Robust state estimation in power systems using pre-filtering measurement data - Mohsen Khosravi; Mahdi Banejad; Heydar Toosian Shandiz (Journal of Artificial Intelligence and Data Mining, 2017)
State estimation is the foundation of any control and decision making in power networks. The first requirement for a secure network is a precise and safe state estimator in order to make decisions based on accurate knowledge of the network status. This paper introduces a new estimator which is able to detect bad data with few calculations without need for repetitions and estimation residual calculation. The estimator is equipped with a filter formed in different times according to Principal Component Analysis (PCA) of measurement data. In addition, the proposed estimator employs the dynamic relationships of the system and the prediction property of the Extended Kalman Filter (EKF) to estimate the states of network fast and precisely. Therefore, it makes real-time monitoring of the power network possible. The proposed dynamic model also enables the estimator to estimate the states of a large scale system online. Results of state estimation of the proposed algorithm for an IEEE 9 bus system shows that even with the presence of bad data, the estimator provides a valid and precise estimation of system states and tracks the network with appropriate speed.