DRL-Driven Pilot Schemes for Optimized Distance Protection in Power Systems Incorporating IEC 61850 Communication Protocol

Distance protection is crucial for maintaining power system reliability. As power systems become increasingly complex, the integration of advanced artificial intelligence (AI) techniques such as Deep Reinforcement Learning (DRL) offers a promising approach to enhance fault detection and response. This paper presents a DRL-Driven pilot scheme combined with the IEC 61850 communication protocol for optimized distance protection. The proposed method leverages real-time data, adaptive decision-making, and high-speed communication to minimize fault clearance time while improving grid resilience. Simulation results demonstrate the effectiveness of the approach under diverse fault scenarios.In this work, DRL is integrated with existing distance protection systems through pilot schemes using the IEC 61850 communication standard, specifically leveraging GOOSE messaging for real-time relay coordination. The fundamentals of DRL in protection systems are examined, covering aspects such as data collection, preprocessing, model training, and performance evaluation. The paper also addresses the challenges and limitations in implementing DRL within protection systems, including issues related to data availability, system complexity, and real-time operation. Comparative analysis between the proposed DRL-Driven distance relays and conventional numerical relays highlights the advantages of DRL, particularly in terms of fault detection time, accuracy, and adaptability to modern grid conditions. Finally, this paper presents a comprehensive DRL-driven protection scheme for transmission lines and evaluates its effectiveness against traditional approaches, paving the way for more resilient and reliable power system protection.