Article detail · 2021
Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
Journal
Energies- Year
- 2021
- Type
- article
Data source split
- YÖKSİS venue Energies
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
OpenAlex · English
Integration of information technologies with power systems has unlocked unprecedented opportunities in optimization and control fields. Increased data collection and monitoring enable control systems to have a better understanding of the pseudo-real-time condition of power systems. In this fashion, more accurate and effective decisions can be made. This is the key towards mitigating negative impacts of novel technologies such as renewables and electric vehicles and increasing their share in the overall generation portfolio. However, such extensive information exchange has created cybersecurity vulnerabilities in power systems that were not encountered before. It is imperative that these vulnerabilities are understood well, and proper mitigation techniques are implemented. This paper presents an extensive study of cybersecurity concerns in Smart grids in line with latest developments. Relevant standardization and mitigation efforts are discussed in detail and then the classification of different cyber-attacks in smart grid domain with special focus on false data injection (FDI) attack, due to its high impact on different operations. Different uses of this attack as well as developed detection models and methods are analysed. Finally, impacts on smart grid operation and current challenges are presented for future research directions.
Topics
Citations
OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.
59 citations
OpenAlex cited_by_count (cache / database)
5 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- Review on Energy Application Using Blockchain Technology With an Introductions in the Pricing Infrastructure 2022
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- Analysis of deceptive data attacks with adversarial machine learning for solar photovoltaic power generation forecasting 2024
- Analysis of deceptive data attacks with adversarial machine learning for solar photovoltaic power generation forecasting 2022
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