Open Access
Issue |
E3S Web Conf.
Volume 591, 2024
International Conference on Renewable Energy Resources and Applications (ICRERA-2024)
|
|
---|---|---|
Article Number | 01005 | |
Number of page(s) | 10 | |
Section | Battery Management System and Power Quality | |
DOI | https://doi.org/10.1051/e3sconf/202459101005 | |
Published online | 14 November 2024 |
- Zhang, X., et al. (2019). A hierarchical control strategy for energy management systems in microgrids integrating renewable energy resources. IEEE Transactions on Sustainable Energy, 10(3), 1163-1172. DOI: 10.1109/TSTE.2019.2896542. [Google Scholar]
- Li, W., et al. (2020). Machine learning-based energy management system for dynamic load and power generation management in hybrid microgrids. Renewable Energy Journal, 154, 1231-1241. DOI: 10.1016/j.renene.2020.01.095. [Google Scholar]
- Sharma, D., et al. (2020). Fuzzy logic-based EMS for microgrids: Load balancing and integration of distributed energy resources. Energy Reports, 6, 1204-1215. DOI: 10.1016/j.egyr.2020.04.034. [Google Scholar]
- Elamari, M., & Ibrahim, H. (2021). AI-driven energy management system using deep learning algorithms for solar-powered microgrids. Journal of Cleaner Production, 293, 126116. DOI: 10.1016/j.jclepro.2021.126116. [Google Scholar]
- Chen, Z., et al. (2021). Hybrid energy management strategy combining AI and heuristic algorithms for energy flow management in microgrids. Applied Energy, 299, 117265. DOI: 10.1016/j.apenergy.2021.117265. [Google Scholar]
- Kumar, N., et al. (2021). Reinforcement learning-based demand response EMS for microgrids. Energy, 214, 118930. DOI: 10.1016/j.energy.2020.118930. [Google Scholar]
- Abdel-Monem, M., et al. (2022). Genetic algorithms for optimizing EMS in hybrid microgrids. Renewable and Sustainable Energy Reviews, 154, 111837. DOI: 10.1016/j.rser.2021.111837. [Google Scholar]
- Nasir, S., et al. (2022). Multi-agent-based EMS for renewable-powered microgrids: System resilience and fault tolerance. IEEE Access, 10, 35421-35432. DOI: 10.1109/ACCESS.2022.3160419. [Google Scholar]
- Hosseini, S., et al. (2023). AI-enabled EMS for microgrids with real-time data analytics. Energy and AI, 10, 100146. DOI: 10.1016/j.egyai.2023.100146. [Google Scholar]
- Tang, Q., et al. (2023). Blockchain-based decentralized EMS integrating machine learning for autonomous energy distribution. IEEE Transactions on Smart Grid, 14(2), 981-993. DOI: 10.1109/TSG.2023.3243272. [Google Scholar]
- Singh, R., & Gupta, M. (2023). Neural network-based EMS for smart microgrids using deep learning. Energy Conversion and Management, 268, 115931. DOI: 10.1016/j.enconman.2023.115931. [Google Scholar]
- Zhou, Y., et al. (2024). AI, IoT, and big data-enabled EMS for renewable microgrids: Improving reliability and reducing costs. Journal of Energy Storage, 58, 103225. DOI: 10.1016/j.est.2024.103225. [Google Scholar]
- Kumar, A.M., Jayakumar, K., “Drilling studies on Particle Board composite using HSS twist drill and spade drill”, Materials Today,Proceedings,5(8), pp. 16346-16351,2018 [CrossRef] [Google Scholar]
- Ladu, N.S.D., senthilkumarsubburaj., Samikannu, R. “A Review of Renewable Energy Resources. Its Potentials, Benefits, and Challenges in South Sudan, 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2021 [Google Scholar]
- Sathi G.; Deshpande Y.D.; Kumar V.; Garg P.; Singh S.; Pattanaik A.,(2023), “Investigating the Ability of AI Algorithms to Optimize Data Access Processes”,2023 3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023,Vol.,no.,pp.-.doi:10.1109/SMARTGENCON60755.2023.10442371 [Google Scholar]
- Dhabliya D.; Reddy B.; Rajarajeswari S.; Ranganathaswamy M.K.; Nivesh; Pandey M.,(2023), “The Enhanced Optimization on Deep Learning Technologies for Data Science Practices”,2023 3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023,Vol.,no.,pp.-.doi:10.1109/SMARTGENCON60755.2023.10442871 [Google Scholar]
- Katyal A.; Pandian R.; Sharma R.; Rajan T.S.; Sharma N.S.; Singh V.,(2023), “An Investigation into the Effectiveness of DNS-Based Authentication for Wireless Networks”,2023 3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023,Vol.,no.,pp.-.doi:10.1109/SMARTGENCON60755.2023.10442791 [Google Scholar]
- Intelligent Energy Management System for Smart Grids Using Machine Learning Algorithms, K Babu, S Sivasubramanian, CS Nivetha, M Soundari, E3S Web of Conferences 387, 05004. [Google Scholar]
- 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]
- 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]
- 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]
- 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]
- Kumar, B. A., Jyothi, B., Rathore, R. S., Singh, A. R., Kumar, B. H., & Bajaj, M. (2023). A novel framework for enhancing the power quality of electrical vehicle battery charging based on a modified Ferdowsi Converter. Energy Reports, 10, 2394-2416. https://doi.org/10.1016/j.egyr.2023.09.070. [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.