A real-time DC faults diagnosis in a DC ring microgrid by using derivative current based optimal weighted broad learning system

Abstract

This paper presents a novel approach for DC faults diagnosis in renewables based DC-ring microgrid (DC-RM). The proposed novel approach consists of a second-order derivative current (SODC) approach, which is adequate for isolation of faults due to its accurate fault detection and faster response during fault. Moreover, this approach is also used to estimate the fault location using cable parameters. On the other hand, most powerful weighted broad learning system (WBLS) is developed for faults classification. However, WBLS has a major challenge i.e., random generation of weights or fixed weights for the input data, which is connected to WBLS network. To address that and generate the suitable weights according to the statistical feature data, optimized WBLS is developed by hybridizing the sine–cosine algorithm (SCA) and chaotic salp swarm algorithm (CSSA), called SC-CSSA-WBLS. Here, the most influential features are used to extract the data from the simulated DC fault current signals. The role of SSA and SCA in the WBLS is to enhance the initialization of population, convergence speed, and search capabilities of both local exploitation and global exploration. Further, the efficacy of the proposed algorithm is validated during different case studies and its superiority is evidenced in terms of detection time, relative computational time, classification accuracy, and relative error when compared to state-of-the-art techniques in MATLAB/Simulink environment. Finally, a real-time hardware setup is implemented using dSPACE DS1104 embedded processor to isolate the various faults that occurred on the DC microgrid accurately using adaptive threshold strategy of SODC approach.