Adaptive Neuro-Fuzzy System for Detection of Wind Turbine Blade Defects

Lesia Dubchak (), Anatoliy Sachenko, Yevgeniy Bodyanskiy, Carsten Wolff, Nadiia Vasylkiv, Ruslan Brukhanskyi and Volodymyr Kochan
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Lesia Dubchak: Faculty of Computer Information Technologies, West Ukrainian National University, 46001 Ternopil, Ukraine
Anatoliy Sachenko: Faculty of Computer Information Technologies, West Ukrainian National University, 46001 Ternopil, Ukraine
Yevgeniy Bodyanskiy: Faculty of Computer Science, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine
Carsten Wolff: Faculty of Computer Science, Dortmund University of Applied Science and Arts, 44139 Dortmund, Germany
Nadiia Vasylkiv: Faculty of Computer Information Technologies, West Ukrainian National University, 46001 Ternopil, Ukraine
Ruslan Brukhanskyi: Education and Research Institute of Innovation, Nature Management and Infrastructure, West Ukrainian National University, 46001 Ternopil, Ukraine
Volodymyr Kochan: Faculty of Computer Information Technologies, West Ukrainian National University, 46001 Ternopil, Ukraine

Energies, 2024, vol. 17, issue 24, 1-20

Abstract: Wind turbines are the most frequently used objects of renewable energy today. However, issues that arise during their operation can greatly affect their effectiveness. Blade erosion, cracks, and other defects can slash turbine performance while also forcing maintenance costs to soar. Modern defect detection applications have significant computing resources needed for training and insufficient accuracy. The goal of this study is to develop the improved adaptive neuro-fuzzy inference system (ANFIS) for wind turbine defect detection, which will reduce computing resources and increase its accuracy. Unmanned aerial vehicles are deployed to photograph the turbines, and these images are beamed back and processed for early defect detection. The proposed adaptive neuro-fuzzy inference system processes the data vectors with lower complexity and higher accuracy. For this purpose, the authors explored grid partitioning and subtractive clustering methods and selected the last one because it uses three rules only for fault detection, ensuring low computational costs and enabling the discovery of wind turbine defects quickly and efficiently. Moreover, the proposed ANFIS is implemented in a controller, which has an accuracy of 91%, that is 1.4 higher than the accuracy of the existing similar controller.

Keywords: wind turbine; adaptive neuro-fuzzy inference system; defect detection; subtractive clustering; grid partitioning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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