| Issue |
Sci. Tech. Energ. Transition
Volume 79, 2024
Decarbonizing Energy Systems: Smart Grid and Renewable Technologies
|
|
|---|---|---|
| Article Number | 62 | |
| Number of page(s) | 18 | |
| DOI | https://doi.org/10.2516/stet/2024056 | |
| Published online | 02 September 2024 | |
Review Article
A comprehensive exploration of IoT-enabled smart grid systems: power quality issues, solutions, and challenges
1
Department of Electrical and Electronics Engineering, Vignan’s Foundation for Science, Technology and Research, Guntur 522213, AP, India
2
Department of Electrical and Electronics Engineering, KSRM College of Engineering, Kadapa 516003, AP, India
3
Department of Electrical Engineering, National Institute of Technology, Patna, Bihar 800005, India
* Corresponding author: arvb_eee@vignan.ac.in
Received:
11
May
2024
Accepted:
18
July
2024
The potential for Internet of Things (IoT) technology to transform energy management has led to significant interest in its incorporation into smart grid systems. This review discusses the state of IoT-powered smart grids today, focusing on applications, current technology, and power quality (PQ) issues. Key problems including harmonics, transients, and voltage fluctuations are identified, and mitigation techniques using sophisticated filters and intelligent systems like fuzzy logic control (FLC) and artificial neural networks (ANN) are investigated. Concerns about interoperability and scalability are among the other challenges the review lists for IoT implementation. The revolutionary potential of IoT in improving smart grid efficiency and dependability is highlighted in our findings, which provide valuable insights for scholars and practitioners seeking to develop this sector.
Key words: Artificial intelligence / Cloud computing / Data analytics / Edge computing / IoT / Machine learning / Power quality / Smart grid
© The Author(s), published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.