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All issues Volume 591 (2024) E3S Web Conf., 591 (2024) 05002 References
Open Access
Issue
E3S Web Conf.
Volume 591, 2024
International Conference on Renewable Energy Resources and Applications (ICRERA-2024)
Article Number 05002
Number of page(s) 8
Section Grid Connected Power Generation Systems with RER
DOI https://doi.org/10.1051/e3sconf/202459105002
Published online 14 November 2024
  1. Arafat, M. Y., M. J. Hossain, and Md Morshed Alam. “Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects.” Renewable and Sustainable Energy Reviews 190 (2024): 114088. [CrossRef] [Google Scholar]
  2. Pragathi, Bellamkonda, Ramesh Chandra Poonia, Bojja Polaiah, and Deepak Kumar Nayak. “Evaluation and analysis of soft computing techniques for grid-connected photo voltaic system to enhance power quality issues.” Journal of Electrical Engineering & Technology 16 (2021): 1833-1840. [CrossRef] [Google Scholar]
  3. Buvana, M, Muthumayil, K, senthil kumar subburaj., Alshamrani, S. S., Ali, I, “Deep optimal VGG16 based COVID-19 diagnosis model”, Computers, Materials and Continua,Vol-70,no 1, pp,43-58,2021 [Google Scholar]
  4. Almasoudi, Fahad M. “Enhancing power grid resilience through real-time fault detection and remediation using advanced hybrid machine learning models.” Sustainability 15, no. 10 (2023): 8348. [CrossRef] [Google Scholar]
  5. Sujith, A.V.L.N.,Swathi, R. Venkatasubramanian, R., Muhibbullah, M.,Osman, S.M., “Integrating Nanomaterial and High-Performance Fuzzy-Based Machine Learning Approach for Green Energy Conversion”, Journal of Nanomaterials,pp- 5793978,2022 [Google Scholar]
  6. Quiñones, Jhon J., Luis R. Pineda, Jason Ostanek, and Luciano Castillo. “Towards smart energy management for community microgrids: Leveraging deep learning in probabilistic forecasting of renewable energy sources.” Energy Conversion and Management 293 (2023): 117440. [CrossRef] [Google Scholar]
  7. V. Vivek, J. Hemalatha, T.P. Latchoumi, S. Mohan. TOWARDS THE DEVELOPMENT OF OBSTACLE DETECTION IN RAILWAY TRACKS USING THERMAL IMAGING. Neural Network World 5/2023, 337–355. DOI: 10.14311/NNW.2023.33.019. 2023. [Google Scholar]
  8. 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, no. 2-4 (2020): 491-506. [CrossRef] [Google Scholar]
  9. Efficiency Analysis Of Modified Sepic Converter For Renewable Energy Applications D Rao, M Jasmin, M Pandey, M Almusawi, GM Ramadan, RS Kumar E3S Web of Conferences 564, 01007 [Google Scholar]
  10. Chandrasekaran, Kumar, Prabaakaran Kandasamy, and Srividhya Ramanathan. “Deep learning and reinforcement learning approach on microgrid.” International Transactions on Electrical Energy Systems 30, no. 10 (2020): e12531. [CrossRef] [Google Scholar]
  11. Robust Control of Power System Frequency Regulation in Islanded Microgrids MS Sjeyanthi, V Ashok, AH Alkkhayat, A Rana, S Pandey, RS Kumar, ... E3S Web of Conferences 540, 10019 [Google Scholar]
  12. Legal Framework for Energy Transition: Balancing Innovation and Regulation YS Bisht, LH Alzubaidi, U Gulbakhor, S Yuvaraj, T Saravanan, RS Kumar, ... E3S Web of Conferences 540, 13015 [Google Scholar]
  13. Pandey M.; Vinay Kumar S.B.; Khurana J.; Kalpana B.; Pathak K.R.; Kumar M.,(2023), “Leveraging Neural Networks to Enhance Large-Scale Data Processing Performance”,2023 3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023,Vol.,no.,pp.-.doi:10.1109/SMARTGENCON60755.2023.10442327 [Google Scholar]
  14. Elabbassi, Ismail, Mohamed Khala, and Omar Eloutassi. “Evaluating and comparing machine learning approaches for effective decision making in renewable microgrid systems.” Results in Engineering 21 (2024): 101888. [CrossRef] [Google Scholar]
  15. Begum A.; Karthikeyan K.; Takale D.G.; Bhambu P.; Yadav D.; Das S.,(2023), “Exploring the Benefits of AI for Content Retrieval”,2023 3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023,Vol.,no.,pp.-.doi:10.1109/SMARTGENCON60755.2023.10442622 [Google Scholar]
  16. Sohal J.; Sahoo G.S.; Hr M.; Jain S.K.; Kalpana B.; Wawage P.S.,(2023), “Evaluating the Impact of Image Restoration on Digital Image Processing Using compression Technique”,2023 3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023,Vol.,no.,pp.-.doi:10.1109/SMARTGENCON60755.2023.10441803. [Google Scholar]
  17. Gowrishankar J.; Vinithra Banu T.; Parikshit N.; Singh V.; Kalra H.; Sahoo G.S.,(2023), “Investigating the Benefits of Machine Learning Algorithms for Text Classification”,2023 3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023,Vol.,no.,pp.-.doi:10.1109/SMARTGENCON60755.2023.10442114. [Google Scholar]
  18. 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]
  19. 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]
  20. Pragathi, Bellamkonda, et al. “Evaluation and analysis of soft computing techniques for grid connected photo voltaic system to enhance power quality issues.” Journal of Electrical Engineering & Technology 16 (2021): 1833-1840. [CrossRef] [Google Scholar]

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