Feature Engineering and Classification Models for Partial Discharge in Power Transformers

TL;DR

This paper develops a simple, fixed-size feature set from PD signals for power transformer fault classification, achieving 99% accuracy with ensemble methods, surpassing phase-based features.

Contribution

It introduces a novel, concise feature set for PD classification and demonstrates the effectiveness of ensemble models in improving accuracy and stability.

Findings

Achieved 99% classification accuracy.

Fixed-size feature set simplifies analysis.

Ensemble models outperform individual classifiers.

Abstract

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.