Deep-Learning-Based Harmonics and Interharmonics Predetection Designed for Compensating Significantly Time-Varying EAF Currents
Artikel i vetenskaplig tidskrift, 2020

In this article, a new approach to compensate both the response and reaction times of active power filters (APF) for special cases of highly time-varying harmonics and interharmonics of electric arc furnace (EAF) currents is proposed. Instead of using the classical approach of taking a window of past current samples and analyzing the data, future samples of EAF currents are predetected using a deep learning (DL)-based method and then analyzed, which provides the opportunity to make real-time analysis. This can also serve the needs of other possible APF applications. Two different methods for prediction of future samples of harmonics and interharmonics have been proposed: predetection of harmonics and interharmonics in the time domain (TD) and in the frequency domain (FD). To obtain the best possible accuracy for both methods, grid search has been employed for parameter optimization of the DL structure. Both TD and FD approaches have been tested on field data, which had been obtained from transformer substations supplying EAF plants. It is shown that the response time of the APF algorithms can be compensated using the TD-based approach, while it is possible to compensate both the response and reaction times of APFs using the proposed FD-based approach. The developed method can be considered to be a feasible candidate solution for generating reference signals for the controllers of new generation of compensation devices, which can be referred to as predictive APFs.

Delays

parallel processing

deep learning (DL)

power quality (PQ)

multiple synchronous reference frame (MSRF)

Time-domain analysis

graphical processing unit (GPU)

electric arc furnace (EAF)

Time factors

harmonics

Active power filter (APF)

Harmonic analysis

Power harmonic filters

harmonic analysis

interharmonics

Frequency-domain analysis

Författare

Ebrahim Balouji

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Karl Bäckström

Chalmers, Data- och informationsteknik, Nätverk och system

Tomas McKelvey

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Ozgul Salor

Kyrgyz Turkish Manas Univ

Gazi Universitesi

IEEE Transactions on Industry Applications

0093-9994 (ISSN)

Vol. 56 3 3250-3260

Ämneskategorier

Bioinformatik (beräkningsbiologi)

Signalbehandling

Annan elektroteknik och elektronik

DOI

10.1109/TIA.2020.2976722

Mer information

Senast uppdaterat

2020-10-01