Classification of HIF detection in NEV profile using wavelet transform and convolution neural network

High impedance faults (HIFs) present a critical challenge in power systems due to their subtle signal characteristics, which often remain undetected by conventional protection methods. These faults typically do not produce significant phase disturbances, making reliable detection difficult. However, analysis of the neutral-to-earth voltage (NEV) profile under fault conditions provides a promising alternative for fault identification. Existing approaches for detecting and classifying HIFs using NEV signals remain limited and may result in inaccurate maintenance decisions. This paper proposes a fault classification framework for multiple fault types, including HIF, three-phase fault, three-phase fault to ground, double line, double line to ground, and single line to ground, based on NEV profiles. Discrete wavelet transform is employed to extract discriminative features from the NEV signals, which are subsequently used as inputs to a convolutional neural network (CNN). For performance comparison, a long short-term memory (LSTM) network is also evaluated. To ensure robust and unbiased assessment, 5-fold cross-validation is adopted, and the models are tested under varying noise conditions of 40 dB, 30 dB, and 20 dB to examine noise resilience. Experimental results demonstrate that the CNN achieves a superior average classification accuracy of 96.93%, outperforming the LSTM, which attains 96%. The findings highlight the CNN’s enhanced robustness and reliability in classifying fault types based on NEV profiles, particularly for challenging HIF scenarios under noisy operating conditions.