New directions in electric arc furnace modeling

• Autorzy: Grabowski Dariusz , Klimas Maciej

Identyfikatory

DOI

10.24425/aee.2023.143695

• Języki publikacji: EN

Abstrakty

EN

This paper presents new directions in the modeling of electric arc furnaces. This work is devoted to an overview of new approaches based on random differential equations, artificial neural networks, chaos theory, and fractional calculus. The foundation of proposed solutions consists of an instantaneous power balance equation related to the electric arc phenomenon. The emphasis is mostly placed on the conclusions that come from a novel interpretation of the equation coefficients.

Słowa kluczowe

EN

artificial neural networks; chaos theory; electric arc furnace; fractional calculus; power balance; stochastic processes

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2023
• Tom: Vol. 72, nr 1
• Strony: 157--172
• Opis fizyczny: Bibliogr. 29 poz., rys., tab., wz.

Twórcy

autor

Grabowski Dariusz

• Faculty of Electrical Engineering, Silesian University of Technology Akademicka 10 str., 44-100 Gliwice, Poland

• https://orcid.org/0000-0002-2673-1579

autor

Klimas Maciej

• Faculty of Electrical Engineering, Silesian University of Technology Akademicka 10 str., 44-100 Gliwice, Poland

• https://orcid.org/0000-0002-4514-3385

Bibliografia

• [1] Jebaraj B.S. et al., Power Quality Enhancement in Electric Arc Furnace Using Matrix Converter and Static VAR Compensator, Electronics, vol. 10, no. 9 (2021), DOI: 10.3390/electronics10091125.
• [2] Łukasik Z., Olczykowski Z., Estimating the Impact of Arc Furnaces on the Quality of Power in Supply Systems, Energies, vol. 13, no. 6 (2020), DOI: 10.3390/en13061462.
• [3] Wciślik M., Strząbała P., Physical model of power circuit of three-phase electric arc furnace, Przegląd Elektrotechniczny, vol. 4, pp. 103–106 (2018), DOI: 10.15199/48.2018.04.26.
• [4] Nikolaev A.A., Tulupov P.G., Savinov D.A., Mathematical model of electrode positioning hydraulic drive of electric arc steel-making furnace taking into account stochastic disturbances of arcs, Proceedings of 2017 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), St. Petersburg, Russia, pp. 1–6 (2017), DOI: 10.1109/ICIEAM.2017.8076205.
• [5] Sawicki A., Electric arc models with non-zero residual conductance and with increased energy dissipation, Archives of Electrical Engineering, vol. 70, no. 4, pp. 819–834 (2021), DOI: 10.24425/aee.2021.138263.
• [6] Lu T., Sun Y., Shen P., An P., Diao S., Study on Time-varying Resistance Model of Arc Furnace Based on Arc Length Modulation and PSO Algorithm, Proceedings of 2020 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia), Weihai, China, pp. 513–518 (2020), DOI: 10.1109/ICP-SAsia48933.2020.9208494.
• [7] Teklić A.T., Filipović-Grčić B., Pavić I., Modelling of three-phase electric arc furnace for estimation of voltage flicker in power transmission network, Electric Power Systems Research, vol. 146, pp. 218–227 (2017), DOI: 10.1016/j.epsr.2017.01.037.
• [8] Mousavi Agah S.M., Hosseinian S.H., Abyaneh H.A., Moaddabi N., Parameter Identification of Arc Furnace Based on Stochastic Nature of Arc Length Using Two-Step Optimization Technique, IEEE Transactions on Power Delivery, vol. 25, no. 4, pp. 2859–2867 (2010), DOI: 10.1109/TPWRD.2010.2044812.
• [9] Samet H., Sadeghi R., Ghanbari T., Time-varying frequency model for electric arc furnaces, IET Generation, Transmission & Distribution, vol. 16, no. 6, pp. 1122–1138 (2022), DOI: 10.1049/gtd2.12355.
• [10] Farzanehdehkordi M., Ghaffaripour S., Tirdad K., Dela Cruz A., Sadeghian A., A wavelet feature-based neural network approach to estimate electrical arc characteristics, Electric Power Systems Research, vol. 208, p. 107893 (2022), DOI: 10.1016/j.epsr.2022.107893.
• [11] Acha E., Semlyen A., Rajakovic N., A harmonic domain computational package for nonlinear problems and its application to electric arcs, IEEE Transactions on Power Delivery, vol. 5, no. 3, pp. 1390–1397 (1990), DOI: 10.1109/61.57981.
