Islanding detection in photovoltaic based DC micro grid using adaptive variational mode decomposition and detrended fluctuation analysis

Abstract This study presents a novel approach using adaptive variational mode decomposition with detrended fluctuation analysis to detect the islanding disturbances for photovoltaic based DC micro grid. DC parameters are simple to estimate in comparison to AC profile. Thus DC parameters are recorded under islanding scenario, and processed through proposed adaptive variational mode decomposition which decomposes the signals into intrinsic mode functions. These segregated intrinsic mode functions are further selected optimally by choosing the significant weighted kurtosis index. This optimal selection (maximisation of weighted kurtosis index) is ensured by modified particle swarm optimisation in terms of number of modes (K) and penalty factor (σ). For detection and monitoring (D&M) accurate islanding scenario the significant intrinsic mode functions are subjected to detrended fluctuation analysis, where power exponent (α) values are utilised for correct detection (i.e. distinguishing islanding out of other grid contingencies by two and three dimensional scattering plots). The effectiveness of the proposed D&M for DC micro grid is established in this paper in terms of classification accuracy and relative computational time. The proposed DC side islanding D&M method is less complex (as compared to AC signals) to be implemented. Fastness and accuracy of proposed D&M is established and performed in MATLAB/Simulink platforms.