Age of Information-Optimized Differential Protection Strategy for Smart Grids

Abstract

Differential protection is a fundamental mechanism in power systems for detecting and isolating faults. However, traditional protection schemes face significant challenges in modern smart grids due to communication delays and the inability to dynamically adapt to real-time information. These limitations often result in reduced fault detection accuracy and delayed system response, threatening the reliability and stability of the grid. To address these shortcomings, we propose an Age of Information (AoI)-optimized differential protection strategy tailored for smart grids. By modeling the differential protection problem as a remote Markov Decision Process (MDP), we incorporate AoI as a critical factor to capture the freshness of information in decision-making under stochastic delays. Our analysis reveals that treating AoI as auxiliary side information, rather than a standalone optimization goal, significantly enhances the timeliness of information and indirectly improves fault detection accuracy. By leveraging AoI, we show that it is possible to enhance the timeliness and accuracy of fault detection, even under adverse network conditions, by ensuring that the system relies on the freshest information available. In particular, our results demonstrate a 6.5% improvement in information freshness compared to traditional methods, leading to enhanced system performance. This study provides new insights into leveraging information freshness to overcome the inherent limitations of traditional differential protection schemes, thereby improving the efficiency and resilience of modern power systems.

Keywords

• Differential protection
• smart grids
• Markov decision process
• age of information
• remote communication-control co-design
• fault detection