A neural network approach to power transformer fault diagnosis

Diagnosis of power transformer abnormality is important for power system reliability. This paper introduces the dissolved gas-in-oil analysis (DGA) according to the characteristic of transformer fault diagnosis, based on fuzzy set theory and adaptive genetic algorithm, a neural network model for tra...

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Bibliographic Details
Main Authors: Fu Yang, Jin Xi, Lan Zhida
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Dissolved gas analysis
Fault diagnosis
Fuzzy set theory
Genetic algorithms
Neural networks
Power system modeling
Power system reliability
Power transformers
Testing
Online Access:Request full text
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Summary:Diagnosis of power transformer abnormality is important for power system reliability. This paper introduces the dissolved gas-in-oil analysis (DGA) according to the characteristic of transformer fault diagnosis, based on fuzzy set theory and adaptive genetic algorithm, a neural network model for transformer fault diagnosis is built by using modular back-propagation (BP). The results of training and testing show that the method is effective and available.