Transformer Fault Diagnosis Utilizing Feature Extraction and Ensemble Learning Model

Transformer Fault Diagnosis Utilizing Feature Extraction and Ensemble Learning Model

This paper proposes a novel method for diagnosing faults in oil-immersed transformers, leveraging feature extraction and an ensemble learning algorithm to enhance diagnostic accuracy. Initially, Dissolved Gas Analysis (DGA) data from transformers undergo a cleaning process to ensure data quality and...

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Bibliographic Details
Main Authors: Gonglin Xu, Mei Zhang, Wanli Chen, Zhihui Wang
Format: Article
Language:English
Published: MDPI AG 2024-09-01
Series:Information
Subjects:
oil-immersed transformer
fault diagnosis
feature extraction
Atomic Orbital Search algorithm
LightGBM model
Online Access:https://www.mdpi.com/2078-2489/15/9/561
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