A Robust Transform-Domain Deep Convolutional Network for Voltage Dip Classification
Journal article, 2018

This paper proposes a novel method for voltage dip classification using deep convolutional neural networks. The main contributions of this paper include: 1) to propose a new effective deep convolutional neural network architecture for automatically learning voltage dip features, rather than extracting hand-crafted features; 2) to employ the deep learning in an effective twodimensional (2-D) transform domain, under space-phasor model (SPM), for efficient learning of dip features; 3) to characterize voltage dips by 2-D SPM-based deep learning, which leads to voltage dip features independent of the duration and sampling frequency ofdiprecordings; and 4) to develop robust automatically-extracted features that are insensitive to training andtest datasets measured from different countries/regions. Experiments were conducted on datasets containing about 6000 measured voltage dips spread over seven classes measured from several different countries. Results have shown good performance of the proposed method: average classification rate is about 97% and false alarm rate is about 0.50%. The test results from the proposed method are compared with the results from two existing dip classification methods. The proposed method is shown to outperform these existing methods

convolutional neural network.

machine learning

Power quality

voltage dip

deep learning

Author

Azam Bagheri

Luleå University of Technology

Irene Yu-Hua Gu

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

Math Bollen

Lureå University of Technology

Ebrahim Balouji

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

IEEE Transactions on Power Delivery

0885-8977 (ISSN)

Vol. 33 6 2794-2802

Deep learning for big power system data

Swedish Energy Agency, 2017-01-01 -- 2020-12-31.

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1109/TPWED.2018.2854677

More information

Latest update

1/3/2019 1