TY - JOUR N2 - Detecting high impedance faults (HIFs) is one of the challenging issues for electrical engineers. This type of fault occurs often when one of the overhead conductors is downed and makes contact with the ground, causing a high-voltage conductor to be within the reach of personnel. As the wavelet transform (WT) technique is a powerful tool for transient analysis of fault signals and gives information both on the time domain and frequency domain, this technique has been considered for an unconventional fault like high impedance fault. This paper presents a new technique that utilizes the features of energy contents in detail coefficients (D4 and D5) from the extracted current signal using a discrete wavelet transform in the multiresolution analysis (MRA). The adaptive neurofuzzy inference system (ANFIS) is utilized as a machine learning technique to discriminate HIF from other transient phenomena such as capacitor or load switching, the new protection designed scheme is fully analyzed using MATLAB feeding practical fault data. Simulation studies reveal that the proposed protection is able to detect HIFs in a distribution network with high reliability and can successfully differentiate high impedance faults from other transients. L1 - http://czasopisma.pan.pl/Content/121585/PDF-MASTER/art10.pdf L2 - http://czasopisma.pan.pl/Content/121585 PY - 2021 IS - No 4 EP - 886 DO - 10.24425/aee.2021.138267 KW - high impedance fault (HIF) KW - multiresolution analysis (MRA) KW - overcurrent relay KW - discrete wavelet transform (DWT) A1 - Suliman, Mohammed Yahya A1 - Alkhayyat, Mahmood Taha PB - Polish Academy of Sciences VL - vol. 70 DA - 2021.11.30 T1 - High impedance fault detection in radial distribution network using discrete wavelet transform technique SP - 873 UR - http://czasopisma.pan.pl/dlibra/publication/edition/121585 T2 - Archives of Electrical Engineering ER -