Next Article in Journal
Study on Quantitative Correlations between the Ageing Condition of Transformer Cellulose Insulation and the Large Time Constant Obtained from the Extended Debye Model
Previous Article in Journal
A Cellular Approach to Net-Zero Energy Cities
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Energies 2017, 10(11), 1830; doi:10.3390/en10111830

An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy

Department of Electrical Engineering, Myongji University, Yongin 449-728, Korea
*
Author to whom correspondence should be addressed.
Received: 12 October 2017 / Revised: 2 November 2017 / Accepted: 7 November 2017 / Published: 10 November 2017
(This article belongs to the Section Electrical Power and Energy System)
View Full-Text   |   Download PDF [4121 KB, uploaded 22 November 2017]   |  

Abstract

Current transformer (CT) saturation is one of the significant problems for protection engineers. If CT saturation is not tackled properly, it can cause a disastrous effect on the stability of the power system, and may even create a complete blackout. To cope with CT saturation properly, an accurate detection or classification should be preceded. Recently, deep learning (DL) methods have brought a subversive revolution in the field of artificial intelligence (AI). This paper presents a new DL classification method based on unsupervised feature extraction and supervised fine-tuning strategy to classify the saturated and unsaturated regions in case of CT saturation. In other words, if protection system is subjected to a CT saturation, proposed method will correctly classify the different levels of saturation with a high accuracy. Traditional AI methods are mostly based on supervised learning and rely heavily on human crafted features. This paper contributes to an unsupervised feature extraction, using autoencoders and deep neural networks (DNNs) to extract features automatically without prior knowledge of optimal features. To validate the effectiveness of proposed method, a variety of simulation tests are conducted, and classification results are analyzed using standard classification metrics. Simulation results confirm that proposed method classifies the different levels of CT saturation with a remarkable accuracy and has unique feature extraction capabilities. Lastly, we provided a potential future research direction to conclude this paper. View Full-Text
Keywords: current transformer (CT) saturation; deep neural networks (DNNs); autoencoder; classification; deep learning (DL); unsupervised feature extraction; supervised fine-tuning strategy current transformer (CT) saturation; deep neural networks (DNNs); autoencoder; classification; deep learning (DL); unsupervised feature extraction; supervised fine-tuning strategy
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Ali, M.; Son, D.-H.; Kang, S.-H.; Nam, S.-R. An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy. Energies 2017, 10, 1830.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top