Overhead Line Faults Detector with Auto Trip Protection

Abstract

Overhead electrical distribution lines constitute a major portion of power delivery infrastructure in developing as well as developed regions due to their economic feasibility and ease of installation. Despite their advantages, these systems are continuously exposed to environmental and mechanical stresses, making them highly susceptible to electrical faults. Fault conditions such as open conductor faults, line-to-line short circuits, and line-to-ground faults can result in serious hazards including electric shock incidents, fire accidents, equipment damage, and prolonged power outages. Rapid detection and isolation of such faults are therefore essential for ensuring safety and maintaining system reliability. This paper presents the design, simulation, and hardware implementation of a three-phase overhead line fault detection and automatic trip protection system using embedded controllers. The proposed system integrates an ESP32 microcontroller for generating balanced three-phase sinusoidal pulse width modulation signals and an Arduino Mega microcontroller for continuous monitoring of voltage and current parameters. A relay-based isolation mechanism disconnects the faulty phase upon detection of abnormal electrical conditions. Additionally, a GSM communication module is incorporated to transmit real-time fault alerts to authorized personnel. The system was validated through simulation and experimental testing, demonstrating reliable fault detection performance and rapid protective action. The proposed prototype provides a scalable and cost-effective solution for enhancing safety in overhead distribution networks.

Introduction

The text presents the design and implementation of a smart embedded system for fault detection and protection in overhead electrical distribution networks. Overhead lines are widely used due to low cost but are highly vulnerable to environmental and mechanical faults such as open-circuit, line-to-line, and line-to-ground faults, which can cause safety hazards and power outages. Traditional protection systems often fail to detect localized faults quickly, leading to delays and risks.

To address this, the project proposes a decentralized, real-time monitoring system using embedded controllers. An ESP32 generates three-phase AC signals for simulation, while an Arduino Mega monitors voltage and current through sensors. The system detects faults using threshold-based analysis of RMS values, automatically isolates faulty phases using relays, and sends instant alerts via GSM communication.

The methodology involves continuous sampling of electrical parameters, fault classification based on deviations, and immediate protective action. Simulation and hardware testing confirm accurate fault detection, fast response, and reliable communication under various fault conditions.

The system offers advantages such as improved safety, reduced downtime, low cost, and scalability. It is suitable for rural power distribution, industrial applications, and smart grid systems. Future enhancements include IoT integration, cloud-based monitoring, advanced analytics, and improved communication technologies for wider deployment.

Conclusion

This paper presented the design and implementation of a smart overhead line fault detection and automatic trip protection system aimed at improving safety and reliability in power distribution networks. The proposed system integrates ESP32-based three-phase sinusoidal pulse width modulation generation with Arduino Mega–based real-time monitoring of voltage and current parameters. By continuously analyzing electrical quantities and comparing them with predefined safety thresholds, the system effectively identifies open-circuit faults, line-to-line faults, and line-to-ground faults. The coordinated operation of sensing units, embedded controllers, relay mechanisms, and communication modules ensures accurate detection and immediate protective action under abnormal conditions. The automatic relay-based isolation mechanism significantly reduces fault duration and minimizes the risk of equipment damage, electric shock incidents, and fire hazards. The inclusion of GSM-based remote alerting further enhances operational efficiency by enabling instant communication of fault details to authorized personnel. Simulation and hardware validation confirm that the system performs reliably under both normal and fault conditions. The developed prototype demonstrates the practical feasibility of implementing intelligent, decentralized protection systems at the distribution level. With further enhancement and scalability, the proposed design can serve as a foundational framework for advanced smart grid applications and modern power system automation.

References

[1] Hinojosa-Palafox, E. A., Rodríguez-Elías, O. M., Pacheco-Ramírez, J. H., Hoyo-Montaño, J. A., Pérez-Patricio, M., & Espejel-Blanco, D. F. (2024). A novel unsupervised anomaly detection framework for early fault detection in complex industrial settings. IEEE Access, 12, 181823–181839. [2] B. Dowdeswell, R. Sinha, and S. G. MacDonell, ‘‘Finding faults: A scoping study of fault diagnostics for industrial cyber–physical systems,’’ J. Syst. Softw., vol. 168, Oct. 2020, Art. no. 110638, doi: 10.1016/j.jss.2020.110638. [3] B. Dowdeswell, R. Sinha, and S. G. MacDonell, “Finding faults: A scoping study of fault diagnostics for industrial cyber–physical systems,” Journal of Systems and Software, vol. 168, 110638, Oct. 2020. [4] H. Khorasgani, A. Farahat, and C. Gupta, ‘‘Data-driven residual generation for early fault detection with limited data,’’ in Proc. Annu. Conf. PHM Soc., Nov. 2020, vol. 12, no. 1, p. 9, doi: 10.36001/phmconf.2020.v12i1.1162. [5] S. Jeschke, C. Brecher, T. Meisen, D. özdemir, and T. Eschert, Industrial Internet of Things and Cyber Manufacturing Systems, 2017, pp. 3–19, doi: 10.1007/978-3-319-42559-7_1. [6] F. Zezulka, P. Marcon, I. Vesely, and O. Sajdl, ‘‘Industry 4.0—An introduction in the phenomenon,’’ IFAC-PapersOnLine, vol. 49, no. 25, pp. 8–12, 2016, doi: 10.1016/j.ifacol.2016.12.002. [7] N. Jazdi, ‘‘Cyber physical systems in the context of Industry 4.0,’’ in Proc. IEEE Int. Conf. Autom., Quality Test., Robot., May 2014, pp. 1–4. [8] X. Dai and Z. Gao, ‘‘From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis,’’ IEEE Trans. Ind. Informat., vol. 9, no. 4, pp. 2226–2238, Nov. 2013, doi: 10.1109/TII.2013.2243743.

Copyright

Copyright © 2026 Vishnu S Nair, Sumayya Anwar, Nizamudheen Reji, Muhammed Safar, Rincy Raphael, Meenu Gibi. 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.