An Unsupervised Fault Detection and Classification Scheme of Power Converters

Faults in a power electronic component (inverter, converter, modules, etc.) can severely affect the reliability, efficiency, as well as the security of the entire power conversion system if not detected by early warning. The inverters that are the most common power converters in the industry are widely used in power supplies, energy management systems, motor control, etc. Nevertheless, due to the diverse operating conditions and complexity of the drive system, the power converters employed in drive systems are more prone to faults. Moreover, collecting a large amount of labeled data in the nonlinear industrial environment is not practical, and this imposes challenges to the prior approaches as they claim a bulk amount of labeled data to be generalized. Taking this as a research challenge, this letter proposes a sparse-autoencoder-based unsupervised power converter fault detection and classification scheme that learns the fault-oriented features from the unlabeled dataset. In addition, the precision and sensitivity study are conducted in this letter to justify whether the proposed scheme delivers a reliable performance or not.