is a precise and safe state estimator in order to make decisions based on an accurate knowledge of the network status. This paper introduces a new estimator that is capable of detecting bad data using few calculations without the need for repetitions and estimation residual calculations. The estimator is equipped with a filter formed in different times according to the Principal Component Analysis (PCA) of the measurement data. In addition, the proposed estimator employs the dynamic relationships of the system and the prediction property of the Extended Kalman Filter (EKF) in order to estimate fast and precise network states. Therefore, it makes the real-time monitoring of the power network possible. The proposed dynamic model also enables the estimator to estimate online the states of a large-scale system. The results obtained for the state estimation of the proposed algorithm for an IEEE 9 bus system shows that even in the presence of bad data, the estimator provides a valid and precise estimation of the system states, and tracks the network with an appropriate speed.