Submitted by Nuvia A. Pacheco on Wed, 10/23/2019 - 11:29
| Title | Identification of Partial Discharge Defects Based on Deep Learning Method |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Authors | Duan L., Hu J., Zhao G., Chen K., He J., Wang S.X |
| Journal | IEEE Transactions on Power Delivery |
| Volume | 34 |
| Pagination | 1557-1568 |
| Date Published | Aug |
| Keywords | critical defects, current probe, current waveforms, Data visualization, Deep learning, deep learning method, detecting pulse, Discharges (electric), electrical equipment, Electrodes, Feature extraction, IEC standards, insulation breakdown, learning (artificial intelligence), modified IEC-60270 experiment platform, operation life, Partial discharge, partial discharge defects, partial discharges, PD current waveform, PD defects, power engineering computing, principle component analysis, repetitive partial discharge, softmax, softmax layer, sparse autoencoder, sparse autoencoder layer, stochastic neighbor embedding methods, Surface discharges, traditional identifying method |
| DOI | 10.1109/TPWRD.2019.2910583 |
