EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 540 (2024) E3S Web of Conf., 540 (2024) 10020 References
Open Access
Issue
E3S Web of Conf.
Volume 540, 2024
1st International Conference on Power and Energy Systems (ICPES 2023)
Article Number 10020
Number of page(s) 8
Section Grid Connected Systems
DOI https://doi.org/10.1051/e3sconf/202454010020
Published online 21 June 2024
  1. Talaat, M., Alsayyari, A. S., Alblawi, A., & Hatata, A. Y. (2020). Hybrid-cloud-based data processing for power system monitoring in smart grids. Sustainable Cities and Society, 55, 102049 [CrossRef] [Google Scholar]
  2. Ramesh, S., Seetha, J., Ramkumar, G., Sahoo, S., Amirthalakshmi, T. M., Ranjith, A.,... & Subbiah, R. (2022). Optimization of solar hybrid power generation using conductance-fuzzy dual-mode control method. International Journal of Photoenergy, 2022. [Google Scholar]
  3. Baek, J., Vu, Q. H., Liu, J. K., Huang, X., & Xiang, Y. (2014). A secure cloud computing based framework for big data information management of smart grid. IEEE transactions on cloud computing, 3(2), 233–244. [Google Scholar]
  4. Bera, S., Misra, S., & Rodrigues, J. J. (2014). Cloud computing applications for smart grid: A survey. IEEE Transactions on Parallel and Distributed Systems, 26(5), 1477–1494. [Google Scholar]
  5. Luo, F., Zhao, J., Dong, Z. Y., Chen, Y., Xu, Y., Zhang, X., & Wong, K. P. (2015). Cloud-based information infrastructure for next-generation power grid: Conception, architecture, and applications. IEEE Transactions on Smart Grid, 7(4), 1896–1912. [Google Scholar]
  6. Naveen, P., Ing, W. K., Danquah, M. K., Sidhu, A. S., & Abu-Siada, A. (2016, March). Cloud computing for energy management in smart grid-an application survey. In IOP Conference Series: Materials Science and Engineering (Vol. 121, No. 1, p. 012010). IOP Publishing. [Google Scholar]
  7. Funabiki, N. (Ed.). (2011). Wireless mesh networks. BoD–Books on Demand. [Google Scholar]
  8. Qiu, R. C., Chen, Z., Guo, N., Song, Y., Zhang, P., Li, H., & Lai, L. (2010, June). Towards a real-time cognitive radio network testbed: architecture, hardware platform, and application to smart grid. In 2010 Fifth IEEE Workshop on Networking Technologies for Software Defined Radio Networks (SDR) (pp. 1–6). IEEE. [Google Scholar]
  9. Gungor, V. C., Sahin, D., Kocak, T., Ergut, S., Buccella, C., Cecati, C., & Hancke, G. P. (2011). Smart grid technologies: Communication technologies and standards. IEEE transactions on Industrial informatics, 7(4), 529–539. [CrossRef] [Google Scholar]
  10. Neagu, O., & Hamouda, W. (2016, April). Performance of WiMAX for smart grid applications. In 2016 International Conference on Selected Topics in Mobile & Wireless Networking (MoWNeT) (pp. 1–5). IEEE. [Google Scholar]
  11. Walid, K., Sofiane, M., & Hamza, G. (2022). Fuzzy logic control to track the maximum power for photovoltaic system. In Artificial Intelligence and Heuristics for Smart Energy Efficiency in Smart Cities: Case Study: Tipasa, Algeria (pp. 277–284). Springer International Publishing. [Google Scholar]
  12. Karanjkar, D. S., Chatterji, S., & Kumar, A. (2014). Design and implementation of a linear quadratic regulator based maximum power point tracker for solar photo-voltaic system. International Journal of Hybrid Information Technology, 7(1), 167–182. [CrossRef] [Google Scholar]
  13. Chang, E. C. (2020). Applying robust intelligent algorithm and Internet of things to global maximum power point tracking of solar photovoltaic systems. Wireless Communications and Mobile Computing, 2020, 1–10. [CrossRef] [Google Scholar]
  14. Karanjkar, D. S., Chatterji, S., Kumar, A., & Shimi, S. L. (2014). Fuzzy adaptive proportional-integral-derivative controller with dynamic set-point adjustment for maximum power point tracking in solar photovoltaic system. Systems Science & Control Engineering: An Open Access Journal, 2(1), 562–582. [CrossRef] [Google Scholar]
  15. Dhaya R., Ujwal U.J., Sharma T., Singh P., Kanthavel R., Selvan S., Krah D., (2022), “Energy-Efficient Resource Allocation and Migration in Private Cloud Data Centre”, Wireless Communications and Mobile Computing, Vol. 2022, no., pp.-. doi:10.1155/2022/3174716 [Google Scholar]
  16. Natarajan S.K., Rathinasabapathy R., Narayanasamy J., Aravind A.R., (2023), “Biometric User Authentication System via Fingerprints Using Novel Hybrid Optimization Tuned Deep Learning Strategy”, Traitement du Signal, Vol. 40, no. 1, pp.375–381.doi:10.18280/ts.400138 [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.