Wind turbine inspection with drone: Advantages and disadvantages

Year 2023, Volume: 7 Issue: 1, 57 - 66, 31.03.2023

Harun Tanrıverdi Güzide Karakuş Ahmet Ulukan

https://doi.org/10.30521/jes.1148877

Abstract

The facilities on wind energy generation are increasingly finding usage areas in line with the ecologically friendly energy generation approach. One of the important activities of wind power generation facilities, which have high investment cost, low operating cost and low environmental impact is the maintenance and repair of wind turbines. A preventive maintenance approach is dominant to reduce maintenance times and eliminate lost time in wind turbines. Damage inspection of turbines has been evolved from tower crane access, rope access, camera viewing, and other applications to image with manual drones over the years. However, when these methods are evaluated within the framework of criteria such as cost, performance, occupational safety and data reliability, they are still insufficient and the need for inspection with autonomous drones arises. The advantages and disadvantages of autonomous drones used in the determination of damage in wind turbines are analyzed and the results are considered to contribute to the practitioners operating in the sector and academicians working in the field.

Keywords

Drone, Inspection, Maintenance, Turbine, Wind energy

Supporting Institution

TÜBİTAK 1512 Individual Young Entrepreneur Program (BIGG)

Project Number

2190220

Thanks

This study was funded by the Scientific and Technological Research Council of Turkey under the name of “Wind turbine inspection with drone, detailed reporting and analysis solutions” with the project type 1512 Individual Young Entrepreneur Program (BIGG).

References

• [1] Gürbüz, E. Y., Altıntaş, A., Sürücü, B., Tuncer A. D. Rüzgar Türbinlerinin Yaban Hayatına Etkilerinin İncelenmesi [Investigation of the Impacts of Wind Turbines on Wildlife]. Journal of Polytechnic 2021; 24(3): 953-962. DOI: 10.2339/politeknik.741965.
• [2] Yavuz, İ. & Özbay, H. Rüzgar Türbinlerinde Kurulum ve Bakım Süreçleri: Bandırma Örneği [Installation and Maintenance Processes in Wind Turbines: The Case of Bandırma]. Mühendislik Bilimleri ve Araştırmaları Dergisi 2020; 2(2): 58-68.
• [3] Doğan, Z., Kurt Şen M., Emeksiz, C. Çift Beslemeli Asenkron Generatörlü Rüzgar Türbinlerinde Arıza Teşhisi [Fault Detection of Wind Turbines with Double-Fed Asynchronous Generator]. In: 3. Anadolu Enerji Sempozyumu; 1-3 Ekim 2015: Muğla, Türkiye, pp. 361-370.
• [4] Komusanac, I., Brindley, G., Fraile, D., Ramirez, L. Wind Energy in Europe – Statistics and the Outlook for 2022-2026”. WindEurope, 2022.
• [5] Durmuş Z., Aslan S.N., Esen, V. Türkiye’de Rüzgar Enerjisinin Mevcut Durumu ve Geleceği [Current Situation and Future of Wind Energy in Turkey]. In: 2nd International Graduate Studies Congress (IGSCONG’22): 08-11 June 2022: Türkiye, pp. 397-407)
• [6] Karık F., Sözen A., İzgeç M. M., Rüzgâr gücü tahminlerinin önemi: Türkiye Elektrik Piyasasında Bir Uygulama [The Importance of Wind Power Forecasts: A Case Study in Turkish Electricity Market], Journal of Politechnic 2017; 20(4): 851-861, 2017. DOI: 10.2339/politeknik.369038
• [7] Ata, R., The current situation of wind energy in Turkey. Journal of Energy, 2013. DOI: 10.1155/2013/794095.
• [8] Gül, F. Yenilenebilir Enerji Kaynakları Kullanımında İş Sağlığı ve Güvenliği Uygulamalarının Araştırılması [The Research Of Using Renewable Energy Resources Occupational Health And Safety Investigation Of Applications], MSc, Necmettin Erbakan Üniversitesi, Konya, Türkiye, 2018.
• [9] Kulsinskas, A., Durdevic, P., Ortiz-Arroyo, D. Internal Wind Turbine Blade Inspections Using UAVs. Analysis and Design Issues. Energies 2021, 14, 294. https://doi.org/10.3390/en14020294
• [10] Asian, S., Ertek, G., Haksoz, C., Pakter, S., Ulun, S., Wind turbine accidents: A datamining study. IEEE Systems Journal (2017); 11(3), 1567-1578. DOI: 10.1109/JSYST.2016.2565818.
• [11] Garcia, D. A., Bruschi, D. A risk assessment tool for improving safety standards and emergency management in Italian onshore wind farms. Sustainable Energy Technologies and Assessments 2016; 18, 48-58.
• [12] Morgenthal, G., Hallermann, N. Quality Assessment of Unmanned Aerial Vehicle (UAV) Based Visual Inspection of Structures. Advances in Structural Engineering 2014; 17(3): 289-302.
• [13] Yılmaz, S., Bakım Uygulamalarında Robotların Yardımcı Ekipman Olarak Kullanılması [Using Robots as Auxiliary Equipment in Maintenance Applications]. Mühendis ve Makina 2020; 61(699): 132-143.
• [14] Khadka, A., Afshar, A., Zadeh, M. & Baqersad, J. Strain monitoring of wind turbines using a semi-autonomous drone. Wind Engineering 2021; 46(1): 296-307.
• [15] Shihavuddin, A. S. M., Chen, X., Fedorov, V., Christensen, A. N., Riis, N. A. B., Branner, K., Dahl, A.B. & Paulsen, R. R. Wind turbine maintenance cost reduction by deep learning aided drone inspection analysis. Peer-reviewed version available at Energies 2019; 12: 676. DOI: 10.20944/preprints201901.0281.v1.
• [16] Martinez, C., Asare Yeboah, F., Herford, S., Brzezinski, M., Puttagunta, V. Predicting wind turbine blade erosion using machine learning. SMU Data Science Review 2019; 2(2): 17.
• [17] Reddy, A., Indragandhi, V., Ravi, L., Subramaniyaswamy, V. Detection of Cracks and damage in wind turbine blades using artificial intelligence-based image analytics. Measurement 2019; 147: 106823.
• [18] Shihavuddin, A.S.M, Chen, X., Fedorov, V., Christensen, A.N., Riis, N.A.B., Branner, K., Dahl, A.B., Paulsen, R.R. Wind turbine surface damage detection by deep learning aided drone inspection analysis. Energies 2019; 12(4): 676.
• [19] Öztürk, H.K. Rüzgar Türbinlerinde İşletme ve Bakım [Operation and Maintenance for Wind Turbines]. Mühendis ve Makina 2020; 61(701): 262-279.
• [20] Wang, Y., Yoshihashi, R., Kawakami, R., You, S, Harano, T., Ito, M., Komagome, K., Iida, M., Naemura, T. Unsupervised anomaly detection with compact deep features for wind turbine blade images taken by a drone. IPSJ Transactions on Computer Vision and Applications 2019; 11(1): 1-7.
• [21] Kaycı B., Demir B. E., Demir F. Deep learning based fault detection and diagnosis in photovoltaic system using thermal images acquired by UAV. Journal of Polytechnic 2022; 1(1): DOI: 10.2339/politeknik.1094586.