Comparison of artificial neural network and multiple regression for partial discharge sources recognition
Defects; Linear regression; Neural networks; Regression analysis; Insulation defects; Multiple linear regressions; Multiple regressions; Offline; Partial discharge sources; Pd detections; PD measurements; Training and testing; Partial discharges
Saved in:
Main Authors: | Abubakar Masud A., Muhammad-Sukki F., Albarracin R., Alfredo Ardila-Rev J., Hawa Abu-Bakar S., Fadilah Ab Aziz N., Bani N.A., Nabil Muhtazaruddin M. |
---|---|
Other Authors: | 55330007200 |
Format: | Conference Paper |
Published: |
Institute of Electrical and Electronics Engineers Inc.
2023
|
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Artificial neural network application for partial discharge recognition: survey and future directions
by: Mas'ud, A. A., et al.
Published: (2016) -
Statistical error tolerances of partial discharge recognition rates
by: Mas'ud, Abdullahi Abubakar, et al.
Published: (2015) -
Artificial generation of partial discharge sources through an algorithm based on deep convolutional generative adversarial networks
by: Ardila Rey, J. A., et al.
Published: (2020) -
A comparison of inductive sensors in the characterization of partial discharges and electrical noise using the chromatic technique
by: Ardila-Rey, J. A., et al.
Published: (2018) -
Comparison of the performance of artificial neural networks and fuzzy logic for recognizing different partial discharge sources
by: Bani, N. A.
Published: (2017)