Islanding Detection in DC Microgrids Based on Empirical Mode Decomposition

Abstract

Improved efficiency, enhanced integration with distributed generations (DGs), and the proliferation of DC loads in power systems have driven a surge in the adoption of DC microgrids (DCMGs). However, unintentional islanding poses a significant threat to these microgrids, necessitating robust islanding detection techniques to safeguard personnel, equipment and system stability. This paper proposes two novel passive islanding detection methods for DCMGs, explicitly addressing the non-detection zone (NDZ). The first proposed method involves collecting voltage samples at the DG terminals and computing their empirical mode decomposition (EMD). Subsequently, intrinsic mode functions (IMFs) are extracted, and the energy in each window is calculated to establish a diagnosis. To effectively distinguish islanding from other disturbances and faults, various scenarios are investigated. The second method is predicated on ensemble empirical mode decomposition (EEMD). Following the collection of voltage samples from DG terminals, white noise is introduced to these samples. The method subsequently extracts IMFs through averaging and quantifies the energy content within each window. The proposed methods are validated through simulations in Simulink/MATLAB. The obtained results demonstrate the methods’ effectiveness in achieving fast and accurate islanding detection.