Araştırma Makalesi
BibTex RIS Kaynak Göster

Investigation of Energy Efficiency with Artificial Intelligence Based Dynamic Blade Angle Control for Wind Turbines

Yıl 2025, Cilt: 8 Sayı: 4, 1654 - 1669, 16.09.2025
https://doi.org/10.47495/okufbed.1568705

Öz

In this study, This study examines the use of Dynamic Blade Angle Control (DBAC) to improve energy efficiency in wind turbines. DBAC adapts blade pitch based on wind speed fluctuations to optimize energy production while maintaining turbine safety. Using an artificial intelligence-based model, this study analyzes the effects of DBAC on energy efficiency. The model predicts energy production based on wind speed and blade pitch, showing that DBAC increases energy efficiency at low speeds and ensures turbine safety at high speeds. The model's performance is enhanced with feedforward control strategies and LiDAR-assisted systems. These results emphasize the importance of DBAC and AI-based control systems in enhancing energy efficiency in wind turbines

Kaynakça

  • Abouheaf M., Gueaieb W., Sharaf A. Model-free adaptive learning control scheme for wind turbines with doubly fed induction generators. IET Renewable Power Generation. 2018; 12(14): 1675-1686.
  • Bossanyi E., Leithead W., Gaunaa M. Impact of multi-axis control on wind turbine efficiency. Wind Energy. 2023; 46: 123-135.
  • Bottasso C., Wang L. Enhancing wind turbine stability with advanced pitch control algorithms. Journal of Renewable and Sustainable Energy. 2023; 15(3): 230-239.
  • David F., Steffen H. Comparative study on neural network-based and traditional pitch control. Energy Reports. 2023; 10: 127-138.
  • Fernandez-Gauna B., Fernandez-Gamiz U. Variable speed wind turbine controller adaptation by reinforcement learning. Integrated Computer-Aided Engineering. 2017; 24(1): 27-39.
  • Haizmann F., Bauer N., Brücke T. Model predictive control for blade pitch optimization. Renewable Energy Journal. 2022; 75: 258-270.
  • Harris M., Cole S., Good M. Longitudinal velocity estimation using nacelle-mounted lidar systems. Renewable Energy Journal. 2023; 35: 457-470.
  • Khaniki R., Azadi M., Faghihimani M. LiDAR-based wind prediction for feedforward pitch control. Journal of Wind Engineering. 2023; 30(2): 112-125.
  • Landaluze M., Saenz-Antoñanzas A., Ibañez E. Variable pitch control for floating wind turbines. Energies. 2023; 16: 675-685.
  • Mathur A., Kumar S., Singh V. Predictive pitch control algorithms for energy maximization. Energy Conversion and Management. 2023; 286: 117-128.
  • Mulders S.P., van der Zee K., Korterink H. Adaptive individual pitch control schemes for fatigue load reduction. Applied Sciences. 2024; 14(1): 327-335.
  • Scholbrock A., Roadman J., Schmid K. Feedforward pitch control for a 15 MW wind turbine using lidar technology. Wind Energy Science. 2023; 8: 157-168.
  • Sierra-García J.E., Santos M. Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas. Revista Iberoamericana de Automática e Informática industrial. 2021; 18(4): 327-335.
  • Simley E., Scholbrock A., Scott G. Evaluation of lidar data accuracy in wind turbine applications. Journal of Wind Engineering and Industrial Aerodynamics. 2022; 187: 130-145.
  • Smith R., Wu J. Optimal blade pitch control for vertical-axis wind turbine performance enhancement. Nature Communications. 2023; 12: 457-469.
  • Van S., Bottasso C. Gain-scheduled pitch control in fluctuating wind. Renewable Energy Science. 2022; 68: 392-405.
  • Yamaguchi S., Kato K., Hara Y. Cabin feeDBACk and lidar for hybrid pitch control. Energy Journal. 2023; 112: 204-214.
  • Yang D., Li X. Machine learning-enhanced wind speed prediction models for optimal pitch control. Energy AI. 2022; 10: 117-125.
  • Yuan Y., Li T., Wang P. Incremental feedforward collective pitch control method for wind turbines. Frontiers in Energy Research. 2023; 23: 267-278.
  • Zhang Z., Jin H. Adaptive enhancement of acoustic resolution photoacoustic microscopy imaging via deep CNN prior. Photoacoustics. 2023; 30: 100484.

