Enhancing State Estimation Accuracy in Power Systems: An ANN-Based Data Mining Approach Defending Cyber Attacks
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In energy systems, measurement accuracy is jeopardized by bad data arising from cyber attacks. When bad data is detected in the measurement dataset as a result of cyber attacks, it's essential to identify and eliminate these data. However, this elimination process introduces the problem of missing measurement data, threatening the system's observability conditions. This study proposes a data mining approach supported by artificial neural networks to address the missing measurement data issue when bad data is detected. Our proposed method aims to maintain the system's observability by completing the measurement data lost due to bad data. Consequently, the measurement set purified from bad data enhances the accuracy of the crow search algorithm based state estimation results. This methodology has been shown to successfully mitigate the adverse effects of unforeseen situations, such as cyber attacks. © 2023 IEEE.
Açıklama
14th International Conference on Electrical and Electronics Engineering, ELECO 2023 -- 30 November 2023 through 2 December 2023 -- Virtual, Bursa -- 197135
Anahtar Kelimeler
ANN, crow search algorithm, cyber attacks, state estimation, Computer crime, Crime, Cyber attacks, Data mining, Learning algorithms, Network security, Neural networks, Observability, ANN, Bad data, Crow search algorithm, Cyber-attacks, Energy systems, Measurement data, Missing measurements, Power, Search Algorithms, System observability, State estimation
Kaynak
14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings
WoS Q Değeri
Scopus Q Değeri
N/A
