IoT-Based Smart Monitoring and Transmission Line Fault Detection and Localization Using NodeMCU, ACS712 Sensors, GPS and Firebase

Abstract

Electrical transmission line faults require rapid detection, classification and localization to reduce outage duration and improve service reliability. This paper presents a low-cost IoT-enabled prototype for monitoring a three-phase transmission line model and reporting fault information in real time. Three ACS712 current sensors measure phase currents and a CD4051 analog multiplexer routes the signals to the single ADC input of a NodeMCU ESP8266 controller. A threshold-based firmware algorithm identifies faulted phases, classifies common fault categories such as LG, LL, LLG and LLLG, and estimates the fault distance using a resistor-tap line model representing 2, 4, 6 and 8 km sections. A NEO-6M GPS receiver provides geographic coordinates, while a 16x2 I2C LCD, RGB LEDs, relay module and buzzer provide local indication and alarm functions. The NodeMCU uploads current values, fault type, distance, latitude and longitude to Firebase Realtime Database for remote monitoring. Prototype tests verified end-to-end sensing, display and cloud logging for line-to-line, line-line-ground, three-phase and healthy conditions. The live Firebase snapshot recorded current values of 432, 404 and 430 ADC counts, a 2 km location estimate, and GPS coordinates near 30.39342 N, 78.43390 E. The results demonstrate the feasibility of an IoT-based educational and laboratory prototype, while also showing that additional calibration and repeated trials are needed for robust field-level deployment. Keywords: transmission line fault detection; fault localization; Internet of Things; NodeMCU ESP8266; ACS712; Firebase; GPS; smart grid monitoring.

Introduction

The text describes the design and implementation of an IoT-based transmission line fault detection and localization system using a low-cost hardware prototype. The system is developed to demonstrate how faults in three-phase transmission lines can be detected, classified, and reported in real time using embedded sensors and cloud connectivity.

Transmission and distribution lines often suffer faults due to environmental and mechanical issues, and quick fault location is essential for reducing repair time. Traditional methods such as impedance-based and traveling-wave techniques are accurate but complex and expensive. This project instead proposes a simplified educational prototype that focuses on affordability, real-time monitoring, and cloud-based visualization.

The system uses a NodeMCU ESP8266, three ACS712 current sensors, and a CD4051 multiplexer to monitor phase currents. Faults are detected using threshold-based logic and classified into LG, LL, LLG, and LLLG types. A resistor-tap model simulates transmission line sections (2–8 km) to estimate fault location. Local outputs include LEDs, LCD display, relay control, and a buzzer for alerts, while GPS and Firebase are used for real-time cloud logging of fault data and location.

The methodology involves reading phase currents, comparing them with calibrated thresholds, and generating fault flags for each phase. Based on these flags, the system determines the fault type and approximate distance. The results are transmitted to Firebase in JSON format, including current values, fault type, estimated distance, and GPS coordinates.

Experimental results show that the system successfully detects different fault conditions, displays them locally, and uploads them to the cloud in real time. Healthy and severe fault conditions (LL, LLG, and LLLG) are correctly identified, though minor calibration issues were observed in some LG cases, indicating the need for improved threshold tuning and signal processing.

Conclusion

An IoT-based smart monitoring and transmission line fault detection prototype was designed and evaluated using NodeMCU ESP8266, ACS712 current sensors, a CD4051 multiplexer, NEO-6M GPS and Firebase Realtime Database. The system detects abnormal phase-current patterns, classifies common fault types, estimates discrete tap distance, provides local LCD/LED/buzzer indication and uploads structured fault records to the cloud. Experimental observations confirm the feasibility of end-to-end sensing and cloud reporting for a scaled laboratory model. The observed LG/LTL ambiguity highlights the need for improved calibration and decision logic. With additional signal processing, repeated testing and higher-voltage isolation design, the prototype can be extended toward a more reliable smart-grid monitoring platform.

References

[1] IEEE Standards Association, \"IEEE C37.114-2014: IEEE Guide for Determining Fault Location on AC Transmission and Distribution Lines,\" 2014. [2] M. M. Saha, J. Izykowski, and E. Rosolowski, Fault Location on Power Networks. London, U.K.: Springer, 2010. DOI: 10.1007/978-1-84882-886-5. [3] S. Roostaee, M. S. Thomas, and S. Mehfuz, \"Experimental studies on impedance based fault location for long transmission lines,\" Protection and Control of Modern Power Systems, vol. 2, no. 16, 2017. [4] S. Adhikari, N. Sinha, and T. Dorendrajit, \"Fuzzy logic based on-line fault detection and classification in transmission line,\" SpringerPlus, vol. 5, no. 1002, 2016. [5] G. Pan et al., \"An internet of thing monitoring system with high-precision time reference for transmission lines and the fault recognition method,\" Electrical Engineering, vol. 104, pp. 4227-4237, 2022. [6] Espressif Systems, \"ESP8266EX Datasheet,\" official product documentation. [7] Allegro MicroSystems, \"ACS712 Hall-Effect Based Linear Current Sensor IC,\" official product documentation. [8] Texas Instruments, \"CD405xB CMOS Single 8-Channel Analog Multiplexer or Demultiplexer With Logic- Level Conversion,\" datasheet. [9] u-blox AG, \"NEO-6 u-blox 6 GPS Modules Data Sheet,\" product documentation. [10] Google Firebase, \"Firebase Realtime Database documentation,\" official documentation. [11] Project dataset supplied by the authors, \"Smart Monitoring and Transmission Line Fault Detection and Localization System,\" Embedded Systems and IoT Final Project, 2026.

Copyright

Copyright © 2026 Khemchand Dahit, Nanse , Kaushal Kushiyait, Sunil Yadav. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.