Parameter estimation of synchronous generators from different types of disturbances

Information geometry for model identification and parameter estimation in renewable energy – DFIG plant case

“…where x is the vector of (differential) state variables, z is the algebraic variable, p is the parameter (typically assumed to be unknown in estimation studies) and t is the (scalar) time variable. System measurement vector is assumed to be of the form [6,7]…”

“…The second is that the vector p is poorly estimated in directions where the curvature is small (relative to directions with high curvature). Numerous literatures [3,[6][7][8] state that all parameters cannot be estimated together in typical cases. In our work, the illconditioned parameters are detected using participation factors of H p (t) [11].…”

“…In the sequel, we only list references directly related to our development here. Cari and Alberto [7] considered parameter estimation for a single generator and formulated the SG identification problem in a differential-algebraic equation (DAE) framework. Huang et al [8] considered the same overall setup, but with phasor measurement unit measurements.…”

A comparative validation of a synchronous generator by trajectory sensitivity and offline methods

“…it uses only measurements that are ease to obtain in the practice (terminal voltage and current, and field voltage), it does not need the power angle, it includes an algebraic equation in the model, and as a consequence, the power angle is estimated. This paper is a continuation of the work presented in Cari and Alberto 18 where the generator parameters were estimated using only simulation results. In this paper the generator parameters are validated using real measurements, and a comparison with offline methods are accomplished.…”

“…For the shortcomings of offline methods to be overcome, the online methods to determine parameters with the machine connected in the grid have been proposed. [10][11][12][13] New online approaches such as using an observer from neural networks and Prony method can be found in Shafighi et al, 14 Shariati et al, 15 and Deghani and Nikravesh, 16 respectively. In addition in Karayaka et al 17 the generator parameters are estimated using operating data.…”