Due to the rapid depletion of fossil fuel reserves and the intensification of problems associated with global warming, humanity is increasingly focusing on renewable energy sources. They have become not only an essential component of modern energy systems but also a foundation for the sustainable development of the future. Wind energy, as one of the most accessible sources of renewable energy, is attracting growing interest from both governments of developed countries and private investors. Modern wind turbines are becoming progressively larger, and their blades, which are key components for energy generation, are continually subjected to damage caused by the aggressive influence of the external environment and cyclic (i.e., time-varying) loads. Consequently, the issues of reliability and safety are paramount for ensuring the uninterrupted supply of electricity to both the population and businesses. To effectively monitor the condition of wind turbines, and particularly their blades, it is necessary to employ information technologies that enable precise and timely detection of potential failures and optimize maintenance processes [1]. The paper analyses and synthesizes a classification of general methods and tools for determining the remaining service life of components subjected to cyclic loads. The concept of a model for predicting the residual service life of the wind turbine blade root is described in detail. Test input data were generated using modern professional software packages for wind turbine dynamics modelling such as OpenFAST and TurbSim. The detailed description of the proposed solution architecture and a flowchart of the developed method algorithm for fatigue assessment of the cross-sectional sectors of the 5 MW wind turbine blade root are provided. The algorithm employs the rainflow counting method and the Palmgren – Miner hypothesis of linear damage accumulation. Based on these foundations software module was developed to implement the proposed model and the obtained results make it possible, at a first approximation, allow predicting the fatigue life of the root of a wind turbine blade after simulated dynamic loads.
[1] Basalkevych, O. A., Rudavskyy, D. V. (2023). The modern state of approaches to monitoring the technical condition of wind turbine blades using information technologies. Ukrainian Journal of Information Technologies, 2023, 5(2), 79–87 [in Ukrainian].
[2] Wang, W., Xue, Y., He, C., Zhao, Y. (2022). Review of the Typical Damage and Damage-Detection Methods of Large Wind Turbine Blades. Energies, 2022, 15(15), 56–72. https://doi.org/10.3390/en15155672
[3] Rudavskyi, D. V. (2011). Residual resource of metal structural elements in Hydrogen-containing environments. Kyiv: Naukova Dumka [in Ukrainian].
[4] Ataya, S.; Ahmed, M.M. (2013). Damages of wind turbine blade trailing edge: Forms, location, and root causes. Engineering Failure Analysis 2013, 35, 480–488. https://doi.org/10.1016/j.engfailanal.2013.05.011
[5] Lee, Y., Barkley. M., Kang H.-T. (2012). Metal Fatigue Analysis Handbook. Practical problem-solving techniquesfor computer-aided engineering: Chapter 3 – Rainflow Cycle Counting Techniques, pp. 89–114. https://doi.org/10.1016/ B978-0-12-385204-5.00003-3
[6] Lee, Y., Barkley. M., Kang H.-T. (2012). Metal Fatigue Analysis Handbook. Practical problem-solving techniquesfor computer-aided engineering: Chapter 9 – Vibration Fatigue Testing and Analysis, pp. 333–382. https://doi.org/10.1016/ B978-0-12-385204-5.00003-3
[7] Matsunaga, H. (2021). Essential Structure of S-N curve: Prediction of Fatigue Life and Fatigue Limit of Defective Materials and Nature of Scatter. International Journal of Fatigue, 2021, 146(5):106–138. https://doi.org/10.1016/ j.ijfatigue.2020.106138
[8] Zienkiewicz, O. C., Taylor, R. L., Zhu,J.Z. (2013). The Finite Element Method: Its Basis and Fundamentals. Seventh Edition, 2013. https://doi.org/10.1016/C2009-0-24909-9
[9] Chandrasekhar, K., Stevanovic, N., Cross, E. J., Dervilis, N., Worden, K. (2021). Damage detection in operational wind turbine blades using a new approach based on machine learning. Renewable energy, 168, 1249–1264. https://doi.org/10.1016/j.renene.2020.12.119
[10] Sirigu, M., Faraggiana, E, Ghigo, A., Petracca, E., Matti- azzo, G., Bracco G. (2022). Development of a simplified blade root fatigue analysis for floating offshore wind turbines. Trends in Renewable Energies Offshore, 2022, 935–941. https://doi.org/10.1201/9781003360773-103
[11] Majewski, P., Florin, N., Jit, J., Stewart, R. A. (2022). End- of-life policy considerations for wind turbine blades. Renewable and Sustainable Energy Reviews, 2022, 164. https://doi.org/10.1016/j.rser.2022.112538
[12] New Zealand Wind Energy Association. Wind Energy Association. Retrieved November 24, 2024, from https://www.windenergy.org.nz/
[13] Meteoblue. Simulated historical climate & weather data for Wellington. Retrieved November 24, 2024, from https://www.meteoblue.com/en/weather/historyclimate/clim atemodelled/wellington_new-zealand_2179537
[14] Structural Basics. Polar moment of inertia formulas. Retrieved November 24, 2024, from https://www. structuralbasics.com/polar-moment-of-inertia-formulas
[15] Chrétien, A., Tahan, A., Pelletier, F. (2024). Wind Turbine Blade Damage Evaluation under Multiple Operating Conditions and Based on 10-Min SCADA Data. Energies, 2024, 17(5), 1202. https://doi.org/10.3390/en17051202