Abstract

Phasor measurement unit (PMU) and phasor data concentrator (PDC) plays a crucial role in the smart grid system for dynamic operations. However, the communication of data from PMU to regional PDC or central PDC is susceptible to various cyber-attacks. To address this issue and ensure data privacy, this study aimed to present an architecture focusing on securing data communication between PMU and central PDC for fault detection purposes. The proposed architecture incorporated a security layer to ensure data privacy and reliable communication. Data privacy was achieved by employing the advanced encryption standard (AES) cryptosystem, which converts the data into AES block ciphers. Additionally, the study introduced two classification models to prevent data manipulation and detect faults. The first model was the squirrel search algorithm (SSA)--based convolutional neural network (CNN) attack detection model. This model identified and reported the presence of attackers attempting to manipulate data, thereby preventing the system from sharing the key for data decryption. By implementing the proposed architecture, this study successfully ensured the prevention of data alteration through data encryption and maintained an attack-free system through the identification and classification of attacks. The system proved to be effective in identifying power system conditions, such as normal operation, faults, zone location, and power swings. Furthermore, the architecture could differentiate between symmetrical faults during power swings and normal power swing conditions. The study focused on the NE-39 Bus system, and its proposed architecture effectively addressed the challenges of data security and fault detection in power systems. The incorporation of advanced techniques, such as the SSA-based CNN models, contributed to the system's improved performance while maintaining a lower computational time compared to existing online methods. By ensuring secure communication and reliable fault detection, this work aimed to present a significant step towards enhancing the efficiency and robustness of smart grid systems in dynamic operations. By implementing the recommended architecture, this study effectively guaranteed data integrity through encryption and established a secure system by identifying and categorizing attacks. The efficacy of the system included its ability to accurately recognize various power system circumstances, including normal operation, faults, zone placement, and power swings. In addition, the architectural design showcased its capability to discern symmetrical flaws occurring during power swings from regular power swing scenarios. This study's proposed design efficiently addressed the difficulties of data security and fault detection in power systems, with a specific focus on the NE-39 Bus system. The integration of sophisticated methodologies, including the utilization of SSA-based CNN models, not only improved the overall efficiency of the system but also reduced processing time in comparison to pre-existing online approaches. This research endeavour demonstrated a substantial advancement in enhancing the effectiveness and robustness of smart grid systems during dynamic operations through secure communication and dependable fault detection mechanisms.

Keyword

Data security, Phasor measurement unit (PMU), Phasor data concentrator (PDC), Convolutional neural network (CNN), Squirrel search algorithm (SSA), Online sequential squirrel search algorithm (OS-SSA) CNN.

Cite this article

Kiruthika M, S. B

Refference

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