Measurement-Based Electric Arc Furnace Model Using Ellipse Formula

Lee, Choongman; Kim, Heejin; Lee, Eun‐Jae; Baek, Seung-Taek; Shim, Jae Yeob

doi:10.1109/access.2021.3127533

Cited by 7 publications

(5 citation statements)

References 38 publications

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“…There are many publications in the literature related to EAF modeling [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Different methods are used to describe the electrical arc in these studies.…”

Section: Figure 1 Power System Of the Eafmentioning

confidence: 99%

“…In [23], a mathematical model has been constructed using the experimentally obtained average dynamic Volt-Ampere characteristics. In [24], another mathematical EAF model based on field data is developed using an ellipse equation. In [25], a detailed model of the EAF has been built up by using a multibody dynamics (MBD) environment with the finite-element method (FEM).…”

Section: Figure 1 Power System Of the Eafmentioning

confidence: 99%

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Elektrik Güç Şebekesi’nde Gerilim Titreşim Analizi için Elektrik Ark Ocağının Dinamik Modeli ile Gerçek Çalışma Verilerinin Karşılaştırılması

KÖROĞLU

2023

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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Electric arc furnaces (EAFs) used in the iron and steel manufacturing industry for melting and reﬁning scrap metals are one of the most disturbing loads that exhibit unbalanced and highly nonlinear characteristics. Serious voltage fluctuations occur in the power system as a result of the rapid change in the current drawn from the grid by the EAF. Voltage fluctuations lead to a power quality problem known as flicker, which is defined as observable changes in light sources that affect the production environment, cause eye fatigue in personnel, and lower the work concentration levels. To investigate the voltage flicker problem, an accurate mathematical model describing the behavior of the EAF load is required. In this study, a dynamic EAF model that can be adjusted to different operating conditions has been developed in the time domain. The electric arc voltage has been modeled as an externally controllable voltage source. The instantaneous arc voltage has been expressed as a function of the arc length independent of the current. The arc resistance, which varies with time and is nonlinear, has also been calculated with differential equations using the instantaneous arc voltage value. To measure the short-term flicker severity index caused by the EAF in the power system, a flicker meter in compliance with the International Electrotechnical Commission (IEC) 61000-4-15 standard has been designed. The current-voltage characteristics of the EAF, its effect on the power system, and the flicker severity occurring at the point of common coupling (PCC) have been analyzed with simulation studies using the PSCAD/EMTDC software. Besides, the simulation results of the dynamic model of the EAF have been compared with the results obtained from the model based on the measured ﬁeld data.

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Section: Figure 1 Power System Of the Eafmentioning

confidence: 99%

Section: Figure 1 Power System Of the Eafmentioning

confidence: 99%

Elektrik Güç Şebekesi’nde Gerilim Titreşim Analizi için Elektrik Ark Ocağının Dinamik Modeli ile Gerçek Çalışma Verilerinin Karşılaştırılması

KÖROĞLU

2023

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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“…On the other hand, time-domain models are suitable for studying EAF in power systems. us, several time-domain models derived by approximating the EAF voltage-current characteristic, such as piece-wise linear, modified piece-wise linear, hyperbolic, exponential, and exponential-hyperbolic models, have been introduced in the literature for studying the EAF behaviors [22,33].…”

Section: Motivation and Incitementmentioning

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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“…arc voltage U′Arc: (12) arc resistance r′Arcn: The resistance value was adopted as 0.1 and 0.2 of the supply reactance value, i.e., R = 5% and R = 10%. For the resistances and reactances specified in this way, X = 50% (with U SV = 100%), and the service short-circuit current I SC is:…”

Section: Power-voltage Characteristics Of the Arc Device Taking Into ...mentioning

confidence: 99%

“…In the work on arc devices, simulation studies are also using computer programs based, among others, on neural networks [11][12][13][14][15][16][17][18][19][20]. However, this requires the use of complex electric arc models and advanced simulation programs.…”

Section: Introductionmentioning

confidence: 99%

Arc Furnace Power-Susceptibility Coefficients

Olczykowski

2022

Energies

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The article presents the susceptibility coefficients active power kp and reactive power kq, as proposed by the author. These coefficients reflect the reaction of arc furnaces (change of the furnace operating point) to supply voltage fluctuations. The considerations were based on the model of the arc device in which the electric arc was replaced with a voltage source with an amplitude dependent on the length of the arc. In the case of voltage fluctuations, such a model gives an assessment of the arc device’s behavior closer to reality than the model used, based on replacing the arc with resistance. An example of the application of the kp and kq coefficients in a practical solution is presented.

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Measurement-Based Electric Arc Furnace Model Using Ellipse Formula

Cited by 7 publications

References 38 publications

Elektrik Güç Şebekesi’nde Gerilim Titreşim Analizi için Elektrik Ark Ocağının Dinamik Modeli ile Gerçek Çalışma Verilerinin Karşılaştırılması

Elektrik Güç Şebekesi’nde Gerilim Titreşim Analizi için Elektrik Ark Ocağının Dinamik Modeli ile Gerçek Çalışma Verilerinin Karşılaştırılması

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

Arc Furnace Power-Susceptibility Coefficients

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