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• Detection and classification of faults including high impedance faults in microgrid. • Fault detection and classification from current signals at the substation. • Faulty feeder and sub-feeder identification within half cycle of post fault current signal. • Wavelet based power spectral density of current signals utilized to detect and classify fault and also identify faulty feeder. • Performance of the proposed algorithm verified under dynamic and challenging situations. The recent progress in the area of microgrid protection has led to the application of data-driven and signal processing techniques for fault detection in microgrids. The deployment of inverter interfaced distributed generators makes the traditional protection schemes ineffective. In this paper a wavelet based power spectral density (PSD) has been utilized to detect fault, identify faulty phases and locate fault in the microgrid. Wavelet based PSD decouples the frequency information from the time information and it is the optimal technique to reveal the change of distribution of harmonics in non-stationary faulty signals as for fault analysis it can consider the faulty frequency band instead of one particular harmonic. The proposed technique can detect fault and identify faulty phases by analyzing current signals from the substation only. The normalized value of PSD of three phase current signals has been considered for fault detection and probabilistic distribution of PSD has been considered for faulty phase identification. The normalized value of PSD of the current signals collected from both ends of the main feeders and one end of the non-DG sub-feeder have been used to locate the faulty feeder and sub-feeder. Appropriate threshold values have been considered to detect fault and to locate the faulty feeder. To locate faults, the technique does not require synchronized signals and thus provides a promising and economical solution for inverter interfaced microgrid. It does not require huge amount of data set for training and testing as it is essentially a threshold based technique and also does not require collection of data at high sampling rate. The proposed scheme has been extensively tested for high impedance fault (HIF), unbalanced loading, load switching, variation of DG parameters, measurement uncertainty and presence of noise in the signal with signal to noise ratio up to 10 dB. This technique provides accurate results for fault resistance from 0 Ω to 300 Ω and up to 10% measurement error. The performance of the proposed technique has been verified for feeder length variation up to 20%. Half cycle of current signal only after fault is required and three levels of wavelet decomposition has been considered. The simulation results and comparative assessment demonstrate the efficacy of the proposed scheme. [ABSTRACT FROM AUTHOR]

• 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]