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Intelligent ITSC Fault Detection in PMSG Using the Machine Learning Technique

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Artificial Intelligence: Theories and Applications (ICAITA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1769))

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Abstract

In a wind turbine system, the permanent magnet synchronous generator (PMSG) is the key element that converts wind energy into electrical energy. According to global statistics, Inter-turns short circuit (ITSC) faults are the frequent electrical stator faults and the major cause of failures.

This paper presents an automatic and intelligent method to detect an ITSC fault in the PMSG based on the machine learning technique. For this aim, this work presents first the two used relevant indicators of ITSC fault extracted from the negative sequence voltage to detect the ITSC fault, then, it describes the database useful to train four machine learning algorithms which are the K-Nearest Neighbors “K-NN”, Naïve Bayes “NB”, Support Vector Machines “SVM”, and Decision tree “DT”. Finally, a comparative study is elaborated to evaluate the effectiveness of each of them and choice the best algorithm that gives the best classification performances in terms of turnaround time and the following metrics: Accuracy, Precision, Recall, and F1 score.

The outcomes of the comparison study show that the SVM algorithm is the best classification machine-learning algorithm for automatic ITSC fault detection since it has the highest performances.

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Authors and Affiliations

  1. LR11ES15 Laboratoire des Systèmes Electriques, Ecole Nationale d’Ingénieurs de Tunis, Tunis, Tunisia

    Issam Bahloul, Monia Bouzid & Sejir Khojet El Khil

Authors
  1. Issam Bahloul
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  2. Monia Bouzid
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  3. Sejir Khojet El Khil
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Corresponding author

Correspondence to Issam Bahloul .

Editor information

Editors and Affiliations

  1. University of Mascara, Mascara, Algeria

    Mohammed Salem

  2. University of Granada, Granada, Spain

    Juan Julián Merelo

  3. Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

  4. University of Mascara, Mascara, Algeria

    Rochdi Bachir Bouiadjra

  5. University of Mascara, Mascara, Algeria

    Mohamed Debakla

  6. University of Mascara, Mascara, Algeria

    Fatima Debbat

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Bahloul, I., Bouzid, M., Khil, S.K.E. (2023). Intelligent ITSC Fault Detection in PMSG Using the Machine Learning Technique. In: Salem, M., Merelo, J.J., Siarry, P., Bachir Bouiadjra, R., Debakla, M., Debbat, F. (eds) Artificial Intelligence: Theories and Applications. ICAITA 2022. Communications in Computer and Information Science, vol 1769. Springer, Cham. https://doi.org/10.1007/978-3-031-28540-0_15

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  • DOI: https://doi.org/10.1007/978-3-031-28540-0_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28539-4

  • Online ISBN: 978-3-031-28540-0

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