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PCA and Autoencoder-Based ANN Models for Transformer Fault Diagnosis Using Dissolved Gas Analysis: Comparative Insights and Challenges

Accurate fault diagnosis of power transformers using Dissolved Gas Analysis (DGA) depends on effective feature extraction to reduce redundancy and improve classification performance. This study compares linear and nonlinear feature extraction methods viz. Principal Component Analysis (PCA) and bottl...

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Principais autores: Mwamba S. Nkwambe, Bonginkosi A. Thango
Format: Artigo
Sprog:Inglês
Udgivet: MDPI AG 2026-06-01
Serier:Energies
Fag:
power transformer
dissolved gas analysis
fault diagnosis
principal component analysis
artificial neural network
IEC 60599
Online adgang:https://www.mdpi.com/1996-1073/19/12/2806
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