Year: 2025
Type: dissertation
Abstract: Modern power transmission networks are evolving rapidly with the growing integration of communication, automation, and control technologies. While this transformation enhances real-time monitoring and operational flexibility, it also introduces new challenges in ensuring reliability, stability, and cybersecurity. Transmission lines remain prone to physical anomalies such as faults due to their long-distance exposure, while increasing digitalization has made them vulnerable to cyber anomalies like False Data Injection Attacks (FDIAs) and Denial-of-Service (DoS) attacks. Conventional protection schemes, based on predefined thresholds and static models, struggle to adapt to these evolving threats, highlighting the need for intelligent, learning-based solutions capable of accurately identifying and localizing system disturbances. To address these challenges, this research develops a series of machine learning–based frameworks to improve anomaly detection, isolation, and localization in transmission systems. Unsupervised learning methods identify operational anomalies without labeled data, while a data-driven Optimal Phasor Measurement Unit (PMU) Placement approach enhances network observability. Hybrid learning-based FDIL techniques provide accurate fault localization under diverse operating conditions. For cyber anomaly detection, a multilabel learning framework identifies and localizes FDIAs across multiple sensors, improving the precision of attack localization. A complementary forecasting-based approach maintains observability and detection reliability under simultaneous FDIA–DoS conditions, ensuring system continuity during partial data loss. These frameworks are validated through simulations and Hardware-In-the-Loop (HIL) experiments on benchmark transmission systems with varying sizes and topologies, demonstrating their reliability, scalability, and resilience.
Author: Shrestha, Rajendra
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7134144017
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