Online Parameter Estimation of a Transient Induction Generator Model Based on the Hybrid Method

Farias, Ecyo R. C.; Cari, Elmer P. T.; Erlich, I.; Shewarega, F.

doi:10.1109/tec.2018.2808238

Cited by 13 publications

(8 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, a constant value (off-line properly set) is used during the entire operating regime of the process. The plant is considered disturbed by a stochastic noise (with zero mean and variance σ 2 = 0.01), as a mandatory condition for a proper functioning of the RLS estimator (ensuring the numerical stability) [16,27,[30][31][32]. Also, a constant value λ=0.995 is identified and set for the forgetting factor, providing the best results.…”

Section: Design Of the Minimum Variance Control Lawmentioning

confidence: 99%

Minimum-Variance Control System with Variable Control Penalty Factor

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Design Of the Minimum Variance Control Lawmentioning

confidence: 99%

Minimum-Variance Control System with Variable Control Penalty Factor

et al. 2020

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…In the research, some methods are used to estimating parameters for simplified generators, as the Kalman [12] filter and the Trajectory Sensitivity Method (TSM) [13]. Farias et al [13] propose an online hybrid method to estimate the transitional model parameters for induction generators, from disturbed measurements. The hybrid algorithm based on trajectory sensitivity and the mapping optimization of the mean-variance uses practical measurements, as voltage and current on the generator terminals.…”

Section: Introductionmentioning

confidence: 99%

Parametric Regression Applied for Determination of Electrical Parameters of Synchronous and Induction Generators Operating in Parallel on the Electrical Energy Repowering System

et al. 2021

View full text Add to dashboard Cite

The purpose of this work is to determine the values of electrical parameters of synchronous and induction machines to validate electrical interactions between an induction generator and a synchronous generator. The generators are connected in two ways: (i) isolated from the common bus and (ii) parallel to the common bus in steady state, subject to nonlinear load. They are old and refurbished machines; thus, the parametric regression methodology is used to determine the electrical parameter values. After the computational model validation, analyses are performed by various system configurations to confirm the repowering and to analyze the system harmonic current flow. The results obtained comparing the computer simulation and experimental tests prove that the validated model represents the real system. With an experimentally validated computational model, it is possible to verify the occurrence of system repowering and the increased value of harmonic distortions in the induction generator terminals, acting as a preferential path for harmonic currents.

show abstract

“…In addition, both nameplate data and experimentally determined machine data are used for machine parameter determination. Despite of the importance of the generator option in wind energy systems, in the literature, the authors consider only motoring operation of the IM, while generator operation is only mentioned in two papers [34,72]. To redress this point, in this paper, measured values for the 2-kVA, 220-V/110-V, 50-Hz three-phase laboratory IM, as induction motor and generator are considered.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Induction Machine Single-Cage and Double-Cage Models Using a Hybrid Simulated Annealing–Evaporation Rate Water Cycle Algorithm

et al. 2020

View full text Add to dashboard Cite

This paper presents the usage of the hybrid simulated annealing—evaporation rate water cycle algorithm (SA-ERWCA) for induction machine equivalent circuit parameter estimation. The proposed algorithm is applied to nameplate data, measured data found in the literature, and data measured experimentally on a laboratory three-phase induction machine operating as an induction motor and as an induction generator. Furthermore, the proposed method is applied to both single-cage and double-cage equivalent circuit models. The accuracy and applicability of the proposed SA-ERWCA are intensively investigated, comparing the machine output characteristics determined by using SA-ERWCA parameters with corresponding characteristics obtained by using parameters determined using known methods from the literature. Also, the comparison of the SA-ERWCA with classic ERWCA and other algorithms used in the literature for induction machine parameter estimation is presented. The obtained results show that the proposed algorithm is a very effective and accurate method for induction machine parameter estimation. Furthermore, it is shown that the SA-ERWCA has the best convergence characteristics compared to other algorithms for induction machine parameter estimation in the literature.

show abstract

Online Parameter Estimation of a Transient Induction Generator Model Based on the Hybrid Method

Cited by 13 publications

References 17 publications

Minimum-Variance Control System with Variable Control Penalty Factor

Minimum-Variance Control System with Variable Control Penalty Factor

Parametric Regression Applied for Determination of Electrical Parameters of Synchronous and Induction Generators Operating in Parallel on the Electrical Energy Repowering System

Parameter Estimation of Induction Machine Single-Cage and Double-Cage Models Using a Hybrid Simulated Annealing–Evaporation Rate Water Cycle Algorithm

Contact Info

Product

Resources

About