  To ensure reliability, power transformers are monitored for partial discharge
(PD) events, which are symptoms of transformer failure. Since failures can have
catastrophic cascading consequences, it is critical to preempt them as early as
possible. Our goal is to classify PDs as corona, floating, particle, or void,
to gain an understanding of the failure location. Using phase resolved PD
signal data, we create a small set of features, which can be used to classify
PDs with high accuracy. This set of features consists of the total magnitude,
the maximum magnitude, and the length of the longest empty band. These features
represent the entire signal and not just a single phase, so the feature set has
a fixed size and is easily comprehensible. With both Random Forest and SVM
classification methods, we attain a 99% classification accuracy, which is
significantly higher than classification using phase based feature sets such as
phase magnitude. Furthermore, we develop a stacking ensemble to combine several
classification models, resulting in a superior model that outperforms existing
methods in both accuracy and variance.
