Novel Probabilistic Neural Network Models Combined with Dissolved Gas Analysis for Fault Diagnosis of Oil-Immersed Power Transformers
Author:
Affiliation:
1. College of Qianhu, Nanchang University, Nanchang 330031, China
2. College of Information Engineering, Nanchang University, Nanchang 330031, China
Funder
National Natural Science Foundation of China
Publisher
American Chemical Society (ACS)
Subject
General Chemical Engineering,General Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acsomega.1c01878
Reference33 articles.
1. Yu, S.; Zhao, D.; Chen, W.; Hou, H. Oil-immersed Power Transformer Internal Fault Diagnosis Research Based on Probabilistic Neural Network. ANT/SEIT, 2016.
2. Wavelet Networks in Power Transformers Diagnosis Using Dissolved Gas Analysis
3. Fault Diagnosis of a Power Transformer Using an Improved Frequency-Response Analysis
4. Dissolved gas analysis of thermal faults in transformer liquids simulated using immersed heating method
5. An integrated method of ANFIS and Dempster-Shafer theory for fault diagnosis of power transformer
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