An improved SVC control for electric arc furnace voltage flicker mitigation

Cernan, Martin; Müller, Zdeněk; Tlustý, Josef; Valouch, Viktor

doi:10.1016/j.ijepes.2021.106831

Cited by 27 publications

(13 citation statements)

References 29 publications

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“…Use of the secondary raw materials, the so-called scrap iron, as a raw material, and electricity as the energy source of this process, steel production in electric arc furnaces has become more prominent than any other steel production process in the world [1,2]. The main sources of secondary raw materials for steel production are construction material waste, old cars, appliances, and household waste, which means that waste can contain a large number of different metals, plastics and rubber, glass, paint, oil, and even salts [1][2][3][4][5][6][7][8]. Due to the high process temperature (1600 °C) in the electric arc furnace, during melting of a batch, some elements evaporate and together with solid particles carried away with the gas phase, form one of the by-products -electric arc furnace dust (EAF dust) [3,8,9].…”

Section: Introductionmentioning

confidence: 99%

Investigation of hazardous waste a case study of electric arc furnace dust characterization

Trifunovic

Milić

Avramović

et al. 2022

Hem Ind

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Dust from an electric arc furnace is formed as the main by-product of the steel production process from the secondary iron-based raw materials. This dust has significant contents of Zn and Fe, as well as Pb, Cd, Ca, Mg, Cr, Mn, Si, Ni, Cu, F, Cl and other elements and is considered hazardous industrial solid waste since it contains heavy metals. In order to protect the environment and public health from the negative impact of this type of hazardous waste, it is necessary, even mandatory, to carry out its treatment in accordance with the legislation of the country where it is located. Before applying any treatment of the electric arc furnace (EAF) dust, it is necessary to perform its detailed characterization. In this paper, the following characterization of EAF dust originating in the Republic of Serbia was performed: physical-mechanical and chemical characterization, determination of granulometric composition, and mineralogical characterization. Also, the EAF dust impact on the environment and human health was assessed (Leachability and Toxicity Characteristic Leaching Procedure (TCLP) tests). The results have shown that the Zn content is in the range 32 to 35 % and that the main mineralogical phases of the dust are zincite, franklinite, magnetite, and magnesioferrite. Granulometric analysis has shown that 80 % of the sample consists of particles less than 26 ?m in size. According to the leaching test results, the EAF dust is characterized as a hazardous waste due to the increased chloride content, while the TCLP test indicated dust toxicity due to the increased contents of Zn, Cd, and Pb.

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Section: Introductionmentioning

confidence: 99%

Investigation of hazardous waste a case study of electric arc furnace dust characterization

Trifunovic

Milić

Avramović

et al. 2022

Hem Ind

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“…Connecting SVC or STATCOM to the user's 110 kV bus bars, along with using a dedicated 400/110 kV transformer, enables a reduction of practically 1.5 … 2 times (in case of using SVC), and 3 … 6 times (in the case of using STATCOM), of the disturbance level at the 110 kV bars of the Electrical Station [3][4][5][6][7]12]. In this way, the problems related to the disturbance of the users in the area are completely solved as well [13].…”

Section: Author Detailsmentioning

confidence: 99%

Contributing by Monitoring Energy Efficiency to the Development of Optimization Measures to Improve Energy Performance in an Industrial Platform

Lipan¹

2022

Alternative Energies and Efficiency Evaluation

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Greenhouse gas emissions and climate change are currently major international problems. This makes it important to increase energy efficiency. The implementation of energy efficiency improvement measures has reduced energy demand for industrial platforms, so that energy plans designed before these measures are no longer appropriate for current tasks (extensions are not considered at this time). I intended to give a clear image and a better understanding of the factories’ power consumption (the industrial area in question is located near a city). A power plant-specific power system is quite disruptive, as can be seen from the monitored data at the power plant users and from the general power supply voltage bars (110 kV, 20 kV) as well as from the voltage bars of the adjacent users. Based on real-time measurements and monitoring devices, the following characteristic curves have been extracted for local energy systems. That is because data analysis is easy to be used for monitoring energy efficiency and elaborating optimization measures to improve power performance.

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“…Due to the stochastic changes in the arc length and other behaviors of electric arcs during the fusion and refining process, EAF's characteristics would be dynamic, nonlinear, and time-variant [4,5]. 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].…”

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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An improved SVC control for electric arc furnace voltage flicker mitigation

Cited by 27 publications

References 29 publications

Investigation of hazardous waste a case study of electric arc furnace dust characterization

Investigation of hazardous waste a case study of electric arc furnace dust characterization

Contributing by Monitoring Energy Efficiency to the Development of Optimization Measures to Improve Energy Performance in an Industrial Platform

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

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