IoT-enabled Automatic Underground Cable Fault Detection and Monitoring System

As technology advances, underground cables are chosen over overhead lines for more reliability, and to make greater use of available space in metropolitan areas. Although underground cables have several advantages, their maintenance can be somewhat challenging. Locating an underground cable fault is often expensive, time-consuming, and complicated when any problem arises. This research investigates underground cable fault distance locators that utilize a microcontroller integrated with GSM, GPS, and other modules. It employs a simple application of Ohm's law, which demonstrates that voltage drop varies with changes in cable length. The cable length in kilometers is simulated using a series of resistors. A detailed simulation was conducted for the proposed circuit arrangement, including the rectification phase to step down the power supply voltage from 220V to 12V and 5V. This setup allows for the identification of fault locations in all three phases. Based on the simulation, a PCB board has also been constructed with the appropriate resistors and switches to detect and measure short and open circuit faults. GSM and GPS technology have been utilized to leverage IoT capabilities. A continuous monitoring system has been proposed where, if a fault occurs, it locates the exact fault location. This system displays the fault's phase and distance on an LCD, sends a mobile alert notification with precise map coordinates, and triggers an LED light indication at the base station. This prototype can be a promising solution though the accuracy depends on several parameters and system specifications during real-time application.

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Publication Details

Published
2025-06-27
DOI
https://doi.org/10.1109/ncim65934.2025.11159992
Citations
1
Primary Topic
Power Systems Fault Detection
Type
article
Field-Weighted Citation Impact
1.21
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article

IoT-enabled Automatic Underground Cable Fault Detection and Monitoring System

Abu Shufian, Israt Jahan, Riadul Islam, Md. Siddikur Rahman et al.
1 citations
Power Systems Fault Detection
1.21
article

IoT-enabled Automatic Underground Cable Fault Detection and Monitoring System

Abu Shufian, Israt Jahan, Riadul Islam, Md. Siddikur Rahman, Md Mahfuzur Rahman Enam, Hasanur Zaman Anonto
article en
1 citations

Abstract

As technology advances, underground cables are chosen over overhead lines for more reliability, and to make greater use of available space in metropolitan areas. Although underground cables have several advantages, their maintenance can be somewhat challenging. Locating an underground cable fault is often expensive, time-consuming, and complicated when any problem arises. This research investigates underground cable fault distance locators that utilize a microcontroller integrated with GSM, GPS, and other modules. It employs a simple application of Ohm's law, which demonstrates that voltage drop varies with changes in cable length. The cable length in kilometers is simulated using a series of resistors. A detailed simulation was conducted for the proposed circuit arrangement, including the rectification phase to step down the power supply voltage from 220V to 12V and 5V. This setup allows for the identification of fault locations in all three phases. Based on the simulation, a PCB board has also been constructed with the appropriate resistors and switches to detect and measure short and open circuit faults. GSM and GPS technology have been utilized to leverage IoT capabilities. A continuous monitoring system has been proposed where, if a fault occurs, it locates the exact fault location. This system displays the fault's phase and distance on an LCD, sends a mobile alert notification with precise map coordinates, and triggers an LED light indication at the base station. This prototype can be a promising solution though the accuracy depends on several parameters and system specifications during real-time application.

American International University-Bangladesh (BD), Lamar University (US), Universiti Teknologi Petronas (MY)
Openalex Percentile: Top 17%
Power Systems Fault Detection
1.21
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