IoT-Based Underground Cable Fault Detection

The IoT-Based Underground Cable Fault Detection system is a smart electrical infrastructure project designed to locate faults in underground power distribution cables quickly and accurately. Instead of relying on manual inspection or excavation across long cable routes, the system applies electrical fault localisation techniques to estimate the fault distance from the monitoring station. Voltage and current measurements are continuously analysed by an embedded controller, which identifies abnormalities such as open-circuit faults, short circuits, earth faults, and insulation failures. Fault information is transmitted to a cloud platform where maintenance engineers can monitor cable health, receive real-time alerts, and analyse fault history through an interactive dashboard. The project demonstrates how IoT can improve fault diagnosis, reduce maintenance time, minimise power outages, and support modern smart grid infrastructure.

Underground power cables offer greater protection and improved aesthetics than overhead transmission lines, but locating faults within buried cables remains a complex and time-consuming task. When a cable develops insulation damage, short circuits, or conductor breaks, maintenance teams often need to inspect long cable sections manually before identifying the exact fault location. This process increases repair costs, extends service interruptions, and affects the reliability of electrical power distribution. Conventional testing methods also require specialised equipment and significant manual effort, making rapid fault diagnosis difficult for utility providers. An intelligent fault detection system capable of continuously monitoring underground cable conditions and accurately estimating fault locations can significantly improve maintenance efficiency and reduce downtime.

The proposed solution develops an IoT-enabled cable monitoring platform that continuously measures electrical parameters along underground distribution lines. The embedded controller analyses voltage drops, current variations, and resistance values to identify abnormal operating conditions. Once a fault is detected, mathematical calculations estimate the approximate distance of the fault from the monitoring station. The calculated information is securely transmitted to a cloud server using MQTT or HTTP protocols, where maintenance engineers can monitor fault events, review historical diagnostics, and receive immediate notifications through a web dashboard. This approach enables faster fault isolation, reduces excavation work, and improves the overall reliability of electrical distribution networks.

Monitoring Layer Voltage Measurement Circuit Current Measurement Circuit Fault Simulation Network Embedded Control Layer ESP32 Arduino Uno Raspberry Pi Communication Layer Wi-Fi MQTT HTTP REST API Cloud Platform Flask Backend MySQL Database Event Processing Engine Maintenance Layer Fault Analytics Dashboard Cable Health Reports Alert Management User Layer Maintenance Engineer Utility Operator System Administrator

The project begins by developing a laboratory-scale underground cable model that represents different sections of an electrical distribution network. A resistor network is used to simulate cable length, allowing various fault conditions such as open circuits, short circuits, and earth faults to be introduced at different distances. Voltage sensing and current sensing circuits are connected to the embedded controller so that electrical behaviour can be continuously monitored under normal and faulty operating conditions. After assembling the hardware, the embedded firmware is programmed to measure electrical parameters at regular intervals and compare them with expected operating values. Instead of simply detecting whether a fault exists, the controller performs resistance-based calculations to estimate the approximate distance between the monitoring unit and the fault location. This enables maintenance personnel to identify the affected cable section without manually inspecting the entire distribution line. The communication module is then configured to establish a secure connection with the cloud platform using MQTT or HTTP protocols. Whenever abnormal electrical conditions are detected, the controller immediately transmits diagnostic information including fault category, estimated fault distance, measured voltage, measured current, timestamp, and device identification. This information is processed by the backend application and permanently stored for maintenance analysis and historical comparison. A web-based monitoring dashboard is developed using Python, Flask, HTML, CSS, and JavaScript to provide utility engineers with a comprehensive view of cable network health. Instead of displaying only fault alerts, the dashboard visualises cable status, recent fault events, estimated fault locations, electrical parameter trends, maintenance history, and communication status. Engineers can search historical incidents, compare recurring faults, and prioritise repair activities based on fault severity and operational impact. The completed system is validated by simulating multiple electrical fault scenarios under varying load conditions. Different fault distances are introduced to evaluate localisation accuracy, while communication reliability, sensor precision, and response time are measured to verify overall performance. After successful testing, the platform can be deployed within smart distribution networks, industrial power systems, educational laboratories, and utility maintenance environments to support rapid fault diagnosis and improve electrical service reliability.

Future versions of the system can integrate AI-based predictive fault analysis, fibre-optic cable monitoring, thermal imaging for underground hotspots, GIS-based cable mapping, SCADA integration, drone-assisted maintenance planning, digital twin technology for power networks, machine learning models for insulation degradation prediction, mobile applications for field engineers, and edge computing for faster fault localisation in large electrical distribution systems.

The IoT-Based Underground Cable Fault Detection project demonstrates how IoT and embedded technologies can modernise electrical maintenance by enabling continuous cable monitoring and intelligent fault localisation. Through real-time electrical analysis, cloud connectivity, and automated diagnostic reporting, the system significantly reduces the time required to identify underground cable failures while improving maintenance planning and power distribution reliability. Students implementing this project gain practical experience in embedded systems, electrical engineering, IoT communication, cloud platforms, and smart utility infrastructure, making it an excellent advanced-level project for Electrical Engineering, Electronics, Embedded Systems, and Computer Science programmes.