A robust nonlinear position observer for synchronous motors with relaxed excitation conditions

Abstract

We would like to thank the Editor in Chief and the reviewers for their interest in our manuscript and also for providing many constructive comments and valuable suggestions. Their comments and suggestions have helped us to improve the quality of the paper, and have been included in the revised manuscript.

Online Estimation of Power System Inertia Using Dynamic Regressor Extension and Mixing

“…Our main contributions in this work are three-fold: arXiv:1812.00201v2 [cs.SY] 4 Apr 2019 1) We propose an algorithm which allows to estimate in real time the inertia constant and the aggregated mechanical power setpoint of a large-scale power system. The algorithm is derived using a first-order nonlinear aggregated power system model in combination with the recently proposed dynamic regressor and mixing (DREM) procedure [18], which already has been applied very successfully to a variety of electrical engineering applications [19]- [21].…”

“…Remark 4.1: As indicated in [28], the model (21) has proven to be sufficiently accurate for representing PFC effects provided predominantly by steam power plants. If a significant amount of other units, such as hydro or gas power plants, also contribute to PFC, then the model (21) should be modified to account for these dynamics. Since we are mainly concerned with inertia estimation (and the dynamics (6) are independent of the PFC mix), we leave this extension for future research.…”

Parameter identification of linear time‐invariant systems using dynamic regressor extension and mixing

“…Remark 3. [1][2][3][4][5][6]11 Remark 4. Besides the important element-by-element monotonicity property of the parameter errors captured by (12), this feature is instrumental to eliminate the need to overparameterize nonlinear regressions to obtain a linear one, a practice that, as is well known, 7,8,13 entails a serious performance degradation.…”

“…This and other advantages of DREM have been discussed in a series of publications. [1][2][3][4][5][6]11 Remark 4. It is well known that nonsquare integrability and PE of a signal are not equivalent properties, even in the scalar case.…”

“…A new procedure to design parameter estimators for linear and nonlinear regressions, called dynamic regressor extension and mixing (DREM), was recently proposed in the work of Aranovskiy et al 1 The technique has been successfully applied in a variety of identification and adaptive control problems. [2][3][4][5][6] For linear regressions, DREM estimators outperform classical gradient or least-squares estimators in the following precise aspect: independent of the excitation conditions, DREM guarantees monotonicity of each element of the parameter error vector that is much stronger than monotonicity of the vector norm, which is ensured with classical estimators. Another interesting property of DREM is that its convergence is established without the usual restrictive requirement of regressor persistence of excitation (PE).…”