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DC microgrids present an effective power system solution for increased integration of renewable sources while providing clear benefits, such as high efficiency and simpler control. However, the protection of DC networks still remains a challenge due to strict time limits for fault interruption imposed by fast rising fault currents in DC systems, and absence of frequency and phasor information. This paper introduces a technique for fast detection and isolation of the faults in the DC microgrids without de-energizing the whole network. In the proposed algorithm, branch current measurements are sampled and Wavelet transform is applied to capture the characteristic changes in the current signals caused by network faults. The temporal variations in the relative wavelet energy within the frequency bands are acquired to construct the feature vector for classification. Artificial neural networks are used as the classifier as it provides a soft criterion for fault detection, featuring smart fault detection capability. The relatively fast calculation time of artificial neural networks makes it a good candidate for this application, due to the strict time restrictions inherited in DC fault isolation. To evaluate the performance, a comprehensive study on the proposed scheme is presented. The results demonstrate the effectiveness of the proposed scheme in terms of fast and reliable fault detection and inbuilt accurate fault localization capability.

Wavelet-Multi Resolution Analysis Based ANN Architecture for Fault Detection and Localization in DC Microgrids