AI-Based Fault Detection and Predictive Maintenance in Wind Power Conversion Systems

AI-Based Fault Detection and Predictive Maintenance in Wind Power Conversion Systems D.B. Hulwan Chitra S Arun Chokkalingan Krishna Kant Dixit S. Subburam Vandana Katarwar S. Tharmar K.P. Navven J. Guru Jawahar K. Ramya B. Pragathi

The research explores the application of Artificial Intelligence (AI) for fault detection and predictive maintenance in wind power conversion systems. Wind energy, a critical component of the global renewable energy mix, faces challenges related to system reliability and maintenance. Traditional methods for detecting faults and scheduling maintenance are often reactive and inefficient, leading to higher costs and downtime. This study proposes an AI-based approach to improve fault detection accuracy and predict potential failures before they occur. By analysing operational data from wind turbines, AI models can identify patterns indicative of faults and provide early warnings, allowing for timely maintenance. The research demonstrates that AI can significantly enhance the reliability and efficiency of wind power systems, reducing operational costs and improving energy production. The findings suggest that AI-based predictive maintenance can play a crucial role in advancing the sustainability of wind energy.
11 14 2024 2024 02003 e3sconf_icrera2024_02003 https://creativecommons.org/licenses/by/4.0/ 10.1051/e3sconf/202459102003 https://www.e3s-conferences.org/10.1051/e3sconf/202459102003 https://www.e3s-conferences.org/10.1051/e3sconf/202459102003/pdf Kokila M. L. S., Christopher V. B., and Ramya G., “Enhanced power system fault detection using quantum‐AI and herd immunity quantum‐AI fault detection with herd immunity optimisation in power systems,” IET Quantum Communication, Jul. 2024, International Journal for Research in Applied Science and Engineering Technology Nadaf 12 6 2013 2024 10.22214/ijraset.2024.63442 10.1109/FASTA61401.2024.10595140 Lin W., Dou J., Yuan L., and Zhang K., “Fault detection based on operational state recognition for wind turbine gearbox,” May 2024, 10.1109/INFOTEH60418.2024.10496038 Hubana T. and Hodzic M., “Artificial Intelligence Based Fault Detection and Classification in Power Systems: An Automated Machine Learning Approach,” Mar. 2024, IEEE Transactions on Industry Applications Zhang 60 2 3492 2024 10.1109/TIA.2023.3341059 Energy Reports Attouri 10 3113 2023 10.1016/j.egyr.2023.09.163 Energy Reports Dhibi 9 5362 2023 10.1016/j.egyr.2023.04.373 10.61173/cgtf5f77 Xia Y., “Leveraging AI Technology for Advancements in Wind Power,” Science and Technology of Engineering Chemistry and Environmental Protection, vol. 1, no. 4, Jan. 2024. 10.4108/eai.1-7-2020.165996 Manoj V., Sravani V., and Swathi A., “A Multi Criteria Decision Making Approach for the Selection of Optimum Location for Wind Power Project in India,” ICST Transactions on Energy Web, p. 165996, Jul. 2018. Mechanical Systems and Signal Processing Qiao 200 110528 2023 10.1016/j.ymssp.2023.110528 IEEE Transactions on Industry Applications Chang 60 4 5269 2024 10.1109/TIA.2024.3379319 Ecological Engineering & Environmental Technology Manoj 23 5 205 2022 10.12912/27197050/152130 Renewable Energy Hang 224 120152 2024 10.1016/j.renene.2024.120152 International Journal of Intelligent Information and Database Systems Pragathi 13.2-4 491 2020 10.1504/IJIIDS.2020.109471 10.1186/s44147-023-00227-3 Attouri K., Dhibi K., Mansouri M., Hajji M., Bouzrara K., and Nounou H., “Enhanced fault diagnosis of wind energy conversion systems using ensemble learning based on sine cosine algorithm,” Journal of Engineering and Applied Science, vol. 70, no. 1, Jun. 2023. IOP Conference Series. Materials Science and Engineering Manoj 1033 1 012035 2021 10.1088/1757-899X/1033/1/012035 Technium Romanian Journal of Applied Sciences and Technology Branco 14 120 2023 10.47577/technium.v14i.9691 International Journal of Green Energy Khan 21 4 771 2023 10.1080/15435075.2023.2217901 International Journal of Power and Energy Conversion Pragathi 9.2 187 2018 10.1504/IJPEC.2018.090684 10.1109/ICETEEEM.2012.6494513 Dinesh L., Sesham H., and Manoj V., “Simulation of D-Statcom with hysteresis current controller for harmonic reduction,” Dec. 2012. 10.11591/ijpeds.v14.i4.pp2388-2397 Touioui H., Ganouche A., Ahmida Z., Bouzekri H., and Tachi F., “Real-time current sensor fault detection and localization in DFIG wind turbine systems,” International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering, vol. 14, no. 4, p. 2388, Dec. 2023. 10.1051/e3sconf/202454010014 Pragathi B., and Ramu P.. “Authentication Technique for Safeguarding Privacy in Smart Grid Settings.” E3S Web of Conferences. Vol. 540. EDP Sciences, 2024. 10.1109/RDCAPE.2017.8358316 Hemanth kumar B. and Lokhande Makarand. M, “Analysis of PWM techniques on Multilevel Cascaded H-Bridge Three Phase Inverter,” 2nd International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE), Noida, India, pp. 465-470, 26th to 27th Oct. 2017.