An intelligent protection scheme for DC microgrid using Hilbert–Huang transform with robustness against PV intermittency and DER outage

This paper presents a robust scheme to detect and isolate faults quickly to prevent significant damage to the DC microgrid. The proposed technique uses the joint framework of Hilbert–Huang transform and empirical mode decomposition for feature extraction and bagging tree classifier to accurately and swiftly identify DC faults, which is challenging due to the limited time available to interrupt them. The intermittency pertaining to PV source and outage of distributed energy resources (DERs) may further complicate the protection task. In this regard, this paper proposes an intelligent scheme for fast fault detection and classification in DC microgrid. The joint framework of Hilbert transform and empirical mode decomposition has been used to calculate discriminatory attributes for characterizing the fault behavior in the signal. The ensemble strategy of efficient bagging tree classifier has been exploited after extensive testing and comparison with other modern approaches in this framework. Compared to other methods, the proposed scheme is more precise and faster which ascertain its efficacy in providing resilient protection to the DC microgrid with immunity to stochastic behavior pertaining to weather intermittency and DER outage. The performance of developed protection technique has also been validated on OPAL-RT digital simulator for authenticating its applicability in field applications.