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

Beordo, Lucas; Cari, Elmer P. T.; Landgraf, Taylon Gomes; Alberto, Luís

doi:10.1002/etep.2255

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Therefore, so-called online methods, conducted while the generator operates on-grid, are highly desirable because they do not impact the global function of the power system. However, it should be noted that some of the existing online methods require information about some specific variables, such as power angle [26]. On the other side, the identification method should be conducted using the equipment already in the power plant.…”

Section: A Backgroundmentioning

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: A Backgroundmentioning

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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“…All components are connected through the network that is represented by the nodal voltage equations:

∆ I_{xy} = Y_{redu} ∆ U_{xy},

where Y redu is the reduced nodal admittance matrix with the intermediate buses eliminated. Combining Equations , , and , the state‐space model of a power system is set up that is used in solving either eigenvalues or system trajectories as follows:

∆ \overset{X}{true} = ([],, A + B_{I} {(Y_{italicredu}^{- 1} - D_{I})}^{- 1} C) ∆ X + ([],, B_{c} + B_{I} {(Y_{italicredu}^{- 1} - D_{I})}^{- 1} D_{c}) ∆ U_{c} .

…”

Section: Building Feedback Modelmentioning

confidence: 99%

“…where Y redu is the reduced nodal admittance matrix with the intermediate buses eliminated. Combining Equations 11, 12, and 13, the state-space model of a power system is set up that is used in solving either eigenvalues or system trajectories [29][30][31] as follows:…”

Section: Building Feedback Modelmentioning

confidence: 99%

Developing feedback model for power system dynamic sensitivity analysis

Hill

et al. 2017

Int Trans Electr Energ Syst

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Summary Since sensitivity analysis provides information on changes of the system dynamics with respect to changes of system parameters, it has been long recognized as an important tool for power system small‐signal stability analysis and controller designs. Sensitivity analysis is now much more important due to all the anticipated changes of future power systems, so more efficient and feasible methods are needed. The current sensitivity analysis methods are either simulation based or eigenvalue based. The eigenvalues and/or the time domain trajectories of the investigated system are solved for scenarios both before and after the parameter changes to evaluate the impacts of these changes on the system dynamics. However, both methods are actually numerically based, so often insufficient in fully revealing system physical characteristics. This paper analyzes the drawbacks of the available methods for analyzing the dynamic impacts of parameter changes in a power system and then develops a feedback‐based model for sensitivity analysis. The model is easier to identify the dynamic interactions of components; meanwhile, it also offers a new perspective on sensitivity analysis from the frequency domain. Case studies of increasing complexity demonstrate fully that the application of the proposed method reveals new insights on power system dynamics that conventional methods could not do.

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Error Analysis of on-line Identification of Parameters of Synchronous Generators

Qiu¹,

Wu²,

Wang³

et al. 2018

2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

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

Cited by 5 publications

References 18 publications

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

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

Developing feedback model for power system dynamic sensitivity analysis

Error Analysis of on-line Identification of Parameters of Synchronous Generators

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