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New topologies of kalman filters for dynamic power system estimation

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Author: 
Arpit Khandelwal, Akash Saxena and Ankush Tandon
Abstract: 

Power system state estimation requires error less data to estimate the exact states of the power system. The Estimation process is done by Energy Management System (EMS) at the control centre with the help of estimated data. In practical conditions, collected data contain the measurement and process errors. These errors are due to high speed measuring devices and Phasor Measurement Units (PMU) installed on different buses. Due to communication errors, different filtration techniques are required at the control centre to get the best estimated data. For nonlinear power system, new improved Kalman filter techniques are introduced in this paper. Emerging Extended Kalman Filter (E-EKF) and Emerging Unscented Kalman Filter (E-UKF) based on the exponential description function are proposed in this paper. The effectiveness of these improved techniques is compared with the conventional nonlinear filterson the basis of elapsed time and Root Mean Square Error (RMSE). The performance of these filters is tested on standard IEEE-30 bus test system.