Authors : Arunkumar E.; Lakshmipriya M.

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/87ck4aa8

Scribd : https://tinyurl.com/mrxhxcty

DOI : https://doi.org/10.38124/ijisrt/26May535

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Abstract : This work has proposed a novel hybrid technique, which incorporates load flow studies and Discrete Wavelet Transform (DWT) with the support vector machine for effective fault detection and classification in the IEEE 9-bus system. This hybrid approach resolves the issues faced by the traditional wavelet thresholding scheme due to its inability to employ machine learning techniques. This hybrid model incorporates DWT to extract features from the power signals and then uses SVM (Support Vector Machine) for effective fault classification. The simulation results obtained using MATLAB/Simulink have shown an increased accuracy level of fault detection in the range of 95.8% for the proposed approach whereas 87.3% is achieved through the conventional wavelet technique. The major faults detected and classified through this technique include: single line to ground (L-G), line to line (L-L), double line to ground (L-L-G), three phase (L-L-L), and three phase to ground (L-L-L-G).

Keywords : Fault Detection, Discrete Wavelet Transform (DWT), Support Vector Machine (SVM), IEEE 9-Bus System, Power System Protection, Machine Learning.

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