PDF

Machine learning classification of power converter control mode

Rabah Ouali, Jean-Yves Dieulot

2022Englishelectricityelectronicsmachine learningpower converterscontrol modesgrid stability

Abstract

Language:

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.

Download

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  -

Similar Items

Optimal Current Control Strategy for Reliable Power Electronics Converters: Frequency-Domain Approach

Amin Rezaeizadeh, Silvia Mastellone

Power electronics converters are key enablers in the global energy transition for power generation, industrial and mobility applications; they convert

2024EnglishPDF

A Scoping Review of Machine Learning Applications in Power System Protection and Disturbance Management

Julian Oelhaf, Georg Kordowich

The integration of renewable and distributed energy resources has fundamentally reshaped modern power systems, challenging conventional protection sch

2023EnglishPDF

Paper Title (use style: paper title)

Amir Hossein Baradaran

Predictive maintenance is a key strategy for ensuring the reliability and efficiency of industrial systems. This study investigates the use of supervi

2020EnglishPDF

Machine Learning–Based Protection and Fault

Milad Beikbabaei, Michael Lindemann

100% inverter-based renewable units are becoming more prevalent, introducing new challenges in the protection of microgrids that incorporate these res

2024EnglishPDF

Learning to Design Analog Circuits to Meet Threshold Specifications

Dmitrii Krylov, Pooya Khajeh

Automated design of analog and radio-frequency circuits using supervised or reinforcement learning from simulation data has recently been studied as a

2023EnglishPDF

Machine Learning Driven Global Optimisation Framework for Analog Circuit Design

Ria Rashid, Komala Krishna

We propose a machine learning-driven optimisation framework for analog circuit design in this paper. Machine learning based global offline surrogate m

2024EnglishPDF