TY - GEN
T1 - Application of shallow neural networks in electric arc furnace modelling
AU - Klimas, Maciej
AU - Grabowski, Dariusz
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Electric arc furnaces (EAFs) are important appliances in steelmaking industry, but they are characterized by nonlinear, dynamic and stochastic nature. Due to this fact, EAFs can have a negative influence on power systems. Measures for mitigation of such problems can be designed properly only with the knowledge of the load influence on the system. Therefore, it is necessary to have accurate models of EAFs, reflecting the complicated character of such loads. Researchers use different approaches for EAF modelling, such as stochastic processes analysis, differential equation models or neural networks. This paper presents the application of three artificial neural network (ANN) based models in EAF modelling. The goal was to provide ANN models which are simple in structure in comparison to deep learning methods used by other researchers. First two models are built on multilayer perceptron networks and the third applies a nonlinear autoregressive exogenous model with the help of a differential equation transformed into Hammerstein-Wiener model. The paper describes the measurement data, a design for each approach and the results of EAF modelling.
AB - Electric arc furnaces (EAFs) are important appliances in steelmaking industry, but they are characterized by nonlinear, dynamic and stochastic nature. Due to this fact, EAFs can have a negative influence on power systems. Measures for mitigation of such problems can be designed properly only with the knowledge of the load influence on the system. Therefore, it is necessary to have accurate models of EAFs, reflecting the complicated character of such loads. Researchers use different approaches for EAF modelling, such as stochastic processes analysis, differential equation models or neural networks. This paper presents the application of three artificial neural network (ANN) based models in EAF modelling. The goal was to provide ANN models which are simple in structure in comparison to deep learning methods used by other researchers. First two models are built on multilayer perceptron networks and the third applies a nonlinear autoregressive exogenous model with the help of a differential equation transformed into Hammerstein-Wiener model. The paper describes the measurement data, a design for each approach and the results of EAF modelling.
KW - electric arc furnace
KW - hammerstein-wiener model
KW - multilayer perceptron
KW - neural network
KW - nonlinear autoregressive exogenous model
UR - https://www.scopus.com/pages/publications/85126432548
U2 - 10.1109/EEEIC/ICPSEurope51590.2021.9584512
DO - 10.1109/EEEIC/ICPSEurope51590.2021.9584512
M3 - Conference contribution
AN - SCOPUS:85126432548
T3 - 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings
BT - 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings
A2 - Leonowicz, Zbigniew M.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021
Y2 - 7 September 2021 through 10 September 2021
ER -