A Review of Power System False Data Attack Detection Technology Based on Big Data

Autor: Zhengwei Chang, Jie Wu, Huihui Liang, Yong Wang, Yanfeng Wang, Xingzhong Xiong
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Information, Vol 15, Iss 8, p 439 (2024)
Druh dokumentu: article
ISSN: 15080439
2078-2489
DOI: 10.3390/info15080439
Popis: As power big data plays an increasingly important role in the operation, maintenance, and management of power systems, complex and covert false data attacks pose a serious threat to the safe and stable operation of the power system. This article first explores the characteristics of new power systems, and the challenges posed by false data attacks. The application of big data technology in power production optimization, energy consumption analysis, and user service improvement is then investigated. The article classifies typical attacks against the four stages of power big data systems in detail and analyzes the characteristics of the attack types. It comprehensively summarizes the attack detection technologies used in the four key stages of power big data, including state estimation, machine learning, and data-driven attack detection methods in the data collection stage; clock synchronization monitoring and defense strategies in the data transmission stage; data processing and analysis, data integrity verification and protection measures of blockchain technology in the third stage; and traffic supervision, statistics and elastic computing measures in the control and response stage. Finally, the limitations of attack detection mechanisms are proposed and discussed from three dimensions: research problems, existing solutions, and future research directions. It aims to provide useful references and inspiration for researchers in power big data security to promote technological progress in the safe and stable operation of power systems.
Databáze: Directory of Open Access Journals
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