Complementary Virtual Mirror Fault Diagnosis Method for Microgrid Inverter

The data loss and corresponding false fault features of single-phase or multiphase detection signals caused by sensors is a relatively troublesome problem, which can increase the difficulty to the fault diagnosis of switch fault and open-phase fault of inverter, and even can lead to false alarm. For these problems, a complementary virtual mirror fault diagnosis method for microgrid inverter is proposed to improve them. First, the virtual mirrors are constructed, which contain fault information in different angle. Second, a cross comparison processing method is used to gain the cross variables and corresponding mirror cross variables by the obtained virtual mirrors and detected signals, which can extract and normalize fault components from different angle in the same way, respectively. Thus, it can reduce the impact of data loss. Third, the self-correction fault degree (scf) and averages fault degree (af) are calculated. Specially, scf can further reduce the influence of data loss and false fault features through the complementarity of cross variables and mirror cross variables. For af, it is a steady-state expression of fault degree, which can reduce the fluctuation of fault degree variable and the possibility of false alarm. Then, fault detection is implemented through the joint decision of scf and af. Finally, the fault is located by the fault detection results and the extracted mirror complementary location information. Compared with the existing fault diagnosis methods, it not only can diagnose switch fault of inverter, but also can have certain robustness for single-phase or multiphase data loss. Meanwhile, it also has relatively fewer parameters and the lower difficulty of debugging, which are conducive to practical application. The effectiveness of the proposed method is validated by the experiment results.