• [12] Ozgun O., Abur A., Development of an arc furnace model for power quality studies, Proceedings of 1999 IEEE Power Engineering Society Summer Meeting, Conference Proceedings (Cat. No. 99CH36364), Edmonton, Canada, vol. 1, pp. 507–511 (1999), DOI: 10.1109/PESS.1999.784402.
• [13] Chang G. et al., Modeling devices with nonlinear Voltage-current Characteristics for harmonic studies, IEEE Transactions on Power Delivery, vol. 19, no. 4, pp. 1802–1811 (2004), DOI: 10.1109/TP-WRD.2004.835429.
• [14] Ortega-Calderon J.E., Modelling and analysis of electric arc loads using harmonic domain techniques, PhD Thesis, Department of Electronics and Electrical Engineering, University of Glasgow, Glasgow (2008).
• [15] Klimas M., Grabowski D., Identification of nonstationary parameters of electric arc furnace model using Monte Carlo approach, Proceedings of 2020 Progress in Applied Electrical Engineering (PAEE), Kościelisko, Poland, pp. 1–6 (2020), DOI: 10.1109/PAEE50669.2020.9158732.
• [16] Dietz M., Grabowski D., Klimas M., Starkloff H.-J., Estimation and analysis of the electric arc furnace model coefficients, IEEE Transactions on Power Delivery, pp. 1–1 (2022), DOI: 10.1109/TP-WRD.2022.3163815.
• [17] Klimas M., Grabowski D., Application of Long Short-Term Memory Neural Networks for Electric Arc Furnace Modelling, Proceedings of Intelligent Data Engineering and Automated Learning – IDEAL 2021, Manchester, UK, pp. 166–175 (2021), DOI: 10.1007/978-3-030-91608-4.
• [18] Klimas M., Grabowski D., Application of Shallow Neural Networks in Electric Arc Furnace Modeling, IEEE Transactions on Industry Applications, vol. 58, no. 5, pp. 6814–6823 (2022), DOI: 10.1109/TIA.2022.3180004.
• [19] Li C., Mao Z., Generative adversarial network–based real-time temperature prediction model for heating stage of electric arc furnace, Transactions of the Institute of Measurement and Control, vol. 44, no. 8, pp. 1669–1684 (2022), DOI: 10.1177/01423312211052213.
• [20] Godoy-Rojas D.F. et al., Attention-Based Deep Recurrent Neural Network to Forecast the Temperature Behavior of an Electric Arc Furnace Side-Wall, Sensors, vol. 22, no. 4 (2022), DOI: 10.3390/s22041418.
• [21] Golestani S., Samet H., Polynomial-dynamic electric arc furnace model combined with ANN, International Transactions on Electrical Energy Systems, vol. 28, no. 7, p. 2561 (2018), DOI: 10.1002/etep.2561.
• [22] Ghiormez L., Panoiu M., Panoiu C., Tirian O., Time series prediction in the case of nonlinear loads by using ADALINE and NAR neural networks, IOP Conf. Ser.: Mater. Sci. Eng., vol. 294, no. 1, p. 12–26 (2018), DOI: 10.1088/1757-899X/294/1/012026.
• [23] Panoiu M., Panoiu C., Ghiormez L., Neuro-fuzzy modeling and prediction of current total harmonic distortion for high power nonlinear loads, Proceedings of 2018 Innovations in Intelligent Systems and Applications (INISTA), pp. 1–7 (2018), DOI: 10.1109/INISTA.2018.8466290.
• [24] MATLAB, version 7.10.0 (R2019b), Natick, Massachusetts: The MathWorks Inc. (2019).
• [25] Jang G., Wang W., Heydt G.T., Venkata S.S., Lee B., Development of Enhanced Electric Arc Furnace Models for Transient Analysis, Electric Power Components and Systems, vol. 29, no. 11, pp. 1060–1073 (2001), DOI: 10.1080/153250001753239257.
• [26] Qi G. et al., A four-wing chaotic attractor generated from a new 3-D quadratic autonomous system, Chaos, Solitons & Fractals, vol. 38, no. 3, pp. 705–721 (2008), DOI: 10.1016/j.chaos.2007.01.029.
• [27] Klimas M., Grabowski D., Application of the deterministic chaos in AC electric arc furnace modeling, Proceedings of 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), pp. 1–6 (2022), DOI: 10.1109/EEEIC/ICPSEurope54979.2022.9854594.
• [28] Grabowski D., Selected Applications of Stochastic Approach in Circuit Theory, Publishing House of the Silesian University of Technology (2015).
• [29] Gulgowski J., Stefański T.P., Trofimowicz D., On applications of elements modelled by fractional derivatives in circuit theory, Energies, vol. 13, no. 21, 5768 (2020), DOI: 10.3390/en13215768.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).