EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 564 (2024) E3S Web Conf., 564 (2024) 03001 References
Open Access
Issue
E3S Web Conf.
Volume 564, 2024
International Conference on Power Generation and Renewable Energy Sources (ICPGRES-2024)
Article Number 03001
Number of page(s) 8
Section Power Converters
DOI https://doi.org/10.1051/e3sconf/202456403001
Published online 06 September 2024
  1. H. Xu, Y. Peng, and L. Su, “Research on Open Circuit Fault Diagnosis of Inverter Circuit Switching tube Based on Machine Learning Algorithm,” IOP Conf.Ser. Mater. Sci. Eng., vol. 452, p. 042015, (Dec. 2018), doi: 10.1088/1757-899X/452/4/042015. [CrossRef] [Google Scholar]
  2. N. Rajeswaran, M. Lakshmi Swarupa, T. Sanjeeva Rao, and K. Chetaswi, “Hybrid Artificial Intelligence based Fault Diagnosis of SVPWM Voltage Source Inverters for Induction Motor,” Mater. Today Proc., vol. 5, no. 1, pp. 565–571, (2018), doi: 10.1016/j.matpr.2017.11.119. [CrossRef] [Google Scholar]
  3. Bhatt, Ankit, Weerakorn Ongsakul, and Jayant Pawar. “Optimal energy management system for carbon–neutral microgrid integrating second-life batteries and crypto mining devices.” Sustainable Energy Technologies and Assessments 64 (2024): 103686. [CrossRef] [Google Scholar]
  4. V. Sridhar and S. Umashankar, “A comprehensive review on CHB MLI based PV inverter and feasibility study of CHB MLI based PV-STATCOM,” Renew. Sustain. Energy Rev., vol. 78, pp. 138–156, (Oct. 2017), doi: 10.1016/j.rser.2017.04.111. [CrossRef] [Google Scholar]
  5. Mohamed. A. Zeddini, R. Pusca, A. Sakly, and M. F. Mimouni, “PSO-based MPPT control of wind-driven Self-Excited Induction Generator for pumping system,” Renew. Energy, vol. 95, pp. 162–177, (Sep. 2016), doi: 10.1016/j.renene.2016.04.008. [CrossRef] [Google Scholar]
  6. M. Shayestegan et al., “An overview on prospects of new generation single-phase transformerless inverters for grid-connected photovoltaic (PV) systems,” Renew. Sustain. Energy Rev., vol. 82, pp. 515–530, (Feb. 2018), doi: 10.1016/j.rser.2017.09.055. [CrossRef] [Google Scholar]
  7. E. Kabalci, Y. Kabalci, R. Canbaz, and G. Gokkus, “Single phase multilevel string inverter for solar applications,” in 2015 International Conference on Renewable Energy Research and Applications (ICRERA), Palermo: IEEE, (Nov. 2015), pp. 109–114. doi: 10.1109/ICRERA.2015.7418459. [Google Scholar]
  8. E. Kabalci and Y. Kabalci, “A wireless metering and monitoring system for solar string inverters,” Int. J. Electr. Power Energy Syst., vol. 96, pp. 282–295, (Mar. 2018), doi: 10.1016/j.ijepes.2017.10.013. [CrossRef] [Google Scholar]
  9. K. Zeb et al., “A comprehensive review on inverter topologies and control strategies for grid connected photovoltaic system,” Renew. Sustain. Energy Rev., vol. 94, pp. 1120–1141, (Oct. 2018), doi: 10.1016/j.rser.2018.06.053. [CrossRef] [Google Scholar]
  10. Y. P. Siwakoti and F. Blaabjerg, “Common-Ground-Type Transformerless Inverters for Single-Phase Solar Photovoltaic Systems,” IEEE Trans. Ind. Electron., vol. 65, no. 3, pp. 2100–2111, (Mar. 2018), doi: 10.1109/TIE.2017.2740821. [CrossRef] [Google Scholar]
  11. R. Hasan, S. Mekhilef, M. Seyedmahmoudian, and B. Horan, “Grid-connected isolated PV microinverters: A review,” Renew. Sustain. Energy Rev., vol. 67, pp. 1065–1080, (Jan. 2017), doi: 10.1016/j.rser.2016.09.082. [CrossRef] [Google Scholar]
  12. H. Zheng, J. Yuan, and L. Chen, “Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation,” Energies, vol. 10, no. 8, p. 1168, (Aug. 2017), doi: 10.3390/en10081168. [CrossRef] [Google Scholar]
  13. Y. Q. Chen, O. Fink, and G. Sansavini, “Combined Fault Location and Classification for Power Transmission Lines Fault Diagnosis With Integrated Feature Extraction,” IEEE Trans. Ind. Electron., vol. 65, no. 1, pp. 561–569, (Jan. 2018), doi: 10.1109/TIE.2017.2721922. [CrossRef] [Google Scholar]
