Fuzzy Granulation Interval-Based Fault Diagnosis Method for Ring-Type DC Microgrid

Abstract

The lack of relevant protection schemes and standards brings significant challenges to the promotion of direct current microgrid (DCMG) technologies. The existing line fault diagnosis methods usually require additional measuring devices to obtain fault signals. Although reducing the number of sensors can lower the system costs, it also increases the difficulty of diagnosis. Pole-to-pole (PP) and positive pole-to-ground (PPG) fault classification under unknown fault resistance, and negative-to-ground (NPG) fault detection under unobvious positive pole current characteristics are two major difficulties. To solve the above issues, a fuzzy granulation interval (FGI) theory-based line fault diagnosis method is proposed in this paper. Only locally measured bus-side voltage and positive pole current signals shared with the converter controller inputs are required. Firstly, the fault characteristics and diagnosis difficulties are analyzed. Then a new NPG fault index interval range (IR) is designed to extract the abnormal post-fault voltage fluctuations. Moreover, the PP/PPG fault classification strategy is designed according to the interval characteristics and capacitor discharge process. Finally, the performance of the proposed method has been evaluated through MATLAB/Simulink simulations and hardware-in-the-loop (HIL) tests. The results demonstrate that the proposed method can accurately discriminate different line faults within 1.5 ms under different fault conditions.