Hybrid Condition Monitoring for Power Electronic Systems

This paper proposes a novel approach for condition monitoring of power electronic systems. When monitoring the state of a power system, reliability is crucial, as this type of system is usually operated continuously for long periods of time, and as both missed faults as well as false detections can easily become prohibitively expensive. Recently, machine-learning-based methods for fault detection of power systems have gained popularity, since they can overcome many of the constrains of model-based techniques. Most of these methods train classifiers for different states of the system under test, and thus, the problem of fault detection becomes a problem of classification. In this paper we compare two of such recent techniques. We show that despite good results, it cannot reasonably be expected that the state classification is solved perfectly for every instant of time, which makes the application of such classifiers infeasible in practical systems. In order to overcome these issues, we propose to re-formulate the task into one of hybrid—neural and statistical—cross-temporal hypothesis testing. This novel hybrid framework allows us to build upon the previous machine-learning-based classification approaches, and to achieve full reliability on a challenging dataset of fault monitoring measurements for a buck-converter.