Prediction of Sector and Region for Three-Level NPC Inverters Using Artificial Neural Networks

Fatih Özen; Rana Ortaç Kabaoğlu; Tarık Veli Mumcu

doi:10.29130/dubited.1739006

TR EN

Yapay Sinir Ağları Kullanarak Üç Seviyeli NPC İnvertörler için Sektör ve Bölge Tahmini

Abstract

Yenilenebilir enerji sistemlerinin en önemli donanım birimi olan inverterlerin performansı, referans gerilim vektörüne bağlı olarak çalıştığı sektör ve bölge değerlerine bağlıdır. Sektör ve bölgenin doğru bir şekilde tespit edilmesi kritik bir öneme sahiptir. Bu çalışma, klasik matematiksel modellere dayalı sektör ve bölge tespitinin eksikliklerini ortadan kaldırmayı amaçlamaktadır. Bu amaç doğrultusunda sektör (6 sınıf) ve bölge (4 sınıf) tespitinin yapay sinir ağları (ANN) mimarileri yardımıyla yüksek doğrulukta tahmin edilmesi sağlanmıştır. Bu kapsamda, Narrow, Medium, Wide, Bilayered ve Trilayered mimari yapıları kullanılmıştır ve sistematik bir şekilde karşılaştırılmıştır. Sektör tespitinde, Narrow NN ve Wide NN %99,97 doğruluk elde ederek en yüksek performansı göstermişlerdir. Bölge tespitinde ise Wide NN %98,81 başarım oranı ile diğer mimari yapılar arasında en yüksek performans değerine sahiptir. Önerilen mimari simülasyon ortamında modellenerek inverter sektör ve bölge, çıkış akım ve gerilim değerleri bakımından incelenmiştir. Simulasyon sonuçları, klasik ve yapay sinir ağları mimarisine dayalı modellerin uyumlu olduğunu ortaya koymakta ve işlem yükünü azaltmaya yönelik bir çözüm sunmaktadır.

Keywords

Inverter, sektör ve bölge tespiti, yapay sinir ağları

Prediction of Sector and Region for Three-Level NPC Inverters Using Artificial Neural Networks

Abstract

The performance of inverters, the most important hardware unit of renewable energy systems, depends on the sector and region values in which they operate in relation to the reference voltage vector. The accurate identification of sectors and regions is crucial. This study aims to overcome the shortcomings of sector and region identification based on classical mathematical models. To this end, the identification of sectors (6 classes) and regions (4 classes) is predicted with highly accuracy using artificial neural network (ANN) architectures. In this context, Narrow, Medium, Wide, Bilayered and Trilayered architectures were used and systematically compared. For sector detection, Narrow NN and Wide NN showed the highest performance with 99.97% accuracy. For region detection, Wide NN has the highest performance among the other architectures with 98.81% accuracy. The proposed architecture is modelled in a simulation environment and analyzed in terms of inverter sector and region, output current and voltage values. The simulation results show that the models based on classical and artificial neural networks are compatible and provide a solution to reduce the processing load.

Keywords

Inverter, sector and region, artificial neural networks

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 author/authors do not wish to acknowledge any individual or institution.

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Details

Primary Language

English

Subjects

Machine Learning Algorithms, Electrical Energy Generation (Incl. Renewables, Excl. Photovoltaics), Electrical Engineering (Other)

Journal Section

Research Article

Authors

Fatih Özen ^*
0000-0002-4232-5394
Türkiye

Rana Ortaç Kabaoğlu
0000-0003-0861-0711
Türkiye

Tarık Veli Mumcu
0000-0002-8995-9300
Türkiye

Publication Date

January 21, 2026

Submission Date

July 9, 2025

Acceptance Date

October 16, 2025

Published in Issue

Year 2026 Volume: 14 Number: 1

DOI

https://doi.org/10.29130/dubited.1739006

IZ

https://izlik.org/JA78LT76RW

APA

Özen, F., Ortaç Kabaoğlu, R., & Mumcu, T. V. (2026). Prediction of Sector and Region for Three-Level NPC Inverters Using Artificial Neural Networks. Duzce University Journal of Science and Technology, 14(1), 60-71. https://doi.org/10.29130/dubited.1739006

AMA

1.Özen F, Ortaç Kabaoğlu R, Mumcu TV. Prediction of Sector and Region for Three-Level NPC Inverters Using Artificial Neural Networks. DUBİTED. 2026;14(1):60-71. doi:10.29130/dubited.1739006

Chicago

Özen, Fatih, Rana Ortaç Kabaoğlu, and Tarık Veli Mumcu. 2026. “Prediction of Sector and Region for Three-Level NPC Inverters Using Artificial Neural Networks”. Duzce University Journal of Science and Technology 14 (1): 60-71. https://doi.org/10.29130/dubited.1739006.

EndNote

Özen F, Ortaç Kabaoğlu R, Mumcu TV (January 1, 2026) Prediction of Sector and Region for Three-Level NPC Inverters Using Artificial Neural Networks. Duzce University Journal of Science and Technology 14 1 60–71.

IEEE

[1]F. Özen, R. Ortaç Kabaoğlu, and T. V. Mumcu, “Prediction of Sector and Region for Three-Level NPC Inverters Using Artificial Neural Networks”, DUBİTED, vol. 14, no. 1, pp. 60–71, Jan. 2026, doi: 10.29130/dubited.1739006.

ISNAD

Özen, Fatih - Ortaç Kabaoğlu, Rana - Mumcu, Tarık Veli. “Prediction of Sector and Region for Three-Level NPC Inverters Using Artificial Neural Networks”. Duzce University Journal of Science and Technology 14/1 (January 1, 2026): 60-71. https://doi.org/10.29130/dubited.1739006.

JAMA

1.Özen F, Ortaç Kabaoğlu R, Mumcu TV. Prediction of Sector and Region for Three-Level NPC Inverters Using Artificial Neural Networks. DUBİTED. 2026;14:60–71.

MLA

Özen, Fatih, et al. “Prediction of Sector and Region for Three-Level NPC Inverters Using Artificial Neural Networks”. Duzce University Journal of Science and Technology, vol. 14, no. 1, Jan. 2026, pp. 60-71, doi:10.29130/dubited.1739006.

Vancouver

1.Fatih Özen, Rana Ortaç Kabaoğlu, Tarık Veli Mumcu. Prediction of Sector and Region for Three-Level NPC Inverters Using Artificial Neural Networks. DUBİTED. 2026 Jan. 1;14(1):60-71. doi:10.29130/dubited.1739006

Yapay Sinir Ağları Kullanarak Üç Seviyeli NPC İnvertörler için Sektör ve Bölge Tahmini

Abstract

Keywords

Prediction of Sector and Region for Three-Level NPC Inverters Using Artificial Neural Networks

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