Model order selection for probing-based power system mode estimation

Perić, Vedran S.; Bogodorova, Tetiana; Mete, Ahmet Naci; Vanfretti, Luigi

doi:10.1109/peci.2015.7064883

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Concluding Remarks and Future Work1

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2016

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“…It is thus necessary to define alternative methods to properly estimate the expected dynamic response of power systems. With this aim, we consider that the following techniques are promising candidates: graybox modelling approach [19]; Bayesian linear uncertainty analysis [20], and probing techniques for model order selection [21]. Future work will focus on the implementation of efficient and accurate methods to define such estimations for large scale power system models.…”

Section: Concluding Remarks and Future Workmentioning

confidence: 99%

On the stochastic nature of deterministic power system models for dynamic analysis

Milano

Zárate-Miñano

2016

2016 IEEE Power and Energy Society General Meeting (PESGM)

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The paper presents a study on the impact of uncertainty on the dynamic response of electric power systems. Three sources of uncertainty are considered, namely, (i) uncertainty in the values of the parameters of physical devices; (ii) uncertainty in the models of dynamic devices; and (iii) variations of the parameters and the numerical scheme to integrate the differential algebraic equations that describe the system. A Monte Carlo analysis is used to define the impact of each source of uncertainty as well as all sources together on the dynamic response of the well-known IEEE 14-bus system.

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Section: Concluding Remarks and Future Workmentioning

confidence: 99%