Condition Monitoring for Power Converters via Deep One-Class Classification

We introduce a novel hybrid approach for the early detection of power converter faults, focusing on the use case of modular multilevel converters. The proposed method is based on training a deep one-class classifier, which learns the characteristics of the normal system operation and can hence recognize deviations even without any training on potential fault conditions of the system. In order to achieve robust and reliable performance, the diagnosis of the system state utilizes short sequences of observations, which are combined through a probabilistic model. The decision about the system state can then take the form of monitoring the T2 test statistics, which allows us to control the maximum classification error. This proposed method, Reliability-guided One-Class Classification (ROCC) was tested on data recorded from a Modular Multilevel Converter. The approach is shown to be effective in all test cases, leading to reliable diagnostics even though the classifier is applied to a wide range of unseen conditions.