Power Quality Disturbance Detection Based on Improved Robust Random Cut Forest
Author:
Affiliation:
1. University of Queensland,School of Information Technology and Electrical Engineering
2. NOJA Power,Department of R&D,Brisbane,Australia
3. Queensland,Department of Renewables & Distributed Energy, Energy,Brisbane,Australia
Funder
Advance Queensland
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9948701/9949620/09949860.pdf?arnumber=9949860
Reference25 articles.
1. A Novel Approach to Arcing Faults Characterization Using Multivariable Analysis and Support Vector Machine
2. DT-CWT based event feature extraction for high impedance faults detection in distribution system
3. A hybrid intelligence approach for power quality disturbances detection and classification
4. High impedance fault detection using combination of multi-layer perceptron neural networks based on multi-resolution morphological gradient features of current waveform
5. Classification of Power Quality Disturbances Using Wigner-Ville Distribution and Deep Convolutional Neural Networks
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