Decentralized data-driven estimation of generator rotor speed and inertia constant based on adaptive unscented Kalman filter

This paper proposes an online decentralized and data-driven method for synchronous generator rotor speed inertia estimation. First, analytical relationship between the terminal voltage/current phasors its internal is derived using Thevenin equivalent. The parameters of latter are obtained via a recursive estimator, which allows us to estimate generator’s from without need any information. Second, we reformulate equivalent swing equation into Kalman filter form, enables use priori-estimated along with measured real reactive power injections obtain each generator. achieved through adaptive unscented filter. We demonstrate that bus frequency approximate estimation by existing methods leads large biases/errors. Numerical simulations carried out on IEEE 39-bus Texas 2000-bus systems reveal our proposed consistently more accurate than methods. Moreover, insensitive both type location system disturbances, it robust different levels measurement noise.