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

Ahmet Yönetken; İdris Kosova

Article Details

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

Authors:

Ahmet Yönetken | İdris Kosova

Publisher:

Osmaniye Korkut Ata Üniversitesi

Main Topics:

Rüzgar Enerjisi Sistemleri

Journal:

Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.8 Issue 4, pp.1654-1669

Document Type:

Article

Publication Date:

9/2025

Document Language:

English

Document ID (Org No.):

2001354221

pISSN:

2687-3729

Keywords:

Abstract:

 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 models 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.

 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 models 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.

 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 models 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.

 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 DKAKnin enerji verimliliği üzerindeki etkileri analiz edilmiştir. Rüzgar hızı ve kanat açısına bağlı olarak enerji üretim tahminleri yapan model, düşük hızlarda DKAKnin 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.

Article Details

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

Authors:

Ahmet Yönetken | İdris Kosova

Publisher:

Osmaniye Korkut Ata Üniversitesi

Main Topics:

Rüzgar Enerjisi Sistemleri

Journal:

Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.8 Issue 4, pp.1654-1669

Document Type:

Article

Publication Date:

9/2025

Document Language:

English

Document ID (Org No.):

2001354221

pISSN:

2687-3729

Dynamic Blade Angle Control

Wind Turbines

Energy Efficiency

Artificial Intelligence

Machine Learning

LiDARDinamik Kanat Açısı Kontrolü

Rüzgar Türbinleri

Enerji Verimliliği

Yapay Zeka

Makine Öğrenmesi

LiDAR

Issues

Article Details

Authors: Ahmet Yönetken | İdris Kosova

Publisher: Osmaniye Korkut Ata Üniversitesi

Main Topics: Rüzgar Enerjisi Sistemleri

Journal: Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.8 Issue 4, pp.1654-1669

Document Type: Article

Publication Date: 9/2025

Document Language: English

Document ID (Org No.): 2001354221

pISSN: 2687-3729

Issues

Authors:

Ahmet Yönetken | İdris Kosova

Publisher:

Osmaniye Korkut Ata Üniversitesi

Main Topics:

Rüzgar Enerjisi Sistemleri

Journal:

Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.8 Issue 4, pp.1654-1669

Document Type:

Article

Publication Date:

9/2025

Document Language:

English

Document ID (Org No.):

2001354221

pISSN:

2687-3729