ESQFL: Digital Twin-Driven Explainable and Secured Quantum Federated Learning for Voltage Stability Assessment in Smart Grids

Voltage stability remains a pivotal concern in power systems, especially with the integration of renewable energy sources and high-demand loads that use induction motors. Recent blackouts have highlighted the vulnerability of wind power systems to voltage stability assessment (VSA) threats. Traditional machine learning-based VSA methods, while efficient, often rely on centralized storage systems, making them susceptible to single-point failures. The incorporation of Digital Twins (DT), providing real-time virtual representations of physical power system components, offers transformative capabilities in prediction, analysis, and profit allocation within smart grids. This paper introduces an Explainable and Secured Quantum Federated Learning (ESQFL) method for VSA, an innovative solution combining quantum techniques, differential privacy (DP), and Shapley value calculation. ESQFL, by leveraging the continuous insights from DT, emphasizes localized data analytics in a decentralized framework, integrating a Gaussian-based DP mechanism for enhanced data privacy and leveraging quantum teleportation for efficient Shapley value transmission. The paper systematically explores these concepts, compares the centralized and decentralized architectures, and provides comprehensive evaluations of ESQFL's efficacy on cross-out testing systems. The findings underscore ESQFL's potential as a pioneering solution in smart grid management, combining quantum computing with the advanced monitoring capabilities of DT for optimal VSA.