Islanding detection in different configurations of multiple photovoltaic distributed generation‐based direct current microgrid using improved mode decomposition technique

Abstract

A novel approach is presented in this paper for the detection of islanding disturbances in a multiple photovoltaic distribution generation-based radial and mesh types DC microgrid using adaptive variational mode decomposition (AVMD) hybridized with detrended fluctuation analysis (DFA). Here, the current signals from the DC bus are extracted and processed through AVMD to yield a set of intrinsic mode functions (IMFs). The optimal selection of VMD parameters like the number of modes K and penalty factor (γ) is achieved using improved particle swarm optimization sine cosine levy flight algorithm that considers minimum mean envelope entropy as its objective function. Further from the set of optimal modes, the most significant IMFs are selected using an effective weighted kurtosis index based on a chosen threshold value. For the classification of islanding and non-islanding events, DFA takes the significant IMFs as its inputs to yield three scaling exponents (α) values according to the window size setup. The α values play a key role in distinguishing various islanding and non-islanding events occurring on the DC microgrid under various topological changes in two-dimensional and three-dimensional scatter plots. The efficacy and superiority of the proposed system are validated by classification accuracy and relative computational time in comparison to the existing methods. The entire study is carried out in MATLAB/Simulink platform.