Function Space Valued Markov Model for Electric Arc Furnace

Chen, F.; Athreya, Krishna B.; Sastry, V.V.; Venkata, S.S.

doi:10.1109/tpwrs.2004.825913

Cited by 31 publications

(7 citation statements)

References 6 publications

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“…Both accuracy and efficiency have been improved by using this method. The accuracy could be improved by increasing the number of harmonics, but it has computational complexity [31].…”

Section: Mathematical Modelsmentioning

confidence: 99%

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Least Square-Support Vector Machine Based Modeling for Electric Arc Furnaces

Vakilian¹

2021

Preprint

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The process of making steel from the scrap metal by means of electric arc furnaces (EAF) has been used extensively in the industry. Accurate modelling of EAFs is, therefore, desired to assess their operations and their impacts on the electrical network. A number of approaches have already been used to model the v-i behavior of electric arc furnaces including mathematical methods and data-driven models. The objective of this thesis is to investigate the data-driven modelling methodologies, in particular, least square support vector machine (LS-SVM). The results obtained show that the proposed method with radial base function kernel provides the model to predict both arc current and arc voltage of EAFs.

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“…Both accuracy and efficiency have been improved by using this method. The accuracy could be improved by increasing the number of harmonics, but it has computational complexity [31].…”

Section: Mathematical Modelsmentioning

confidence: 99%

“…Unlike previous studies that had difficulty to capture the even harmonics, this model was able to capture even harmonics. In [31], another model described the v-i prediction approach for the electric arc furnace based on Markov chain processes. The model has been implemented by using an active harmonic filter (AHF) in…”

Section: Mathematical Modelsmentioning

confidence: 99%

Least Square-Support Vector Machine Based Modeling for Electric Arc Furnaces

Vakilian¹

2021

Preprint

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“…The difference between the actual signal and the modeled signal in the frequency domain is reduced using the non-linear least-squares method. A strategy suggested in [26] is to expand the concept of the state case to a cycle-vector scenario to make it possible to forecast one or more cycles of the EAF current/voltage. Fourier transform (FT) frequency decomposition method is suggested to forecast the cycle's consecutive frequency components.…”

Section: Introductionmentioning

confidence: 99%

Enhanced models with time‐series coefficients used for electric arc furnace

Babaei

Samet

Jalil

2023

IET Generation Trans & Dist

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Electric arc furnace (EAF) is one of the most disturbing loads in the power system as it is highly time-varying and nonlinear. In this paper, new models based on the instantaneous EAF voltage and current, recorded from EAFs in Mobarakeh steel company in Isfahan/Iran are proposed. The focus of this paper is on developing efficient models based on the Schwarz model where three modified types are proposed. The original Schwarz model has four constant parameters. Unlike the original Schwarz model which uses invariant parameters, the time-varying nature of the arc's parameters is considered in the proposed models. The first modified model has two constant model orders and two time-varying coefficients. Additionally, to further enhance the model's accuracy, in the second modified model, six parameters include three constant model orders, and three time-varying coefficients exist. The third model uses all the original model's parameters as time-series parameters. The parameters of the time-varying model are derived using autoregressive moving average (ARMA) models. The accuracy of the proposed model is evaluated by calculating the active power, harmonics, instantaneous flicker, and short-term flicker. Finally, three MATLAB codes are provided to generate the model parameters time series.

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“…In , a model consisting of a nonlinear, time‐varying function is presented where two different time variation laws of arc length, sinusoidal and band‐limited white noise, are considered. In , an approach including application of the Markov chains corresponding to each frequency component of EAF current is proposed to predict the components for the next cycle. Two types of reactive power compensators are compared in for compensation of flicker caused by EAFs.…”

Section: Introductionmentioning

confidence: 99%

A dynamic, nonlinear and time-varying model for electric arc furnace

Samet

Farjah

Sharifi

2014

Int. Trans. Electr. Energ. Syst.

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Summary Electric arc furnace (EAF) is a nonlinear and time‐varying load that causes power quality problems such as voltage flicker and harmonics. Field measurement of voltage and current from EAFs in Mobarakeh Steel Company in Isfahan‐Iran are used to introduce a comprehensive model for EAF. A new dynamic, nonlinear and time‐varying model is presented here. In other words, the function between EAF voltage and current is nonlinear, differential and with time‐varying parameters. By using the recorded data, the order of the dynamic part is determined by a new approach. The nonlinear characteristic is modeled with proper polynomial functions. All parameters related to the dynamic and nonlinear functions are considered time varying as time series. Autoregressive moving average (ARMA) models are implemented and the suitable ARMA order is attained for all EAF time‐varying parameters. Copyright © 2014 John Wiley & Sons, Ltd.

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Function Space Valued Markov Model for Electric Arc Furnace

Cited by 31 publications

References 6 publications

Least Square-Support Vector Machine Based Modeling for Electric Arc Furnaces

Least Square-Support Vector Machine Based Modeling for Electric Arc Furnaces

Enhanced models with time‐series coefficients used for electric arc furnace

A dynamic, nonlinear and time-varying model for electric arc furnace

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