| dc.description.abstract | This thesis provides a comprehensive analysis of underground cable faults, covering their fundamental
characteristics, detection methodologies, location techniques, and protection strategies. Underground
cables are vital components of modern power distribution systems, offering aesthetic and environmental
advantages over overhead lines. However, their buried nature presents significant challenges for fault
management, leading to prolonged power outages, increased repair costs, and reduced system reliability.
This study reviews the types and causes of underground cable faults, including open circuit, short circuit,
ground, and incipient faults, and their detrimental effects on power systems. It critically evaluates
traditional methods such as Murray Loop, Varley Loop, Time Domain Reflectometry (TDR), and high
voltage surge (thumping) techniques, highlighting their principles, advantages, and limitations.
Furthermore, the thesis explores advanced techniques, including acoustic methods, thermal imaging,
Partial Discharge (PD) detection, Very Low Frequency (VLF) testing, and the integration of Artificial
Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies for enhanced fault
detection, location, and prediction. Various pro taction strategies, such as overcurrent, earth fault, and
differential protection, along with modern relaying schemes and preventive maintenance practices, are
discussed to ensure system reliability and safety. Real-world case studies illustrate the practical application
and significant economic and operational benefits of effective fault management. Finally, the thesis
concludes with a summary of findings, contributions, and outlines future research directions to further
enhance the resilience, efficiency, and sustainability of underground power distribution networks. | en_US |
