Deep Learning Based Predictive Compensation of Flicker, Voltage Dips, Harmonics and Interharmonics in Electric Arc Furnaces
Artikel i vetenskaplig tidskrift, 2022

In this research work, deep machine learning-based methods together with a novel data augmentation are developed for predicting flicker, voltage dip, harmonics, and interharmonics originating from highly time-varying electric arc furnace (EAF)currents and voltage. The aim of the prediction is to counteract both the response and reaction time delays of active power filters(APFs) designed explicitly for electric arc furnaces (EAF). Multiple synchronous Reference frame (MSRF) analysis is used to decompose the frequency components of the EAF current and voltage waveforms into dqo components. Then using low-pass filters and prediction of the future values of these dqo components, reference signals for APFs are generated. Three different methods have been developed. In two of them, a low-pass Butterworth filter is used together with a linear FIR based prediction or long short-term memory network (LSTM) for prediction. In the third method, a deep convolutional neural network (CNN) combined with a LSTM network is used to filter and predict at the same time. For a 40 ms prediction horizon, the proposed methods provide 2.06%, 0.31%, 0.99%prediction errors of the dqo components for the Butterworth and linear prediction, Butterworth and LSTM and CNN with LSTM, respectively. The error of the predicted reconstructed waveforms of flicker, harmonics, and interharmonics resulted in 8.5%,1.90%, and 3.2% reconstruction errors for the above-mentioned methods. Finally, a Simulink and GPU-based implementation of predictive APF using Butterworth filter + LSTM and a trivial APF resulted in 96% and 60% efficiency on the compensation of EAF current interharmonics.

Hardware

convolutional neural networks

voltage dip

Power systems

Voltage fluctuations

deep learning

Delays

multiple synchronous reference frame

linear prediction

electric arc furnace

Butterworth filter

Harmonic analysis

long short-term memory

Delay effects

interharmonics

active power filter

flicker

Prediction algorithms

harmonics

Författare

Ebrahim Balouji

Chalmers, Mekanik och maritima vetenskaper, Förbränning och framdrivningssystem

Özgül Salor

Turkiye Bilimsel ve Teknolojik Arastirma Kurumu

Tomas McKelvey

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

IEEE Transactions on Industry Applications

0093-9994 (ISSN)

Vol. In Press

Ämneskategorier

Bioinformatik (beräkningsbiologi)

Signalbehandling

Annan elektroteknik och elektronik

DOI

10.1109/TIA.2022.3160135

Mer information

Senast uppdaterat

2022-04-08