Hybrid Condition Monitoring for Power Converters: Learning-Based Methods With Statistical Guarantees

In this paper, a new learning-based method is proposed for the early detection of changes of parameters in power converters. It circumvents the pertinent shortcomings of previous model-based methods, such as their need for acquiring switching signals, dependence on the control method, or the need for isolating a part of the system during monitoring and thus interfering with the system performance or its start-up time. Additionally, a downside of the learning-based approaches, which is that their performance depends on the quality of the measurements, is addressed through constructing hybrid models that combine the benefits of both lines of development. Our approaches are evaluated with several types of features based on wavelet decomposition and empirical mode decomposition. Using these, an ANN-based classifier is trained for fault detection. For achieving the final decision on the presence or absence of a fault state, we propose the use of sequential hypothesis testing. This hybrid approach yields a much higher reliability than instantaneous ANN-based classification, while allowing for a statistically sound cross-temporal information integration and providing a controllable error bound for the probability of misclassifications. The proposed method was evaluated on two data-sets recorded from a buck converter and an arm of a modular multilevel converter. The results show that, for both systems, the proposed method is capable of reliably recognizing changes of the system parameters. The potential for practical applications is shown through an implementation on a low-power-consumption microcontroller.