Reducing computational complexity in nonlinear power system state estimation via ANN-assisted linear Kalman filtering
This paper proposes a computationally efficient dynamic state estimation algorithm for nonlinear power networks using synchronized phasor measurements and a combination of an artificial neural network and a linear Kalman filter. The methodology involves using both the predicting abilities of the artificial neural net...