Cyber Attacks and Faults Discrimination in Intelligent Electronic Device-Based Energy Management Systems

نویسندگان

چکیده

Intelligent electronic devices (IEDs) along with advanced information and communication technology (ICT)-based networks are emerging in the legacy power grid to obtain real-time system states provide energy management (EMS) wide-area monitoring control capabilities. Cyber attackers can inject malicious data into EMS mislead state estimation process disrupt operations or initiate blackouts. A machine learning algorithm (MLA)-based approach is presented this paper detect false injection attacks (FDIAs) an IED-based EMS. In addition, stealthy construction of FDIAs their impact on detection rate MLAs analyzed. Furthermore, impacts natural disturbances such as faults considered, research work extended distinguish between cyber by using state-of-the-art MLAs. paper, Random Forest, OneR, Naive Bayes, SVM, AdaBoost used classifiers, performance parameters rate, positive precision, recall, f-measure analyzed for different case scenarios IEEE benchmark 14-bus system. The experimental results validated load flow from New York Independent System Operator (NYISO).

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ژورنال

عنوان ژورنال: Electronics

سال: 2021

ISSN: ['2079-9292']

DOI: https://doi.org/10.3390/electronics10060650