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• High impedance fault detection in multi-feeder radial distribution system. • Detection and classification of faults and faulty line identification in presence of wind farm. • Power spectral density of current signals used to detect fault. • Wavelet covariance matrix used to calculate power spectral density. The recent progress in the area of signal processing has led to the development of intelligent schemes for fault classification and faulty phase selection in distribution system. The conventional algorithms find limitations to detect high impedance fault (HIF) and HIF remains a great concern for utilities as it can cause serious danger and accident if not detected properly. This study presents an algorithm for the detection and classification of HIF in a multi-feeder radial distribution system based on the calculation of power spectral density of faulty current signals. Discrete wavelet transform has been used to decouple the time information from the frequency information to detect and classify HIF effectively. The proposed technique has been extensively assessed under various dynamic situations including the presence of distributed generation and nonlinear load. This technique does not involve any training or learning process nor it involves any concern about data synchronization. The fault detection time is improved as it requires only half cycle of post fault current signal to detect and classify faults successfully. It is noteworthy to mention that the proposed technique considers only one end terminal current data. The comparative assessment reveals that this method can overcome the limitations of other existing techniques. [ABSTRACT FROM AUTHOR]