DETECTING MULTI-LABEL ATTACKS IN AC MICROGRIDS BY USING MACHINE LEARNING
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Date
2023
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Publisher
UMT, Lahore
Abstract
In today's competitive market, power utilities are increasingly turning to innovative smart grid technologies to improve the efficiency of their grids. However, this comes with a heightened risk of cyber-attacks, making strong cybersecurity measures essential for the protection of power grids. To detect and counteract false data injection (FDI) assaults, this study suggests a multi-label attack detection method for AC microgrids. In the secondary control of AC microgrids, the method uses frequency and voltage control variables as an optimization problem. The task is formulated as a multi-label classification problem to overcome the difficulty of finding the co-occurrence dependencies and discrepancies in power flow measurements brought on by FDI attacks. This allows for the use of a single model to detect various types of attacks and changes in load, improving the efficiency of the attack detection process. The proposed technique is compared against five different machine learning approaches, and its performance is evaluated using the IEEE 34-bus distribution test system. The results demonstrate effectiveness of the multi-label attack detection technique in identifying and mitigating cyber-attacks in AC microgrids. Overall, this paper provides valuable insights into the development of effective cybersecurity measures for power utilities, assisting to guarantee the secure and reliable operation of electricity grids in today’s competitive market.