Fault detection and classification of DC microgrid based on VMD

Abstract

Purpose This paper aims to present a new fault detection and classification scheme of both DC faults and AC faults on a DC microgrid network. Design/methodology/approach To achieve reliable protection, the derivative of DC current signal is decomposed into several intrinsic modes using variational mode decomposition (VMD), which are then used as inputs to the Hilbert–Haung transform technique to obtain the instantaneous amplitude and frequency of the decomposed modes of the signal. A weighted Kurtosis index is used to obtain the most sensitive mode, which is used to compute sudden change in discrete Teager energy (DTE), indicating the occurrence of the fault. A stacked autoencoder-based neural network is applied for classifying the pole to ground (PG), pole to pole (PP), line to ground (LG), line to line (LL) and three-phase line to ground (LLLG) faults. The effectiveness of the proposed protection technique is validated in MATLAB/SIMULINK by considering different test cases. Findings As the maximum fault detection time is only 5 ms, the proposed detection technique is very fast. A stacked autoencoder-based neural network is applied for classifying the PG, PP, LG, LL and LLLG faults with classification accuracy of 99.1%. Originality/value The proposed technique provides a very fast, reliable and accurate protection scheme for DC microgrid system.

Detection and localization of fault in DC microgrid using discrete Teager energy and generalized least square method

“…1–11), given in Table 4. Similarly, PP and ARC fault detection times are 1.2 ms and 10 ms, which are also lower as compared to detection times in Barik (2023, pp. 302–322) and Shamsoddini et al (2020, pp.…”

“…In proposed fault detection method, three types of fault detection like PG fault detection, PP fault detection and ARC fault detection have been carried out. It is observed that the detection time for PG fault is 2.4 ms, which is less as compared to the detection time in Barik (2023, pp. 302–322) and Shamsoddini et al (2020, pp.…”

“…531–544). DC fault detection and classification using VMD is discussed in literature (Barik, 2023, pp. 302–322).…”