Parameters estimation of electric arc furnace based on an analytical solution of power balance equation

Alkaran, Davood Solati; Vatani, Matin; Sanjari, M. J.; Gharehpetian, Gevork B.

doi:10.1002/etep.2295

Cited by 8 publications

(9 citation statements)

References 32 publications

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“…Manchur [13] Alkaran [14] Marulanda [15] Esfahani [16] Golestani [17] Illahi [18] Samet [19] Deepak [20] Dietz [21] Non-linear…”

Section: A) Linearity Versus Non-linearitymentioning

confidence: 99%

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Optimized time varying parameters of the power balance equation model for electric arc furnaces

Babaei,

Samet

2024

IET Generation Trans & Dist

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An accurate model of electric arc furnaces (EAFs) is required to analyse their impact on the system's power quality. One of the well‐known approaches to model EAF is using the power balance equation. This model has two terms, each one has a coefficient named the model parameter. Also, both terms are powered to values, which are called model orders. So, the model includes two model parameters and two model orders. Model orders highly affect the modelling precision. The optimum model orders have not been obtained yet in previous studies. Here, based on many actual records from EAFs, the best model orders for refining, melting, and scrapping stages are attained. Unlike the original power balance equation which has constant model parameters, the first proposed model uses the attained optimal model orders with the time‐varying model parameters. In the second proposed model, the model orders are also considered time‐varying. Model parameters, which are updated every 10 ms, are obtained using autoregressive moving average (ARMA) models. The correctness of the models is assessed by calculating some important electric quantities using the actual data and the proposed models. Finally, the time‐varying parameters are generated using two MATLAB codes.

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“…Manchur [13] Alkaran [14] Marulanda [15] Esfahani [16] Golestani [17] Illahi [18] Samet [19] Deepak [20] Dietz [21] Non-linear…”

Section: A) Linearity Versus Non-linearitymentioning

confidence: 99%

“…In fact, in a dynamic model, the electrical quantities in the previous moments are used. Some of the dynamic models are proposed in [14,15,17,[19][20][21]. c) Time-constant versus time-varying parameters Time-varying parameters cause flicker.…”

Section: B) Dynamic Versus Staticmentioning

confidence: 99%

Optimized time varying parameters of the power balance equation model for electric arc furnaces

Babaei,

Samet

2024

IET Generation Trans & Dist

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“…Accordingly, EAF causes power quality problems in the supply network, such as voltage and current imbalances [6], harmonics [7,8], and voltage flicker [9][10][11]. To investigate the EAF's adverse effects in the point of common coupling (PCC) and provide appropriate solutions to deal with the disturbances, using precise EAF mathematical models and implementing the EAF in specialized simulation software is essential [12][13][14]. Although several studies have been done in the field of EAFs, there is a research gap in selecting an appropriate model incorporating a suitable simulation environment.…”

Section: Motivation and Incitementmentioning

confidence: 99%

“…In (13), Δt and T denote the duration of simulation steps and the simulation step number. By arranging (13), the arc current is calculated at any moment based on the arc current and arc voltage at the previous step as given in…”

Section: V(t) − V a (T) � L Eqmentioning

confidence: 99%

Optimal Electric Arc Furnace Model’s Characteristics Using Genetic Algorithm and Particle Swarm Optimization and Comparison of Various Optimal Characteristics in DIgSILENT and EMTP-RV

Nooshabadi

Sadeghi

Hashemi‐Dezaki

2022

International Transactions on Electrical Energy Systems

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The stochastic and nonlinear characteristics of electric arc furnaces (EAFs) lead to power quality challenges in the power system. In studying EAF behaviors, having optimized characteristics/models, selecting a suitable and optimum model that adapts to the actual characteristics of EAFs, and investigating simulation software’s capability for implementing EAF models are essential. However, the literature shows a research gap in investigating EAF simulations in various software products based on different models. This paper studies several time-domain models, such as piece-wise linear, modified piece-wise linear, hyperbolic, exponential, and exponential-hyperbolic models, for EAF modeling and simulation. The optimal estimation of parameters for the introduced models is necessary to adapt actual EAF characteristics. Thus, one of the studies taken in this paper is optimizing the EAF model’s characteristics. The proposed optimization problem is solved using the genetic algorithm (GA) and particle swarm optimization (PSO). Moreover, the optimized models are simulated in DIgSILENT and EMTP-RV to investigate different EAF models from the viewpoint of accuracy and efficiency. The optimization of different EAF models’ characteristics and comparison of EMTP-RV and DIgSILENT in simulating EAF behavior are the contributions of this paper. The proposed method is validated based on the actual data of a realistic EAF-based steel company in Iran. The obtained results show that the modified piece-wise linear model has the most accuracy in identifying the EAF behavior. The test results based on DIgSILENT and EMTP-RV simulations imply that the EAF could be simulated with high accuracy using modified piece-wise linear and piece-wise linear models. In general, EMTP-RV has expressed more accuracy in simulating different EAF models, and the simulation execution speed of EMTP-RV is around 2.5 times faster than DIgSILENT. In contrast, DIgSILENT is more suitable to facilitate the power system studies of EAF according to its extensive study tools and library.

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“…Electric arc furnace (EAF) is considered as one of the largest reactive power loads in the power systems globally . EAF is a harmonic‐rich nonlinear load from voltage and current points of view . Moreover, an EAF requires large, time‐varying, and nondeterministic reactive power to operate .…”

Section: Introductionmentioning

confidence: 99%

Enhancement of SVC performance in electric arc furnace for flicker suppression using a Gray‐ANN based prediction method

Samet

Mojallal

Ghanbari

et al. 2018

Int Trans Electr Energ Syst

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Summary Delays in reactive power measurement and thyristor ignition limit SVC performance in flicker mitigation of electric arc furnaces (EAFs). To overcome this limitation, prediction methods can be employed to forecast the EAF reactive power for half cycle ahead, used as a reference signal of the SVC. The utilized prediction methods in this area can be divided into linear and black‐box approaches. However, the linear approaches cannot extract the nonlinear governed relations, and using a black‐box model is not efficient for linear relations. A Gray‐ANN method is proposed here to take the advantages of the two mentioned approaches. Results from indices based on actual records of Mobarakeh Steel Company confirm superiorities of the proposed method over previously utilized prediction methods in this application. Furthermore, SVC's flicker mitigation ability is evaluated using the actual data. The results confirm the significant reduction of flicker compared with the regular system.

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Parameters estimation of electric arc furnace based on an analytical solution of power balance equation

Cited by 8 publications

References 32 publications

Optimized time varying parameters of the power balance equation model for electric arc furnaces

Optimized time varying parameters of the power balance equation model for electric arc furnaces

Optimal Electric Arc Furnace Model’s Characteristics Using Genetic Algorithm and Particle Swarm Optimization and Comparison of Various Optimal Characteristics in DIgSILENT and EMTP-RV

Enhancement of SVC performance in electric arc furnace for flicker suppression using a Gray‐ANN based prediction method

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