Robust Fault Detection and Characterisation in AC Microgrids using Ensemble Empirical Mode Decomposition

Microgrids typically have a complex power system with multiple sources (i.e. PV solar panels, wind turbines, etc.), loads, and control systems. This increases the complexity of the protection coordination process, which requires a detailed understanding of the system’s behavior and dynamic characteristics. In addition, Microgrids are often located in rural or remote areas, making reliability and safety of critical importance. This requires a protection coordination system that can detect and respond to faults quickly and reliably. This paper presents a robust fault detection and characterization approach for AC Microgrids using ensemble empirical mode decomposition (EEMD). The approach is based on two-stage fault detection and classification. First, the EEMD-based fault detection algorithm is used to detect the presence of a fault. Secondly, the EEMD-based classification algorithm is used to identify the type of fault. The proposed approach is tested on a three-phase AC Microgrid system which is subjected to various fault conditions. The EEMD decomposes the current signal into a set of intrinsic mode functions (IMFs) and Residues. The Residuals are then analyzed to detect the fault signature and its type. The proposed approach has been tested in MATLAB/Simulink on a Modified IEEE 13-bus Microgrid model, and simulation results show that it is effective in protecting the system from any damage due to faults.