Adaptive Power System Analysis in the Age of Cyber Threats: Securing Smart Grids against Dynamic Variations

Abstract

The increasing integration of renewable energy sources and advanced digital technologies in smart grids has transformed power systems, making them more efficient yet vulnerable to sophisticated cyber threats. This study explores adaptive power system analysis methods to secure smart grids against cyber-attacks while addressing dynamic variations in power generation and load demand. By incorporating AI-based threat detection models such as Support Vector Machines (SVM), Autoencoders, and K-means clustering, the research examines their effectiveness in detecting anomalies and ensuring operational stability. The study focuses on the challenges posed by variable renewable energy sources, which introduce noise into the data, complicating the identification of legitimate system fluctuations versus cyber-attacks. Using a cyber-physical testbed, the proposed framework was validated, revealing a significant improvement in detection accuracy and system resilience. The adaptive learning algorithms demonstrated their capacity to adjust to fluctuations in renewable generation, reducing false positive rates and improving the overall reliability of the smart grid. Results show a 95% accuracy in threat detection with minimal disruption to power delivery, highlighting the practicality of this approach in real-world applications. Additionally, the study provides a cost analysis, emphasizing the economic benefits of preemptive threat identification over traditional methods. The findings underscore the importance of adaptive cybersecurity frameworks for maintaining the stability and security of modern power systems in the face of evolving cyber threats and dynamic operational conditions. This research offers valuable insights into designing resilient power systems capable of adapting to the uncertainties posed by renewable energy sources while maintaining robust protection against cyber-attacks.