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Machine learning classification of power converter control mode

Rabah Ouali, Jean-Yves Dieulot

2022Englishelectricityelectronicsmachine learningpower converterscontrol modesgrid stability

Abstract

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To ensure the proper functioning of the current and future electrical grid, it is necessary for Transmission System Operators (TSOs) to verify that energy providers comply with the grid code and specifications provided by TSOs. A lot of energy production are connected to the grid through a power electronic inverter. Grid Forming (GFM) and Grid Following (GFL) are the two types of operating modes used to control power electronic converters. The choice of control mode by TSOs to avoid impacting the stability of the grid is crucial, as is the commitment to these choices by energy suppliers. This article proposes a comparison between commonplace machine learning algorithms for converter control mode classification: GFL or GFM. The classification is based on frequency-domain admittance obtained by external measurement methods. Most algorithms are able to classify accurately when the control structure belongs to the training data, but they fail to classify modified control structures with the exception of the random forest algorithm.

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Cite This Work

@article{1622817a-2878-46df-bce1-351d9731603a,
  title={Machine learning classification of power converter control mode},
  author={Rabah Ouali and Jean-Yves Dieulot},
  year={2022},
  language={English}
}
TY  - JOUR
TI  - Machine learning classification of power converter control mode
AU  - Rabah Ouali
AU  - Jean-Yves Dieulot
PY  - 2022
LA  - English
ER  -

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