A new method to identify synchronous generator and
            <scp>turbine‐governor</scp>
            parameters of a gas unit using a
            <scp>closed‐loop</scp>
            model

Zaker, Behrooz; Khalili, Ramtin; Rabieyan, Hadi; Karrari, M.

doi:10.1002/2050-7038.13110

Cited by 8 publications

(3 citation statements)

References 37 publications

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“…Specific current and voltage waveforms during the power system's remote line-to-line fault can be used to determine the generator parameters, as depicted in [21]. The authors in [22] proposed an efficient three-stage algorithm for identifying the turbinegovernor and SG parameters using their precise models. The Unscented Kalman Filter (UKF) application to estimate the parameters of the SG during unbalanced operating conditions is presented in [23].…”

Section: Related Workmentioning

confidence: 99%

Artificial Neural Network-Based Nonlinear Black-Box Modeling of Synchronous Generators

Micev

Ćalasan

Radulović

et al. 2023

IEEE Trans. Ind. Inf.

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This paper deals with black-box modeling of synchronous generators based on artificial neural networks (ANN). ANN is applied to define the relationship between the excitation and terminal generator voltage values, while the Levenberg-Marquardt algorithm is used for determining the ANN's weight coefficients. The relation is made based on generator response on reference voltage step changes. The proposed approach is checked using the experimental results obtained from the measurements on a real 120 MVA generator from hydroelectric power plant Piva in Montenegro. Further, a fair comparison with the nonlinear auto-regression model with the exogenous input (NARX) and Hammerstein-Wiener (H-W) model is made. For the validation, different experiments were conducteddifferent values of step disturbances, other controller parameters, and different rotating speeds. Based on the presented results, it can be noted that the proposed ANN model is very accurate and provides a very high degree of matching with the experimental results and outperforms the other considered nonlinear models. Furthermore, the proposed test procedure and model is easy to implement and do not require disconnection of the generator from the grid or additional equipment for experimental realization. Such obtained models can be used for different testing types related to the excitation system.

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Section: Related Workmentioning

confidence: 99%

Artificial Neural Network-Based Nonlinear Black-Box Modeling of Synchronous Generators

Micev

Ćalasan

Radulović

et al. 2023

IEEE Trans. Ind. Inf.

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“…The SG model mathematical model has been widely studied in [1,31]. Although a seven-order model can be considered for the SG to reflect its behaviours completely, this model can put a heavy computational burden on control and PE algorithms.…”

Section: 4mentioning

confidence: 99%

Inclusive modelling and parameter estimation of a steam power plant using an LMI‐based unknown input reconstruction algorithm

Arastou

Rabieyan

Karrari

2021

IET Generation Trans & Dist

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A comprehensive dynamic model of a steam power plant, including turbine, governor, and boiler models, is proposed in this paper. The whole turbine-boiler control strategies inter alia turbine following, boiler following, coordinated control, and sliding pressure control modes are also taken into account in the modelling procedure, making the integrated model adaptable for all the steam units. A novel multi-stage algorithm is asserted in the other part of the paper to estimate the obtained model parameters using input-output data from operational tests. Regarding the fact that the mechanical power as the turbine output and one of the synchronous generator (SG) inputs cannot be measured directly, an efficacious unknown input reconstruction (UIR) method is employed in this paper to determine the mechanical power from SG measured signals. The UIR observer in this method is obtained by solving a linear matrix inequality (LMI). Other parameters are identified by applying a Genetic Algorithm (GA) to the measurement data. Finally, the acquired model and parameters are validated by employing the recorded data from several tests, and the results are discussed.

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“…The comprehensive model of SG and its equivalent circuits have been presented in [2][3][4]. In [5,6], a third-order model of SG has been taken into account for primary frequency control and state estimation under cyber attacks, respectively.…”

Section: Introductionmentioning

confidence: 99%

Parameter identification of small distributed hydro generators during excitation system start‐up

Arastou

Rabieyan

Karrari

et al. 2022

IET Generation Trans & Dist

Self Cite

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Concerning the penetration of renewable energy resources into the power grid, investigation of the power system dynamics to meet the power demand and maintain stability has gathered momentum recently. On the other hand, evaluating the generator parameters in the distributed generation (DG) units as a subsystem of a large-scale power grid is crucial for control and stability analysis purposes. To this aim, a simple and efficacious algorithm to identify the operational parameters of synchronous generators (SGs) in small hydro DGs is proposed here. The required data for the SG identification using the recommended approach is gathered during excitation system start-up while the DG is disconnected from the grid. Using the proposed test procedure, the adverse impact of magnetic saturation on estimation results can be avoided. Furthermore, a straightforward parameter estimation (PE) procedure is established here to obtain the intended parameters from the recorded data. Finally, the proposed method was applied to a 10 MW hydro DG, and the results are discussed.

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A new method to identify synchronous generator and turbine‐governor parameters of a gas unit using a closed‐loop model

Cited by 8 publications

References 37 publications

Artificial Neural Network-Based Nonlinear Black-Box Modeling of Synchronous Generators

Artificial Neural Network-Based Nonlinear Black-Box Modeling of Synchronous Generators

Inclusive modelling and parameter estimation of a steam power plant using an LMI‐based unknown input reconstruction algorithm

Parameter identification of small distributed hydro generators during excitation system start‐up

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