Detection of faults and DG islanding in PV-Wind DC ring bus microgrid by using optimized VMD based improved broad...

This paper presents a novel approach for the detection and classification of photovoltaic with wind based DC ring bus microgrid DC faults and DG (distributed generation) islanding events. This novel approach consists of adaptive variational mode decomposition (AVMD) and an improved broad learning system (IBLS). Initially, DC fault current signals are captured from the DC bus under different operating conditions and processed through the AVMD to decompose the signals into intrinsic mode functions (IMFs). The VMD is made adaptive by minimizing the objective function of the L-kurtosis index for optimal modal number (K) and penalty factor (σ) through the improved whale optimization (IWO) algorithm. From the optimal IMFs, the most significant IMFs are chosen based on the threshold of the L-kurtosis index, and they are passed through statistical features to extract efficient data. Further, the training and testing of this data set is carried out through IBLS for obtaining the accurate detection and discrimination of DC faults. The conventional BLS method is improved through elastic net ridge regression for calculating the weights. The effectiveness of the proposed AVMD based IBLS algorithm is verified by its superiority in terms of relative computation time (RCT), classification accuracy (CA) with the confusion matrix, and their performance indices by comparing with other existing methods under different case studies. Finally, the simplicity and practicability of the proposed work are tested and implemented in the dSPACE 1104 embedded processor.