Deep Learning Based Predictive Compensation of Flicker, Voltage Dips, Harmonics and Interharmonics in Electric Arc Furnaces

Abstract

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 with the prediction is to counteract both the response and reaction time delays of active power filters (APFs) specifically designed 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 lowpass 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 lowpass 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 96% and 60% efficiency on compensation of EAF current interharmonics.

Power Factor Modelling and Prediction at the Hot Rolling Mills’ Power Supply Using Machine Learning Algorithms

“…Deep machine learning-based algorithms and novel data augmentation are used to forecast flicker, voltage dip, harmonics, and interharmonics from highly time-varying electric arc furnace (EAF) currents and voltage in [28]. The prediction aims to reduce response and reaction time delays in electric arc furnaces (EAF)-specific active power filters (APFs).…”

“…As a result, many papers in specialized journals present research on the presence of harmonic currents, load unbalances, or overcurrents. In recent years, there has been a significant amount of research conducted on power quality and harmonic currents, focusing on the utilization of artificial intelligence and machine learning techniques [22][23][24][25][26][27][28][29][30]. Our research also focuses on utilizing deep learning and recurrent neural networks (RNN) for predicting the power factor in hot rolling mils factories.…”

A Novel Overlapping ME Peaks Decomposition Algorithm Based on Iterative Derivative Sharpening

“…Hasan et al [20] showed that SG filter can remove harmonics and does not cause distortion of grid voltage signal. In addition to the SG filter, commonly used signal smoothing methods include Wl denoising [21], Bw filter [22], Chebyshev filter [23], and Et filter [24]. It can be seen that these methods have been widely used in the smoothing of different kinds of signals.…”