q-Calculus based extended Kalman filter for the dynamic state estimation of a synchronous generator

The evolution of power systems into smart systems requires complex, efficient, and effective algorithms for control and optimization of the system. With this motivation, we propose and study a novel extended Kalman filter (EKF) algorithm utilizing q-calculus for the real-time dynamic state estimation (DSE) problem for synchronous generators. A 4
^th
order nonlinear synchronous generator model is studied for DSE. Two distinct cases were looked into, one involving normal simulation and the other involving short circuit fault simulation. The observed advantages of the proposed algorithm are faster convergence and better mean-square-error (MSE) performance. As a further advantage, utilization of q-calculus also introduces a tunable q-parameter into the EKF algorithm. The DSE problem is studied under in an environment of Gaussian noise and compared with the traditional EKF. Further research work is suggested involving comparison with other DSE algorithms and in-depth analysis of the algorithm.