Rüzgâr Türbinleri İçin Yapay Zekâ Tabanlı Dinamik Kanat Açısı Kontrolü ile Enerji Verimliliğinin Araştırılması

Yıl 2025, Cilt: 8 Sayı: 4, 1654 - 1669, 16.09.2025
https://doi.org/10.47495/okufbed.1568705

Öz

Bu çalışmada, rüzgar türbinlerinde enerji verimliliğini artırmak için Dinamik Kanat Açısı Kontrolü (DKAK) yöntemini incelemektedir. Rüzgar hızındaki değişimlere göre kanat açısının uyarlanmasını sağlayan DKAK, türbin güvenliğini korurken enerji üretimini optimize etmeyi amaçlamaktadır. Çalışmada, yapay zeka destekli bir model kullanılarak DKAK’nin enerji verimliliği üzerindeki etkileri analiz edilmiştir. Rüzgar hızı ve kanat açısına bağlı olarak enerji üretim tahminleri yapan model, düşük hızlarda DKAK'nin enerji verimliliğini artırdığını, yüksek hızlarda ise türbin güvenliğini sağladığını göstermektedir. Modelin performansı, ileri beslemeli kontrol stratejileri ve LiDAR destekli sistemler ile güçlendirilmiştir. Bu sonuçlar, rüzgar türbinlerinde enerji verimliliğini artırmak için DKAK ve yapay zeka tabanlı kontrol sistemlerinin önemini vurgulamaktadır.

Kaynakça

  • Abouheaf M., Gueaieb W., Sharaf A. Model-free adaptive learning control scheme for wind turbines with doubly fed induction generators. IET Renewable Power Generation. 2018; 12(14): 1675-1686.
  • Bossanyi E., Leithead W., Gaunaa M. Impact of multi-axis control on wind turbine efficiency. Wind Energy. 2023; 46: 123-135.
  • Bottasso C., Wang L. Enhancing wind turbine stability with advanced pitch control algorithms. Journal of Renewable and Sustainable Energy. 2023; 15(3): 230-239.
  • David F., Steffen H. Comparative study on neural network-based and traditional pitch control. Energy Reports. 2023; 10: 127-138.
  • Fernandez-Gauna B., Fernandez-Gamiz U. Variable speed wind turbine controller adaptation by reinforcement learning. Integrated Computer-Aided Engineering. 2017; 24(1): 27-39.
  • Haizmann F., Bauer N., Brücke T. Model predictive control for blade pitch optimization. Renewable Energy Journal. 2022; 75: 258-270.
  • Harris M., Cole S., Good M. Longitudinal velocity estimation using nacelle-mounted lidar systems. Renewable Energy Journal. 2023; 35: 457-470.
  • Khaniki R., Azadi M., Faghihimani M. LiDAR-based wind prediction for feedforward pitch control. Journal of Wind Engineering. 2023; 30(2): 112-125.
  • Landaluze M., Saenz-Antoñanzas A., Ibañez E. Variable pitch control for floating wind turbines. Energies. 2023; 16: 675-685.
  • Mathur A., Kumar S., Singh V. Predictive pitch control algorithms for energy maximization. Energy Conversion and Management. 2023; 286: 117-128.
  • Mulders S.P., van der Zee K., Korterink H. Adaptive individual pitch control schemes for fatigue load reduction. Applied Sciences. 2024; 14(1): 327-335.
  • Scholbrock A., Roadman J., Schmid K. Feedforward pitch control for a 15 MW wind turbine using lidar technology. Wind Energy Science. 2023; 8: 157-168.
  • Sierra-García J.E., Santos M. Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas. Revista Iberoamericana de Automática e Informática industrial. 2021; 18(4): 327-335.
  • Simley E., Scholbrock A., Scott G. Evaluation of lidar data accuracy in wind turbine applications. Journal of Wind Engineering and Industrial Aerodynamics. 2022; 187: 130-145.
  • Smith R., Wu J. Optimal blade pitch control for vertical-axis wind turbine performance enhancement. Nature Communications. 2023; 12: 457-469.
  • Van S., Bottasso C. Gain-scheduled pitch control in fluctuating wind. Renewable Energy Science. 2022; 68: 392-405.
  • Yamaguchi S., Kato K., Hara Y. Cabin feeDBACk and lidar for hybrid pitch control. Energy Journal. 2023; 112: 204-214.
  • Yang D., Li X. Machine learning-enhanced wind speed prediction models for optimal pitch control. Energy AI. 2022; 10: 117-125.
  • Yuan Y., Li T., Wang P. Incremental feedforward collective pitch control method for wind turbines. Frontiers in Energy Research. 2023; 23: 267-278.
  • Zhang Z., Jin H. Adaptive enhancement of acoustic resolution photoacoustic microscopy imaging via deep CNN prior. Photoacoustics. 2023; 30: 100484.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Rüzgar Enerjisi Sistemleri
Bölüm Araştırma Makaleleri (RESEARCH ARTICLES)
Yazarlar

