TY  - EJOU
AU  - Swain, Kunjabihari 
AU  - Cherukuri, Murthy 
AU  - Samanta, Indu Sekhar 
AU  - Appasani, Bhargav 
AU  - Bizon, Nicu 
AU  - Oproescu, Mihai 

TI  - IoT Based Transmission Line Fault Classification Using Regularized RBF-ELM and Virtual PMU in a Smart Grid
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 145
IS  - 2
SN  - 1526-1506

AB  - 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.
KW  - Phasor measurement units; power system protection; situational awarenes; phaselet; fault classification; extreme learning machine

DO  - 10.32604/cmes.2025.067121