TSSA-Based Multifault Diagnosis Algorithm for Microgrid Inverter

In industrial applications, the fault diagnosis of equipment often have a lot of complex interferences. Especially, the uncertainty and data loss of detection signals caused by sensors will greatly increase the interference and difficulty for the microgrid system of inverter fault diagnosis, and even result in false alarm and erroneous triggering of protection units. In order to solve the problem, a fault diagnosis algorithm based on topology self-similarity assessment for microgrid inverter is proposed. First, the data nucleus topology extraction method is used to extract nucleus topologies of detection signals, which can effectively reflect the key information of the different data state. Meanwhile, it can greatly reduce the startup amount of diagnosis method and corresponding calculation. Second, the topology virtual mirrors are reconstructed by the extracting nucleus topologies, which can effectively reduce the influence of data loss. Third, the obtained nucleus topologies and topology virtual mirrors are used for periodic self-similarity assessment to extract the corresponding period fault degree variables, respectively, which can effectively avoid the influence of signal bias and amplitude change, and are sensitive to the continuous fault feature of inverter. Further, the obtained period fault degree variables are used for fault detection to obtain dual detection results. Finally, the fault detection and location of corresponding phase are realized by the dual detection results and location information. The effectiveness of the proposed algorithm is validated by the detailed experiment results and comparison.