Ahmet Yönetken 0000-0003-1844-7233

İdris Kosova 0009-0007-0090-9360

Yayımlanma Tarihi 16 Eylül 2025
Gönderilme Tarihi 16 Ekim 2024
Kabul Tarihi 6 Nisan 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 8 Sayı: 4

Kaynak Göster

APA Yönetken, A., & Kosova, İ. (2025). Investigation of Energy Efficiency with Artificial Intelligence Based Dynamic Blade Angle Control for Wind Turbines. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 8(4), 1654-1669. https://doi.org/10.47495/okufbed.1568705
AMA Yönetken A, Kosova İ. Investigation of Energy Efficiency with Artificial Intelligence Based Dynamic Blade Angle Control for Wind Turbines. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi. Eylül 2025;8(4):1654-1669. doi:10.47495/okufbed.1568705
Chicago Yönetken, Ahmet, ve İdris Kosova. “Investigation of Energy Efficiency with Artificial Intelligence Based Dynamic Blade Angle Control for Wind Turbines”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 8, sy. 4 (Eylül 2025): 1654-69. https://doi.org/10.47495/okufbed.1568705.
EndNote Yönetken A, Kosova İ (01 Eylül 2025) Investigation of Energy Efficiency with Artificial Intelligence Based Dynamic Blade Angle Control for Wind Turbines. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 8 4 1654–1669.
IEEE A. Yönetken ve İ. Kosova, “Investigation of Energy Efficiency with Artificial Intelligence Based Dynamic Blade Angle Control for Wind Turbines”, Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 8, sy. 4, ss. 1654–1669, 2025, doi: 10.47495/okufbed.1568705.
ISNAD Yönetken, Ahmet - Kosova, İdris. “Investigation of Energy Efficiency with Artificial Intelligence Based Dynamic Blade Angle Control for Wind Turbines”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 8/4 (Eylül2025), 1654-1669. https://doi.org/10.47495/okufbed.1568705.
JAMA Yönetken A, Kosova İ. Investigation of Energy Efficiency with Artificial Intelligence Based Dynamic Blade Angle Control for Wind Turbines. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2025;8:1654–1669.
MLA Yönetken, Ahmet ve İdris Kosova. “Investigation of Energy Efficiency with Artificial Intelligence Based Dynamic Blade Angle Control for Wind Turbines”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 8, sy. 4, 2025, ss. 1654-69, doi:10.47495/okufbed.1568705.
Vancouver Yönetken A, Kosova İ. Investigation of Energy Efficiency with Artificial Intelligence Based Dynamic Blade Angle Control for Wind Turbines. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2025;8(4):1654-69.

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