Open Access

ARTICLE

IoT Based Transmission Line Fault Classification Using Regularized RBF-ELM and Virtual PMU in a Smart Grid

Kunjabihari Swain¹, Murthy Cherukuri^1,*, Indu Sekhar Samanta², Bhargav Appasani^3,*, Nicu Bizon^4,5, Mihai Oproescu⁴

1 Department of Electrical and Electronics Engineering, NIST University, Berhampur, 761008, India
2 Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha-O-Anusandhan University, Bhubaneswar, 751030, India
3 School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, 751024, India
4 Piteşti University Centre, The National University of Science and Technology POLITEHNICA Bucharest, Pitesti, 110040, Romania
5 ICSI Energy, National Research and Development Institute for Cryogenic and Isotopic Technologies, Râmnicu Vâlcea, 240050, România

* Corresponding Authors: Murthy Cherukuri. Email: ; Bhargav Appasani. Email:

(This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)

Computer Modeling in Engineering & Sciences 2025, 145(2), 1993-2015. https://doi.org/10.32604/cmes.2025.067121

Received 25 April 2025; Accepted 15 October 2025; Issue published 26 November 2025

Abstract

Transmission line faults pose a significant threat to power system resilience, underscoring the need for accurate and rapid fault identification to facilitate proper resource monitoring, economic loss prevention, and blackout avoidance. Extreme learning machine (ELM) offers a compelling solution for rapid classification, achieving network training in a single epoch. Leveraging the Internet of Things (IoT) and the virtual instrumentation capabilities of LabVIEW, ELM can enable the swift and precise identification of transmission line faults. This paper presents a regularized radial basis function (RBF) ELM-based fault detection and classification system for transmission lines, utilizing a LabVIEW based virtual phasor measurement unit (PMU) and IoT sensors. The transmission line fault is identified using the phaselet algorithm applied to the phase current acquired from the virtual PMU. Classification is then performed using the ELM algorithm. The proposed methodology is validated in real-time on a practical transmission line, achieving an accuracy of 99.46%. This has the potential to significantly influence future fault detection strategies incorporating virtual PMUs and machine learning.

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Keywords

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