Rare events, especially those that could potentially negatively impact society, often require
humans' decision-making responses. Detecting rare events can be viewed as a prediction …

A comprehensive review on the methods used for fault detection, classification and location
in transmission lines and distribution systems is presented in this study. Though the three …

Most existing classification approaches assume the underlying training set is evenly
distributed. In class imbalanced classification, the training set for one class (majority) far …

Historical data sets for fault stage diagnosis in industrial machines are often imbalanced and
consist of multiple categories or classes. Learning discriminative models from such data sets …

Class imbalance is among the most persistent complications which may confront the
traditional supervised learning task in real-world applications. The problem occurs, in the …

In terms of machine learning-based power system dynamic stability assessment, it is
feasible to collect learning data from massive synchrophasor measurements in practice …

In the field of classification problems, we often encounter classes with a very different
percentage of patterns between them, classes with a high pattern percentage and classes …

Transient stability is important in power systems. Disturbances like faults need to be
segregated to restore transient stability. A comprehensive review of fault diagnosing …

In many real application areas, the data used are highly skewed and the number of
instances for some classes are much higher than that of the other classes. Solving a …

Rotating machinery plays a key role in mechanical equipment, and the fault diagnosis of
rotating machinery is a popular research topic. To overcome the dependency on expert …