A CRITICAL REVIEW ON FAULT ANALYSIS IN UNDERGROUND TRANSMISSION CABLE

The growing demand for reliable electricity in urban and industrial environments has accelerated the adoption of underground transmission cables due to their safety, durability, and environmental benefits. However, the concealed nature of these systems poses significant challenges in detecting and localizing faults. This paper presents a critical review of fault causes, types, and diagnostic techniques in underground transmission cables. Traditional methods such as time domain reflectometry and bridge-based techniques are compared with modern approaches employing the Internet of Things (IoT), thermal imaging, and artificial intelligence (AI). The review identifies key limitations in existing systems and emphasizes the need for integrated solutions. A multi-layer diagnostic architecture is proposed, combining smart sensing nodes, edge computing, and cloud-based analytics for real-time fault monitoring and predictive maintenance. This framework enhances reliability, reduces downtime, and minimizes repair costs. The study concludes that hybrid fault management systems integrating traditional and intelligent methods offer the most effective pathway for achieving resilient and sustainable underground power networks.