Abstract
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 and provide the energy management system (EMS) with wide-area monitoring and advanced control capabilities. Cyber attackers can inject malicious data into the EMS to mislead the state estimation process and disrupt operations or initiate blackouts. A machine learning algorithm (MLA)-based approach is presented in this paper to detect false data injection attacks (FDIAs) in an IED-based EMS. In addition, stealthy construction of FDIAs and their impact on the detection rate of MLAs are analyzed. Furthermore, the impacts of natural disturbances such as faults on the system are considered, and the research work is extended to distinguish between cyber attacks and faults by using state-of-the-art MLAs. In this paper, state-of-the-art MLAs such as Random Forest, OneR, Naive Bayes, SVM, and AdaBoost are used as detection classifiers, and performance parameters such as detection rate, false positive rate, precision, recall, and f-measure are analyzed for different case scenarios on the IEEE benchmark 14-bus system. The experimental results are validated using real-time load flow data from the New York Independent System Operator (NYISO).
Highlights
The demand for power supply is growing with time, and various types of efficient and renewable energy sources are being integrated into the legacy power grid
Different case scenarios of normal operational, random, and stealthy cyber attacks and faults are simulated in the IEEE 14-bus test system
On the other hand, synthesized data are prepared by using a proper mathematical model and are labelled as attack data to train machine learning algorithm (MLA) classifiers
Summary
The demand for power supply is growing with time, and various types of efficient and renewable energy sources are being integrated into the legacy power grid. A simple and realistic machine learning-based approach is presented in this paper to detect cyber attacks in an IED-based power system EMS and assess the feasibility of state-of-the-art MLAs in distinguishing between FDIAs and faults. The IED-Based Smart Power System The smart power system is a complex infrastructure of tightly coupled physical and cyber networks In this large and complex cyber-physical network, an advanced metering infrastructure comprised of several IEDs such as PMUs is used to monitor and measure important physical quantities such as voltages, currents, power, and angles at different bus and node locations of a power system. Other IEDs, such as digital intelligent relays, are deployed to generate autonomous control decisions
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