Considerations Regarding the Design of a Minimum Variance Control System for an Induction Generator

Filip, Ioan; Mihet‐Popa, Lucian; Vasar, Cristian; Proştean, Octavian; Szeidert, Iosif

doi:10.3390/electronics8050532

Cited by 4 publications

(7 citation statements)

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“…A change of the operating point has as consequence a change of the linear model's parameters and the real-time tracking of these can be done by such a parameter estimation algorithm (in this case, the recursive least square estimator-RLS) [12][13][14]. For the case of an induction generator connected to a power system, its complete functionality is described by a nonlinear mathematical model of the seventh order (the d-q model represented by the Park equations) [10,22,23]. The complete model (and the parameter values) of the considered process has already been presented in extenso in one of the author's recent paper [10] and therefore it is not detailed in the present paper (being mentioned only in an Appendix A at the end of the paper).…”

Section: Design Of the Minimum Variance Control Lawmentioning

confidence: 99%

“…For the case of an induction generator connected to a power system, its complete functionality is described by a nonlinear mathematical model of the seventh order (the d-q model represented by the Park equations) [10,22,23]. The complete model (and the parameter values) of the considered process has already been presented in extenso in one of the author's recent paper [10] and therefore it is not detailed in the present paper (being mentioned only in an Appendix A at the end of the paper).…”

Section: Design Of the Minimum Variance Control Lawmentioning

confidence: 99%

“…However, the influence of this factor over the control system perturbed by external disturbances is quite low [18,25,27,28]. The influence of this tuning parameter (λ) is much more significant in the case of process with time-varying internal parameters [10,12,18,29]. Therefore, a constant value (off-line properly set) is used during the entire operating regime of the process.…”

Section: Design Of the Minimum Variance Control Lawmentioning

confidence: 99%

“…Furthermore, if the operating point changes under the action of external disturbances (taking into account a wide variation range of disturbances) or due to the variation of internal parameters, the usage of classical control solutions (PI, PID controllers) becomes infeasible [5,7]. In these cases, a minimum variance control system is a viable solution due to its adaptive nature, ensured by the self-tuning characteristic of the controller parameters [8][9][10]. Obviously, with these parameter estimates computed in real-time, a parameter estimator is required (as a necessary component integrated into the control system) [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the stability of the minimum-variance control system, a complete analysis was carried out by authors in other papers [10,11,16,17].…”

mentioning

confidence: 99%

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Minimum-Variance Control System with Variable Control Penalty Factor

et al. 2020

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The present paper proposes (as the main contribution) an additional self-tuning mechanism for an adaptive minimum-variance control system, whose main goal is to extend its functionality for a large value range of unmeasurable perturbations which disturb the controlled process. Through the standard design procedure, a minimum variance controller uses by default an internal self-tuning mechanism based on the process parameter estimates. However, the main parameter which overwhelmingly influences the control performance is the control penalty factor ( ρ ) . This parameter weights the term that describes the control variance in a criterion function whose minimization is the starting point of the control law design. The classical minimum-variance control involves an off-line tuning of this parameter, its value being set as constant throughout the entire operating regime. Based on the measurement of the process output error, the contribution of the proposed strategy consists in a real-time tuning of the control penalty factor, to ensure the stability of the control system, even under conditions of high disturbances. The proposed tuning mechanism adjusts this parameter by implementing a bipositional switching strategy based on a sharp hysteresis loop. Therefore, instead of the standard solution that involves a constant value of the control penalty factor ρ (a priori computed and set), this paper proposes a dual value for this controller parameter. The main objective is to allow the controlled process to operate in a stable fashion even in more strongly disturbed regimes (regimes where the control system becomes unstable and is usually switched off for safety reasons). To validate the proposed strategy, an induction generator integrated into a wind energy conversion system was considered as controlled plant. Operating under the action of strong disturbances (wind gusts, electrical load variations), the extension of safe operating range (thus avoiding the system disengagement) is an important goal of such a control system.

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