Robust recurrent neural network-based dynamic equivalencing in power system

Lino, O.-C.Y.

doi:10.1109/psce.2004.1397537

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…The accuracy and robustness of the DANN as dynamic equivalent are evaluated in multi-machine systems [19]. Area C constitutes the internal area; areas A and B forming the external area are replaced by the DANN.…”

Section: Dynamical Ann-based Equivalencingmentioning

confidence: 99%

Nonlinear Approaches for Dynamic Equivalencing in Power Systems

Lino

Fette

Dong

et al. 2006

2006 IEEE PES Power Systems Conference and Exposition

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This paper addresses innovative nonlinear approaches for dynamic equivalencing of machines for interconnected power systems. In contrast to the existing approaches that consider only fixed equivalencing steps without taking into consideration the machine parameters, these approaches reformulate the classical conditions incorporating real electromechanical model parameters and behaviours of the machines in the dynamic equivalencing. These aspects enable the integration of modern and intelligent techniques, such as the pattern recognition algorithms, Fuzzy concept as machine splitting factor and the system identification by dynamical artificial neural networks. The electromechanical-based approaches generate accurate robust, non-linear dynamic equivalents and thereby enhance significantly their consistency and practical application on network reliability, management and planning.Test of these approaches have been performed and evaluated in large-scale model of the European Interconnected Electric Power System (UCTE/CENTREL) and 16 multi machine system.

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Section: Dynamical Ann-based Equivalencingmentioning

confidence: 99%

Nonlinear Approaches for Dynamic Equivalencing in Power Systems

Lino

Fette

Dong

et al. 2006

2006 IEEE PES Power Systems Conference and Exposition

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“…For example, to obtain a dynamic equivalent of an external subsystem, an optimization problem has been solved by the Levenberg-Marquardt algorithm [14]. Within these techniques artificial neural networks (ANNs) are a prevalent method because of its high inherent ability for modeling nonlinear systems, including power system dynamic equivalents [12,[15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A strategy to simultaneously tune power system stabilizers

Ramı́rez

Correa

Hernández

2012

International Journal of Electrical Power & Energy Systems

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“…An interconnected power system can be divided into two subsystems, the internal area that has to be retained for detailed analysis and the external area that is to be reduced to a simplified model [2]. The conventional dynamic equivalencing is based on coherency identification.…”

Section: Introductionmentioning

confidence: 99%

“…The conventional dynamic equivalencing is based on coherency identification. It consists of the following classical steps [1][2][3][4][5][6]: (I)Coherency identification; (2)Model aggregation; (3)Static network reduction. Most classical approaches require a complete input parameter, but available measurements may be insufficient to reliably identify all the model parameters [1,7].…”

Section: Introductionmentioning

confidence: 99%

“…Most classical approaches require a complete input parameter, but available measurements may be insufficient to reliably identify all the model parameters [1,7]. In paper [2,7], authors are more care for the distribution dynamic equivalents. In this paper a recurrent ANN-based dynamic equivalencing for transmission networks in interconnected power systems is introduced and series parallel model (SPM) structure is chosen to identify equivalent model.…”

Section: Introductionmentioning

confidence: 99%

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Recurrent Neural Network-based Dynamic Equivalencing in Power System

Han

Deng

2007

2007 IEEE International Conference on Control and Automation

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In stability analysis, only particular areas need to be further detailed simulation and analyzed. It is espected that systems can be simplified and without employing great programming effort or extensive time-consuming computations. In this paper, dynamic equivalent for transmission networks in the interconnected power systems using the recurrent artificial neural netwoks (ANN) is introduced and series parallel model (SPM) structure is chosen to identify equivalent model. The approach depends on measurements of the boundary nodes, need not to postulate any fixed dynamic model in advance. The weighting parameters and structure of ANN will define the parameters and structure of the external areas. This new approach is used to simplify the 39 nodes network of IEEE, the results show the accuracy of the equivalent model and the validity of the proposed method.

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Robust recurrent neural network-based dynamic equivalencing in power system

Cited by 5 publications

References 8 publications

Nonlinear Approaches for Dynamic Equivalencing in Power Systems

Nonlinear Approaches for Dynamic Equivalencing in Power Systems

A strategy to simultaneously tune power system stabilizers

Recurrent Neural Network-based Dynamic Equivalencing in Power System

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