Convolutional Neural Network for the Classification of the Control Mode of Grid-Connected Power Converters
With the integration of power converters into the power grid, it becomes crucial for the Transmission System Operator (TSO) to ascertain whether they are operating in Grid Forming or Grid Following modes. Due to intellectual properties, classification can only be performed based on non-intrusive mea...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/24/6458 |
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| author | Rabah Ouali Martin Legry Jean-Yves Dieulot Pascal Yim Xavier Guillaud Frédéric Colas |
| author_facet | Rabah Ouali Martin Legry Jean-Yves Dieulot Pascal Yim Xavier Guillaud Frédéric Colas |
| author_sort | Rabah Ouali |
| collection | DOAJ |
| description | With the integration of power converters into the power grid, it becomes crucial for the Transmission System Operator (TSO) to ascertain whether they are operating in Grid Forming or Grid Following modes. Due to intellectual properties, classification can only be performed based on non-intrusive measurements and models, such as admittance at the PCC. This classification poses a challenge as the TSO lacks precise knowledge of the actual control structures and algorithms. This paper introduces a novel classification algorithm based on Convolutional Neural Networks (CNN), capable of detecting patterns in sequential data. The proposed CNN utilizes a new architecture to separate admittances along the d and q axes, and a decision layer allows to determine the correct converter mode. The performance of the proposed CNN model was assessed through two tests and compared to the traditional feedforward model. The proposed CNN architecture demonstrates significant classification capabilities, as it is able to identify the control mode of the converter even when its control structure is not part of the training dataset. |
| format | Article |
| id | doaj-art-9f9fbef92c4749e6a41106c97ad8d295 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-9f9fbef92c4749e6a41106c97ad8d2952025-08-20T02:53:41ZengMDPI AGEnergies1996-10732024-12-011724645810.3390/en17246458Convolutional Neural Network for the Classification of the Control Mode of Grid-Connected Power ConvertersRabah Ouali0Martin Legry1Jean-Yves Dieulot2Pascal Yim3Xavier Guillaud4Frédéric Colas5Centre de Recherche en Informatique Signal et Automatique de Lille, University Lille, CNRS, Centrale Lille, UMR 9189, 59655 Lille, FranceL2EP—Laboratoire d’Electrotechnique et d’Electronique de Puissance, University Lille, Centrale Lille, Arts et Métiers Paris Tech, HEI, EA 2697, 59655 Lille, FranceCentre de Recherche en Informatique Signal et Automatique de Lille, University Lille, CNRS, Centrale Lille, UMR 9189, 59655 Lille, FranceCentre de Recherche en Informatique Signal et Automatique de Lille, University Lille, CNRS, Centrale Lille, UMR 9189, 59655 Lille, FranceL2EP—Laboratoire d’Electrotechnique et d’Electronique de Puissance, University Lille, Centrale Lille, Arts et Métiers Paris Tech, HEI, EA 2697, 59655 Lille, FranceL2EP—Laboratoire d’Electrotechnique et d’Electronique de Puissance, University Lille, Centrale Lille, Arts et Métiers Paris Tech, HEI, EA 2697, 59655 Lille, FranceWith the integration of power converters into the power grid, it becomes crucial for the Transmission System Operator (TSO) to ascertain whether they are operating in Grid Forming or Grid Following modes. Due to intellectual properties, classification can only be performed based on non-intrusive measurements and models, such as admittance at the PCC. This classification poses a challenge as the TSO lacks precise knowledge of the actual control structures and algorithms. This paper introduces a novel classification algorithm based on Convolutional Neural Networks (CNN), capable of detecting patterns in sequential data. The proposed CNN utilizes a new architecture to separate admittances along the d and q axes, and a decision layer allows to determine the correct converter mode. The performance of the proposed CNN model was assessed through two tests and compared to the traditional feedforward model. The proposed CNN architecture demonstrates significant classification capabilities, as it is able to identify the control mode of the converter even when its control structure is not part of the training dataset.https://www.mdpi.com/1996-1073/17/24/6458grid forminggrid followingdeep learningconvolutional neural networkfrequency admittance |
| spellingShingle | Rabah Ouali Martin Legry Jean-Yves Dieulot Pascal Yim Xavier Guillaud Frédéric Colas Convolutional Neural Network for the Classification of the Control Mode of Grid-Connected Power Converters Energies grid forming grid following deep learning convolutional neural network frequency admittance |
| title | Convolutional Neural Network for the Classification of the Control Mode of Grid-Connected Power Converters |
| title_full | Convolutional Neural Network for the Classification of the Control Mode of Grid-Connected Power Converters |
| title_fullStr | Convolutional Neural Network for the Classification of the Control Mode of Grid-Connected Power Converters |
| title_full_unstemmed | Convolutional Neural Network for the Classification of the Control Mode of Grid-Connected Power Converters |
| title_short | Convolutional Neural Network for the Classification of the Control Mode of Grid-Connected Power Converters |
| title_sort | convolutional neural network for the classification of the control mode of grid connected power converters |
| topic | grid forming grid following deep learning convolutional neural network frequency admittance |
| url | https://www.mdpi.com/1996-1073/17/24/6458 |
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