A comparative study of short term flicker severity due to electric arc furnace operation

Dutta, Pertha Protim; Fahad, Abul Hasan; Chowdhury, A. Hasib

doi:10.1109/icece.2014.7027011

Cited by 3 publications

(6 citation statements)

References 6 publications

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“…where M is the number of EAFs which are working simultaneously in the system, P st is the flicker severity due to one EAF and n is the flicker summation factor. Flicker summation factor (n) can be defined to evaluate the relation between the cumulative P st of the system (P st,cum ) and P st of one of the EAFs [34]. Smaller n shows that the system suffers from higher P st,cum .…”

Section: 6mentioning

confidence: 99%

“…The maximum of instantaneous flicker can be used to approximate short-term flicker as shown in (9). This approximation is more accurate for cases that the system voltage fluctuation is sinusoidal [34].…”

Section: Approximation Of P St Using the Maximum Value Of The Instant...mentioning

confidence: 99%

“…The maximum of instantaneous flicker can be used to approximate short‐term flicker as shown in (9). This approximation is more accurate for cases that the system voltage fluctuation is sinusoidal [34].

\begin{equation}{P_{st}} = \sqrt {0.5096 \times {S_{{\rm{max}}}}} \ \end{equation}

…”

Section: Assessment Of the Proposed Modelmentioning

confidence: 99%

“…When more than one EAF is working in the system simultaneously the cumulative P st at PCC can be calculated as follows:

\begin{equation}{p_{st,cum}} = \sqrt[n]{{\mathop \sum \limits_{i = 1}^M p_{st,i}^n}}\ \end{equation}

where M is the number of EAFs which are working simultaneously in the system, P st is the flicker severity due to one EAF and n is the flicker summation factor. Flicker summation factor ( n ) can be defined to evaluate the relation between the cumulative P st of the system ( P st,cum ) and P st of one of the EAFs [34]. Smaller n shows that the system suffers from higher P st,cum .…”

Section: Assessment Of the Proposed Modelmentioning

confidence: 99%

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Time‐varying frequency model for electric arc furnaces

Samet

Sadeghi

Ghanbari

2021

IET Generation Trans & Dist

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Accurate modelling of the Electric arc furnace (EAF) is necessary to study its impact on the power systems. Here, a new model for the EAF is presented in which both non-linearity and time-varying characteristics are taken into account. The model consists of seven parallel current sources which represent the fundamental and harmonic currents. Magnitude and phase of the fundamental harmonic and magnitude of the second to seventh harmonics are considered as time-varying which are updated every 0.01 s. To do so, the actual data recorded from eight EAFs in the Mobarakeh steel company (MSC), Isfahan, Iran are used. Autoregressive moving average (ARMA) models are employed to derive the parameters of the time-varying model. Also, different orders and coefficients of the ARMA model are considered at each run of the model, which enables the proposed model to predict the nonstationary and stochastic behaviours of the EAF over long-time periods. It is shown that the proposed combined time-varying-harmonic model can even model the generated interharmonics. To evaluate the accuracy of the proposed model, the derived power spectral density (PSD) of the EAF time-varying parameters, the instantaneous flicker, and shortterm flicker by the proposed model are compared with the actual recorded data.

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Section: 6mentioning

confidence: 99%

Section: Approximation Of P St Using the Maximum Value Of The Instant...mentioning

confidence: 99%

\begin{equation}{P_{st}} = \sqrt {0.5096 \times {S_{{\rm{max}}}}} \ \end{equation}

…”

Section: Assessment Of the Proposed Modelmentioning

confidence: 99%

“…When more than one EAF is working in the system simultaneously the cumulative P st at PCC can be calculated as follows:

\begin{equation}{p_{st,cum}} = \sqrt[n]{{\mathop \sum \limits_{i = 1}^M p_{st,i}^n}}\ \end{equation}

Section: Assessment Of the Proposed Modelmentioning

confidence: 99%

See 2 more Smart Citations

Time‐varying frequency model for electric arc furnaces

Samet

Sadeghi

Ghanbari

2021

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…In this section, the short term flicker is approximated using the maximum instantaneous flicker obtained from Section 5.2 by the following equation [36]:

P_{normalst} = \sqrt{0.5096 \times S_{normalmax}}

…”

Section: Assessment Of the Proposed Modelmentioning

confidence: 99%

Non stationary‐ARMA flicker model for squirrel cage induction generators based wind farms

Samet

Ketabipour

Bagheri

2021

IET Renewable Power Gen

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Time varying nature of the wind power is studied previously considering different time intervals from seconds to days. However for power quality problems such as flicker, a model which considers the extremely fast variations is essential. Here by using large number of actual records, a time-varying model is proposed which considers the extremely short-time variations of wind active and reactive powers. The wind farm is modelled as a current source with time varying amplitude and phase which change every 0.01 s. Autoregressive moving-average (ARMA) models are utilized to model the variations and ARMA coefficients are calculated for every record. Same to the actual behaviour, the proposed model is non-stationary as the ARMA order and coefficients are different at every run of the model. The proposed model is confirmed through utilizing several applications which need the power time series with extremely short sampling intervals. The following studies are performed by using the actual data and then the proposed model: power spectral density of active and reactive power variations, instantaneous flicker, short term flicker (Pst), estimating the Pst using the maximum value of the instantaneous flicker, estimation of cumulative Pst for multiple wind turbines, and the impact of SVC on flicker mitigation. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Modeling of Extremely Short-Time Power Variations of Wound Rotor Induction Machines Wind Farms for Flicker Studies

Ketabipour,

Samet,

Mohammadi

et al. 2023

IEEE Access

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Time-varying nature of wind farms is one of their major obstacles in providing a constant and reliable power output. They can be considered as a time-varying source of power considering different timeframes, from long to extremely short time periods. The focus of this study is modeling the wind farms for power quality studies by focusing on voltage flicker caused by the extremely fast wind farm output power variations. Despite there being several models developed for modeling the variations over longer time periods, there are few models that consider the extremely short time power variation, i.e., those in the range of 5-15 milliseconds. Our research started with the acquisition of a large data set of actual instantaneous voltage and current signals, recorded at a wind farm under different weather and operating conditions. The data set is utilized to develop practical models for the individual wind turbines and for the whole wind farm suitable for the mentioned extremely short time variations for the case of wind farms with the wound rotor induction generators (WRIG). The proposed model can be used for voltage flicker studies in power systems with WRIGs. The equivalent model of the WRIG is represented by a current source which its magnitude and phase change every half-cycle. It is observed that the variations of active and reactive powers follow a nonstationary seasonal time series where the seasonal part is not a simple single frequency. The seasonal term contains several frequencies which are modeled by 10 frequency components between 0.1 Hz to 1 Hz plus a DC component. The remaining component is modeled by autoregressive moving average (ARMA) models. The accuracy of the proposed equivalent model is assessed by several tests based on actual data and their corresponding simulated time series.

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A comparative study of short term flicker severity due to electric arc furnace operation

Cited by 3 publications

References 6 publications

Time‐varying frequency model for electric arc furnaces

Time‐varying frequency model for electric arc furnaces

Non stationary‐ARMA flicker model for squirrel cage induction generators based wind farms

Modeling of Extremely Short-Time Power Variations of Wound Rotor Induction Machines Wind Farms for Flicker Studies

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