Study on a hybrid algorithm for accurate ripple detection in DC microgrids

Abstract

AbstractWith the advantages of the low cost of transmission lines and high efficiency, the DC microgrid has become a rising star in the low‐voltage network. However, multi‐source and multi‐transformation characteristics in DC microgrids will lead to the existence of various forms of ripple generation sources in the system. The existence of a large amount of ripple not only directly cause the reduction of power quality of DC microgrids, but also decreases the accuracy of electricity billing. It is vital to achieving accurate ripple detection for the reliable power supply of the DC microgrid. A DC‐side ripple detection method, which combines the Whale Optimization Algorithm (WOA), Variational Modal Decomposition (VMD), and Hilbert Transform (HT), is therefore investigated and proposed here. Firstly, the sample entropy (SampEn) is used as the fitness function, applied the whale optimization algorithm, is to determine the optimal decomposition scale and penalty factor, of the variational modal decomposition; then, the ripple‐containing DC signal is decomposed into a series of eigenmodal components (imf) by the optimized variational modal decomposition algorithm. Finally, the Hilbert Transform is performed to obtain the amplitude and frequency of the ripple components. To prove the effectiveness, the proposed method is compared with the conventional Empirical Mode Decomposition (EMD) and the VMD algorithm, by testing simulated DC signals without and with noise. Results show that the proposed method has higher accuracy and noise robustness than the EMD and VMD algorithms.