ANN and cross-correlation based features for discrimination between electrical and mechanical defects and their localization in transformer winding
Author:
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/94/6927323/06927368.pdf?arnumber=6927368
Cited by 84 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. A new approach towards more accurate modeling of mechanical defects in power transformer windings;Measurement;2025-01
2. A New Improved Methodology to Identify an Interturn Fault Location in Transformer Winding Based on Fault Location Factor;IEEE Transactions on Industrial Electronics;2024-11
3. Classifying transformer winding deformation type by combination of FRA polar plot texture feature and multiple SVM classifiers;Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023);2024-05-13
4. Application of generative AI-based data augmentation technique in transformer winding deformation fault diagnosis;Engineering Failure Analysis;2024-05
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