A Framework for Evaluating Automatic Classification of Underlying Causes of Disturbances and Its Application to Short-Circuit Faults

Most works in power systems event classification concern classifying an event according to the morphology of the corresponding waveform. An important and even more difficult problem is the classification of the event underlying cause. However, the lack of labeled data is more problematic in this second scenario. This paper proposes a framework based on frame-based sequence classification (FBSC), the Alternative Transient Program (ATP), and a public dataset to advance research in this area. As a proof of concept, a thorough evaluation of automatic classification of short circuits in transmission lines is discussed. Simulations with different preprocessing (e.g., wavelets) and learning algorithms (e.g., support vector machines) are presented. The results can be reproduced at other sites and elucidate several tradeoffs when designing the front end and pattern recognition stages of a sequence classifier. For example, when considering the whole event in an offline scenario, the combination of the raw front end and a decision tree is competitive with wavelets and a neural network.