A feature based distributed machine learning for post fault restoration of a microgrid under different stochastic scenarios

Stochastic nature of a large scale wind power plant in a power system with insufficient load margin has significant impact on a post fault network. Such probabilistic character of a system makes it quite a challenge to maintain post fault system stability. A short circuit fault under such contingenc...

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Bibliographic Details
Published in2017 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia) pp. 1 - 6
Main Authors Karim, Miftah Al, Currie, Jonathan, Lie, Tek-Tjing
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2017
Subjects
Circuit faults
Generators
Microgrids
Reactive power
Rotors
Stability analysis
Wind power generation
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Summary:Stochastic nature of a large scale wind power plant in a power system with insufficient load margin has significant impact on a post fault network. Such probabilistic character of a system makes it quite a challenge to maintain post fault system stability. A short circuit fault under such contingency may introduce power system oscillation resulting in massive voltage fluctuations. One probable solution is to develop a corrective voltage control (CVC) framework in order to maintain sufficient load margin. Standard CVC measures are based on active and reactive dispatch from generating units. However, in a post contingent scenario it is often critical to select appropriate parameters for CVC. This study implements an offline-online data analysis approach using feature selection and machine learning algorithms, as a mean to develop an accurate CVC framework based on supervisory machine control.
ISSN:2378-8542
DOI:10.1109/ISGT-Asia.2017.8378412