Machine Learning Based Fault Diagnosis in Variable Speed Synchronous Generators

Mithat Önder; Muhsin Uğur Doğan

doi:10.29130/dubited.1808533

TR EN

Değişken Hızlı Senkron Jeneratörlerde Makine Öğrenmesine Dayalı Arıza Teşhisi

Abstract

Bu çalışmada, değişken hızlı senkron jeneratörlerde arıza teşhisi amacıyla beş farklı makine öğrenmesi algoritması olan Karar Ağacı, Rastgele Orman, En Yakın Komşular, Destek Vektör Makineleri ve Lojistik Regresyonun performansları incelenmiştir. Gerçek deneysel verilerden oluşan veri seti, jeneratörün hem sağlıklı hem de arızalı çalışma durumlarını içermektedir. Veri üzerinde Normalizasyon, Z-Score Standartlaştırması ve Özellik Seçimi gibi ön işleme adımları uygulanarak, bu işlemlerin sınıflandırma performansına etkileri değerlendirilmiştir. Elde edilen bulgulara göre, Karar Ağacı algoritması %99.43 doğruluk oranı ve 0.975 MCC değeri ile en yüksek performansı göstermiştir. Rastgele Orman algoritması benzer sonuçlar verirken, En Yakın Komşular, Destek Vektör Makineleri ve Lojistik Regresyon algoritmaları daha düşük doğruluk değerlerine ulaşmıştır. Ön işleme adımlarının model performansında anlamlı bir artış sağlamadığı, veri setinin ölçek açısından zaten dengeli olduğu belirlenmiştir. Sonuçlar, Karar Ağacı algoritmasının değişken hızlı senkron jeneratörlerde arıza tespiti için en uygun ve güvenilir yöntem olduğunu ortaya koymuştur. Bu çalışma, makine öğrenmesi tabanlı yaklaşımların jeneratörlerde erken arıza teşhisinde etkin bir şekilde kullanılabileceğini göstermektedir.

Keywords

Senkron jeneratör, makine öğrenmesi, arıza teşhisi.

Ethical Statement

Bu makale daha önce yayınlanmamıştır ve başka bir yerde yayınlanması için değerlendirme aşamasında değildir.

Machine Learning Based Fault Diagnosis in Variable Speed Synchronous Generators

Abstract

In this study, the performance of five different machine learning algorithms, decision tree, random forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and logistic regression, was investigated for fault diagnosis in variable-speed synchronous generators. The dataset, consisting of real-world experimental data, includes both healthy and faulty generator operating states. Pre-processing steps such as normalization, Z-Score standardization, and feature selection were applied to the data, and the effects of these processes on classification performance were evaluated. According to the findings, the decision tree algorithm achieved the highest performance with an accurate rate of 99.43% and Matthews Correlation Coefficient (MCC) value of 0.975. While the random forest algorithm yielded similar results, the KNN, SVM, and logistic regression algorithms achieved lower accuracy values. It was determined that the pre-processing steps did not provide a significant increase in model performance, and the dataset was already balanced in terms of scale. The results revealed that the decision tree algorithm is the most suitable and reliable method for fault detection in variable-speed synchronous generators. This study demonstrates that machine learning-based approaches can be used effectively in early fault diagnosis in generators.

Keywords

Synchronous generator, Machine learning, Fault diagnosis

Supporting Institution

This research received no external funding.

Ethical Statement

This study does not involve human or animal participants. All procedures followed scientific and ethical principles, and all referenced studies are appropriately cited.

Thanks

The authors would like to express their sincere thanks to the editor and the anonymous reviewers for their helpful comments and suggestions.

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Details

Primary Language

English

Subjects

Electrical Engineering (Other)

Journal Section

Research Article

Authors

Mithat Önder ^*
0000-0001-8577-3659
Türkiye

Muhsin Uğur Doğan
0000-0001-7341-1714
Türkiye

Publication Date

January 21, 2026

Submission Date

October 22, 2025

Acceptance Date

December 25, 2025

Published in Issue

Year 2026 Volume: 14 Number: 1

DOI

https://doi.org/10.29130/dubited.1808533

IZ

https://izlik.org/JA34BZ44FM

APA

Önder, M., & Doğan, M. U. (2026). Machine Learning Based Fault Diagnosis in Variable Speed Synchronous Generators. Duzce University Journal of Science and Technology, 14(1), 143-151. https://doi.org/10.29130/dubited.1808533

AMA

1.Önder M, Doğan MU. Machine Learning Based Fault Diagnosis in Variable Speed Synchronous Generators. DUBİTED. 2026;14(1):143-151. doi:10.29130/dubited.1808533

Chicago

Önder, Mithat, and Muhsin Uğur Doğan. 2026. “Machine Learning Based Fault Diagnosis in Variable Speed Synchronous Generators”. Duzce University Journal of Science and Technology 14 (1): 143-51. https://doi.org/10.29130/dubited.1808533.

EndNote

Önder M, Doğan MU (January 1, 2026) Machine Learning Based Fault Diagnosis in Variable Speed Synchronous Generators. Duzce University Journal of Science and Technology 14 1 143–151.

IEEE

[1]M. Önder and M. U. Doğan, “Machine Learning Based Fault Diagnosis in Variable Speed Synchronous Generators”, DUBİTED, vol. 14, no. 1, pp. 143–151, Jan. 2026, doi: 10.29130/dubited.1808533.

ISNAD

Önder, Mithat - Doğan, Muhsin Uğur. “Machine Learning Based Fault Diagnosis in Variable Speed Synchronous Generators”. Duzce University Journal of Science and Technology 14/1 (January 1, 2026): 143-151. https://doi.org/10.29130/dubited.1808533.

JAMA

1.Önder M, Doğan MU. Machine Learning Based Fault Diagnosis in Variable Speed Synchronous Generators. DUBİTED. 2026;14:143–151.

MLA

Önder, Mithat, and Muhsin Uğur Doğan. “Machine Learning Based Fault Diagnosis in Variable Speed Synchronous Generators”. Duzce University Journal of Science and Technology, vol. 14, no. 1, Jan. 2026, pp. 143-51, doi:10.29130/dubited.1808533.

Vancouver

1.Mithat Önder, Muhsin Uğur Doğan. Machine Learning Based Fault Diagnosis in Variable Speed Synchronous Generators. DUBİTED. 2026 Jan. 1;14(1):143-51. doi:10.29130/dubited.1808533

Değişken Hızlı Senkron Jeneratörlerde Makine Öğrenmesine Dayalı Arıza Teşhisi

Abstract

Keywords

Ethical Statement

Machine Learning Based Fault Diagnosis in Variable Speed Synchronous Generators

Abstract

Keywords

Supporting Institution

Ethical Statement

Thanks

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite