EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 591 (2024) E3S Web Conf., 591 (2024) 02003 References
Open Access
Issue
E3S Web Conf.
Volume 591, 2024
International Conference on Renewable Energy Resources and Applications (ICRERA-2024)
Article Number 02003
Number of page(s) 11
Section Wind Power Conversion System
DOI https://doi.org/10.1051/e3sconf/202459102003
Published online 14 November 2024
  1. M. L. S. Kokila, V. B. Christopher, and G. Ramya, “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, [Google Scholar]
  2. S. Nadaf, “AI for Predictive Maintenance in Industries,” International Journal for Research in Applied Science and Engineering Technology, vol. 12, no. 6, pp. 2013–2017, Jun. 2024, [CrossRef] [Google Scholar]
  3. W. Lin, J. Dou, L. Yuan, and K. Zhang, “Fault detection based on operational state recognition for wind turbine gearbox,” May 2024, [Google Scholar]
  4. T. Hubana and M. Hodzic, “Artificial Intelligence Based Fault Detection and Classification in Power Systems: An Automated Machine Learning Approach,” Mar. 2024, [Google Scholar]
  5. H. Zhang, X. Guo, and P. Zhang, “Improved PSO-SVM-Based Fault Diagnosis Algorithm for Wind Power Converter,” IEEE Transactions on Industry Applications, vol. 60, no. 2, pp. 3492–3501, Mar. 2024 [CrossRef] [Google Scholar]
  6. K. Attouri, K. Dhibi, M. Mansouri, M. Hajji, K. Bouzrara, and M. Nounou, “Effective uncertain fault diagnosis technique for wind conversion systems using improved ensemble learning algorithm,” Energy Reports, vol. 10, pp. 3113–3124, Nov. 2023, [CrossRef] [Google Scholar]
  7. K. Dhibi, M. Mansouri, M. Hajji, K. Bouzrara, H. Nounou, and M. Nounou, “A novel hybrid methodology for fault diagnosis of wind energy conversion systems,” Energy Reports, vol. 9, pp. 5362–5371, Dec. 2023 [CrossRef] [Google Scholar]
  8. Y. Xia, “Leveraging AI Technology for Advancements in Wind Power,” Science and Technology of Engineering Chemistry and Environmental Protection, vol. 1, no. 4, Jan. 2024. [Google Scholar]
  9. V. Manoj, V. Sravani, and A. Swathi, “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. [Google Scholar]
  10. L. Qiao, Y. Zhang, and Q. Wang, “Fault detection in wind turbine generators using a meta-learning-based convolutional neural network,” Mechanical Systems and Signal Processing, vol. 200, p. 110528, Oct. 2023. [CrossRef] [Google Scholar]
  11. M. Chang, K.-H. Chen, Y.-S. Chen, C.-C. Hsu, and C.-C. Chu, “Developments of AI-Assisted Fault Detection and Failure Mode Diagnosis for Operation and Maintenance of Photovoltaic Power Stations in Taiwan,” IEEE Transactions on Industry Applications, vol. 60, no. 4, pp. 5269–5281, Jul. 2024. [CrossRef] [Google Scholar]
  12. V. Manoj, P. Rathnala, S. R. Sura, S. N. Sai, and M. V. Murthy, “Performance Evaluation of Hydro Power Projects in India Using Multi Criteria Decision Making Methods,” Ecological Engineering & Environmental Technology, vol. 23, no. 5, pp. 205–217, Sep. 2022. [CrossRef] [Google Scholar]
  13. X. Hang, X. Zhu, X. Gao, Y. Wang, and L. Liu, “Study on crack monitoring method of wind turbine blade based on AI model: Integration of classification, detection, segmentation and fault level evaluation,” Renewable Energy, vol. 224, p. 120152, Apr. 2024. [CrossRef] [Google Scholar]
  14. Pragathi, Bellamkonda, Deepak Kumar Nayak, and Ramesh Chandra Poonia. “Lorentzian adaptive filter for controlling shunt compensator to mitigate power quality problems of solar PV interconnected with grid.” International Journal of Intelligent Information and Database Systems 13.2-4 (2020): 491-506. [CrossRef] [Google Scholar]
  15. K. Attouri, K. Dhibi, M. Mansouri, M. Hajji, K. Bouzrara, and H. Nounou, “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. [CrossRef] [Google Scholar]
  16. V. Manoj, A. Swathi, and V. T. Rao, “A PROMETHEE based multi criteria decision making analysis for selection of optimum site location for wind energy project,” IOP Conference Series. Materials Science and Engineering, vol. 1033, no. 1, p. 012035, Jan. 2021. [CrossRef] [Google Scholar]
  17. C. T. N. M. Branco and J. C. Da Silva, “Modeling of fault detection and isolation in pitch control of wind energy conversion systems via digital twins,” Technium Romanian Journal of Applied Sciences and Technology, vol. 14, pp. 120–125, Oct. 2023,. [CrossRef] [Google Scholar]
  18. P. W. Khan and Y.-C. Byun, “A Review of machine learning techniques for wind turbine’s fault detection, diagnosis, and prognosis,” International Journal of Green Energy, vol. 21, no. 4, pp. 771–786, May 2023. [Google Scholar]
  19. Pragathi, Bellamkonda, et al. “Power quality improvement for grid interconnected solar PV system using neural network control algorithm.” International Journal of Power and Energy Conversion 9.2 (2018): 187-204. [CrossRef] [Google Scholar]
  20. L. Dinesh, H. Sesham, and V. Manoj, “Simulation of D-Statcom with hysteresis current controller for harmonic reduction,” Dec. 2012. [Google Scholar]
  21. H. Touioui, A. Ganouche, Z. Ahmida, H. Bouzekri, and F. Tachi, “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. [Google Scholar]
  22. Pragathi, B., and P. Ramu. “Authentication Technique for Safeguarding Privacy in Smart Grid Settings.” E3S Web of Conferences. Vol. 540. EDP Sciences, 2024. [Google Scholar]
  23. B. Hemanth kumar and Makarand. M Lokhande, “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. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.