Ebrahim Balouji

Doctoral Student at Signal Processing

Ebrahim Balouji is a Ph.D. student in the Signal processing group. He is involved in a project on signal analysis and data mining for big power systems using deep learning, a collaboration project between the Signal processing and Power engineering groups at Chalmers.

Source: chalmers.se
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Showing 6 publications

2020

A deep learning approach to earth fault classification and source localization

Ebrahim Balouji, Karl Bäckström, Petri Hovila
IEEE PES Innovative Smart Grid Technologies Conference Europe. Vol. 2020-October, p. 635-639
Paper in proceeding
2020

Deep-Learning-Based Harmonics and Interharmonics Predetection Designed for Compensating Significantly Time-Varying EAF Currents

Ebrahim Balouji, Karl Bäckström, Tomas McKelvey et al
IEEE Transactions on Industry Applications. Vol. 56 (3), p. 3250-3260
Journal article
2019

Partial discharge classification in power electronics applications using machine learning

Ebrahim Balouji, Thomas Hammarström, Tomas McKelvey
GlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings
Paper in proceeding
2019

Deep Learning Based Harmonics and Interharmonics Pre-Detection Designed for Compensating Significantly Time-varying EAF Currents

Ebrahim Balouji, Karl Bäckström, Tomas McKelvey et al
2019 IEEE Industry Applications Society Annual Meeting, IAS 2019
Paper in proceeding
2018

A LSTM-based Deep Learning Method with Application to Voltage Dip Classification

Ebrahim Balouji, Irene Yu-Hua Gu, Math H. J. Bollen et al
2018 18TH INTERNATIONAL CONFERENCE ON HARMONICS AND QUALITY OF POWER (ICHQP)
Paper in proceeding
2018

A Robust Transform-Domain Deep Convolutional Network for Voltage Dip Classification

Azam Bagheri, Irene Yu-Hua Gu, Math Bollen et al
IEEE Transactions on Power Delivery. Vol. 33 (6), p. 2794-2802
Journal article

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