Dynamic oscillations predicted by computer studies

Butts, M.M.; Smith, H. S.

doi:10.1109/67.65036

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…Modeling power system components and networks is important for different studies in the planning and operation of electricity networks [1], and the level of complexity in the models depends on the type of studies carried out [2]. Traditional tools for power system modeling are usually tied to a specific time scale, often limiting the applicability and/or validity of the models to a specific kind of studies [3].…”

Section: Conventional Power System Modeling and Simulationmentioning

confidence: 99%

A Modelica Power System Component Library for Model Validation and Parameter Identification

Vanfretti¹,

Bogodorova²,

Baudette³

2014

Linköping Electronic Conference Proceedings

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This paper summarizes the work performed in one of the work-package of the FP7 iTesla project. This work consisted in the development of a power system component library for phasor time domain simulation in Modelica. The models were used to build power system network models, used in experiments for parameter identification. The experiments were carried out with the RAPID toolbox, which has been developed at SmarTS Lab within the same project. The toolbox was written in MATLAB, making use of FMI Technologies for interacting with Modelica models.

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Section: Conventional Power System Modeling and Simulationmentioning

confidence: 99%

A Modelica Power System Component Library for Model Validation and Parameter Identification

Vanfretti¹,

Bogodorova²,

Baudette³

2014

Linköping Electronic Conference Proceedings

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“…With this trend, new challenges for planning, operations and control of large-scale power networks will appear. This fact will make the dynamic simulation of large-scale networks [2] an even more daunting computational task for power system stability programs [3], [4], including those carrying out Dynamic Security Assessment (DSA) [5].…”

mentioning

confidence: 99%

Structured power system model reduction of non-coherent areas

Sturk

Vanfretti

Chompoobutrgool

2012

2012 IEEE Power and Energy Society General Meeting

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Abstract-This paper demonstrates how structured model reduction can be used to reduce the order of power systems without the need to identify coherent groups of generators. To this end the Klein-Rogers-Kundur 2-area system is studied in detail. It is shown how different modes of the system are captured as the model order is varied, which is of interest in e.g. distributed controller design, where the objective is to damp these oscillations. The power system is divided into a study area and an external area and the proposed algorithm is used to reduce the external area to a low order linear system, while retaining the nonlinear description of the study area. It is shown that this approach permits greater deviations from the steady-state than if a reduced system that is entirely linear is used, while still yielding accurate simulation results.Index Terms-Model reduction of power systems, internal systems, structured model reduction, dynamic equivalents

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On-line measurement of low-frequency oscillations in power systems

El-Naggar

2009

Measurement

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Dynamic oscillations predicted by computer studies

Cited by 4 publications

References 2 publications

A Modelica Power System Component Library for Model Validation and Parameter Identification

A Modelica Power System Component Library for Model Validation and Parameter Identification

Structured power system model reduction of non-coherent areas

On-line measurement of low-frequency oscillations in power systems

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