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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Transformer Fault Diagnosis Utilizing Feature Extraction and Ensemble Learning Model

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