对比传统与深度学习模型在变压器故障检测中的表现。
Benchmarking Traditional Machine Learning and Deep Learning Models for Fault Detection in Power Transformers
- 用10个月监测数据，比较5种机器学习和4种深度学习模型。
- 随机森林达86.82%准确率，1D-CNN为86.30%，表现接近。
- 适合电力系统故障诊断研究者参考模型选择策略。
精准诊断电力变压器故障对保障电力系统稳定与安全至关重要。本研究对比了传统机器学习（ML）与深度学习（DL）算法在变压器故障分类中的表现。基于为期10个月的设备状态监测数据，提取气体浓度特征并进行归一化处理，训练了五种ML分类器：支持向量机（SVM）、K近邻（KNN）、随机森林（RF）、XGBoost 和人工神经网络（ANN）。同时评估了四种深度学习模型：长短期记忆网络（LSTM）、门控循环单元（GRU）、一维卷积神经网络（1D-CNN）和TabNet。实验结果表明，两类方法性能相当：随机森林取得最高ML准确率86.82%，1D-CNN达到86.30%的接近水平。
原文摘要 · Abstract (English)
Accurate diagnosis of power transformer faults is essential for ensuring the stability and safety of electrical power systems. This study presents a comparative analysis of conventional machine learning (ML) algorithms and deep learning (DL) algorithms for fault classification of power transformers. Using a condition-monitored dataset spanning 10 months, various gas concentration features were normalized and used to train five ML classifiers: Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest (RF), XGBoost, and Artificial Neural Network (ANN). In addition, four DL models were evaluated: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), One-Dimensional Convolutional Neural Network (1D-CNN), and TabNet. Experimental results show that both ML and DL approaches performed comparably. The RF model achieved the highest ML accuracy at 86.82%, while the 1D-CNN model attained a close 86.30%.
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