Enhancing Dynamic Security Assessment in Smart Grids Through Quantum Federated Learning

Dynamic Security Assessment (DSA) is critical for maintaining stability in large-scale smart grids, especially with the growing integration of renewable energy sources and the inherent uncertainties. Traditional model-based analytical methods are increasingly inadequate under these complex conditions. To address these challenges, we propose a pioneering Quantum Federated Learning-based DSA (QFLDSA) method by combining hybrid quantum-classical machine learning and federated learning. QFLDSA offers an effective way to deal with high-dimensional data and uncertainties inherent in the grid. Moreover, QFLDSA leverages the unique capabilities of quantum computing to enhance the processing of differential-algebraic equations that underpin grid stability. This paper demonstrates through extensive simulations that QFLDSA significantly outperforms traditional methods, achieving the highest average F1-score performance at 97.94%, while maintaining 97.67 ± 0.17% prediction accuracy on both classical and quantum computing devices only with fewer transmitted model parameters (reducing up to ∼ 1000X). These enhancements enable more reliable and rapid deployment of preventive stability control measures across smart grids. Our results underscore QFLDSA's potential as a robust solution for the dynamic security challenges of modern smart grids, paving the way for future innovations in grid management technology. Note to Practitioners - In the rapidly evolving world of smart cyber-physical grids, ensuring the stability of electric power systems is paramount. Failures in these systems can lead to catastrophic blackouts, affecting countless homes and businesses. Traditional DSA methods to assess and ensure this stability, while effective, are becoming increasingly complex and vulnerable to single points of failure or cyberattacks. Enter the QFLDSA method, a novel approach we introduce in this paper. In simple terms, this method combines the strengths of quantum machine learning and federated learning to analyze data efficiently across a distributed system. Here's why these matters: 1) Localized Analysis: Instead of relying on a central hub to analyze all data, QFLDSA allows for localized data analysis. This means that if one part of the system fails, it does not bring down the entire grid's analysis capabilities. It is akin to having multiple control rooms instead of one, ensuring that a problem in one room does not halt the entire operation. 2) Future-Ready: As we move towards a future where quantum computing becomes more prevalent, QFLDSA is designed to work seamlessly with both today's classical devices and tomorrow's quantum devices. This ensures that as technology evolves, our method remains relevant and efficient. 3) Proven Performance: We have not just introduced a new method; we have rigorously tested it. Our theoretical proofs and practical tests confirm that QFLDSA offers accurate and efficient data analysis for smart grids. For industry professionals, the takeaway is clear: if looking for a resilient, future-ready, and proven method to ensure the stability of smart grid, QFLDSA offers a compelling solution. © 2004-2012 IEEE.