Using modified prediction interval-based machine learning model to mitigate data attack in microgrid. (July 2021)

• Record Type: Journal Article
• Authors: Ye, Zhangfan; Yang, Huawei; Zheng, Mingkui
• Is Part Of: International journal of electrical power & energy systems. Volume 129(2021)
• Publication Date: 2021-07
• Subjects: MG operation -- Cyber resilience -- Prediction interval -- Data integrity -- M-GWO -- LSTM -- LUBE; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2021.106847
• Languages: English
• ISSNs: 0142-0615

Abstract

Abstract: Recently, microgrids (MGs) have been attracted more attention due to their technique and economic advantages. However, along with these advantages, because of the cyber and physical structure of MGs, they are more prone to cyber and physical attacks. To this end, in this paper, a new machine learning framework is developed to detect and mitigate the fake data. More specifically, a new machine learning technique has been developed, which is mainly based on the long-short term memory (LSTM); however, modified with recurrent neural network (RNN) and prediction intervals (PIs). Finally, an evolutionary algorithm has been used to address the nonlinearity and complexity associated with the problem. The proposed framework is tested on real MG data. Results show the efficiency and merit of the proposed techniques, compare to the conventional techniques.