Detection and Classification of Defects in XLPE Power Cable Insulation via Machine Learning Algorithms
Author:
Affiliation:
1. Texas A&M University,Department of Electrical and Computer Engineering, College Station,USA,TX 77843
2. Texas A&M University at Qatar,Department of Electrical and Computer Engineering,Doha,Qatar,23874
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9774031/9773331/09774113.pdf?arnumber=9774113
Reference35 articles.
1. UHF Partial Discharge Localization in Gas-Insulated Switchgears: Gradient Boosting Based Approach
2. Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method
3. Prediction of leakage current of non-ceramic insulators in early aging period
4. Investigation of pollution flashover on high voltage insulators using artificial neural network;gencoglu;Expert Syst Appl,2009
5. Investigation of Void Size and Location on Partial Discharge Activity in High Voltage XLPE Cable Insulation
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