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As cyber threats evolve, Power Distribution Systems (PDS) face growing risks from sophisticated attacks like False Data Injection Attacks (FDIAs), which can disrupt system stability and reliability. This thesis presents a quantum-based approach using Quantum Support Vector Machines (QSVM) to detect and mitigate FDIAs in PDS. By leveraging quantum feature mapping, the QSVM model efficiently identifies subtle anomalies within high-dimensional data, enhancing the accuracy and speed of FDIA detection. The methodology includes the integration of an augmented Lagrangian function to further optimize detection performance. Validated using the IEEE-13 bus system, this QSVM framework showcases its potential as a robust, real-time detection tool for cybersecurity in smart grid infrastructures. The results underscore the promise of quantum computing in strengthening the resilience of critical energy systems. |