  14. R. Guerreiro Carvalho Cunha, E. T. da Silva, and C. Marques de Sá Medeiros, “Machine learning and multiresolution decomposition for embedded applications to detect short-circuit in induction motors,” Comput. Ind., vol. 129, p. 103461, (Aug. 2021), doi: 10.1016/j.compind.2021.103461. [CrossRef] [Google Scholar]
  15. C. Xiao, Z. Liu, T. Zhang, and X. Zhang, “Deep Learning Method for Fault Detection of Wind Turbine Converter,” Appl. Sci., vol. 11, no. 3, p. 1280, (Jan. 2021), doi: 10.3390/app11031280. [CrossRef] [Google Scholar]
  16. Zheng, Wang, Jiang, and He, “A Novel Machine Learning-Based Short-Circuit Current Prediction Method for Active Distribution Networks,” Energies, vol. 12, no. 19, p. 3793, (Oct. 2019), doi: 10.3390/en12193793. [CrossRef] [Google Scholar]
  17. A. Malik, A. Haque, and K. V. S. Bharath, “Deep Learning Based Fault Diagnostic Technique for Grid Connected Inverter,” in 2021 IEEE 12th Energy Conversion Congress & Exposition - Asia (ECCE-Asia), Singapore, Singapore: IEEE,(May 2021), pp. 1390–1395. doi: 10.1109/ECCE-Asia49820.2021.9479371. [Google Scholar]
  18. H. Behrends, D. Millinger, W. Weihs-Sedivy, A. Javornik, G. Roolfs, and S. Geißendörfer, “Analysis of Residual Current Flows in Inverter Based Energy Systems Using Machine Learning Approaches,” Energies, vol. 15, no. 2, p. 582, (Jan. 2022), doi: 10.3390/en15020582. [CrossRef] [Google Scholar]
  19. B. D. E. Cherif, M. Chouai, S. Seninete, and A. Bendiabdellah, “Machine-Learning-Based Diagnosis of an Inverter-Fed Induction Motor,” IEEE Lat. Am. Trans., vol. 20, no. 6, pp. 901–911, (Jun. 2022), doi: 10.1109/TLA.2022.9757372. [CrossRef] [Google Scholar]
  20. M. Skowron, T. Orlowska-Kowalska, M. Wolkiewicz, and C. T. Kowalski, “Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor,” Energies, vol. 13, no. 6, p. 1475, (Mar. 2020), doi: 10.3390/en13061475. [CrossRef] [Google Scholar]
  21. M.-F. Guo, N.-C. Yang, and W.-F. Chen, “Deep-Learning-Based Fault Classification Using Hilbert–Huang Transform and Convolutional Neural Network in Power Distribution Systems,” IEEE Sens. J., vol. 19, no. 16, pp. 6905–6913, (Aug. 2019), doi: 10.1109/JSEN.2019.2913006. [CrossRef] [Google Scholar]
  22. K. Khalil, O. Eldash, A. Kumar, and M. Bayoumi, “Machine Learning-Based Approach for Hardware Faults Prediction,” IEEE Trans. Circuits Syst. Regul. Pap., vol. 67, no. 11, pp. 3880–3892, (Nov. 2020), doi: 10.1109/TCSI.2020.3010743. [CrossRef] [Google Scholar]
  23. R. Guerreiro Carvalho Cunha, E. T. da Silva, and C. Marques de Sá Medeiros, “Machine learning and multiresolution decomposition for embedded applications to detect short-circuit in induction motors,” Comput. Ind., vol. 129, p. 103461, (Aug. 2021), doi: 10.1016/j.compind.2021.103461. [CrossRef] [Google Scholar]
  24. “Karpiński et al. - 2022 - Diagnostics of Articular Cartilage Damage Based on.pdf.” [Google Scholar]
  25. “Li et al. - 2018 - Discrimination of soft tissues using laser-induced.pdf.” [Google Scholar]
  26. “Rodrigues et al. - 2019 - Classification of EEG signals to detect alcoholism.pdf.” [Google Scholar]
  27. “Khishe and Mosavi - 2020 - Chimp optimization algorithm.pdf.” [Google Scholar]
  28. B. Abdollahzadeh, F. Soleimanian Gharehchopogh, and S. Mirjalili, “Artificial gorilla troops optimizer: A new nature‐inspired metaheuristic algorithm for global optimization problems,” Int. J. Intell. Syst., vol. 36, no. 10, pp. 5887–5958, (Oct. 2021), doi: 10.1002/int.22535. [CrossRef] [Google Scholar]
  29. L. Abualigah, D. Yousri, M. Abd Elaziz, A. A. Ewees, M. A. A. Al-qaness, and A. H. Gandomi, “Aquila Optimizer: A novel meta-heuristic optimization algorithm,” Comput. Ind. Eng., vol. 157, p. 107250, (Jul. 2021), doi: 10.1016/j.cie.2021.107250. [CrossRef] [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.