In this paper, we address the challenge of learning with limited fault data
for power transformers. Traditional operation and maintenance tools lack
effective predictive capabilities for potential faults. The scarcity of
extensive fault data makes it difficult to apply machine learning techniques
effectively. To solve this problem, we propose a novel approach that leverages
the knowledge graph (KG) technology in combination with gradient boosting
decision trees (GBDT). This method is designed to efficiently learn from a
small set of high-dimensional data, integrating various factors influencing
transformer faults and historical operational data. Our approach enables
accurate safe state assessments and fault analyses of power transformers
despite the limited fault characteristic data. Experimental results demonstrate
that this method outperforms other learning approaches in prediction accuracy,
such as artificial neural networks (ANN) and logistic regression (LR).
Furthermore, it offers significant improvements in progressiveness,
practicality, and potential for widespread application.

应用知识图谱和梯度提升决策树技术，我们提出了一种能够有效学习少量高维数据的方法，用于电力变压器的安全状态评估和故障分析，取得了相比于神经网络和逻辑回归等其他方法更高的预测准确性，并在进步性、实用性和广泛应用潜力方面有显著改进。

基于知识图谱的电力变压器故障预测

Power Transformer Fault Prediction Based on Knowledge Graphs

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.

使用相态分解部分放电信号数据来识别局部放电，建立了一套包括总量级、最大值级和最长空区段长度等特征的分类模型，采用随机森林和支持向量机分类方法，在分类准确率方面取得了显著的成果，同时开发了一个叠加集成模型，结果表明该模型在准确性和方差方面均优于现有方法。