Fault detection in the secondary side of electric arc furnace transformer using the primary side data

IET Science Measure & Tech

et al. 2022

Self Cite

This paper deals with the utilization of printed circuit board Rogowski coils (PCBRCs) for differential protection of electric arc furnace (EAF) transformers in Mobarakeh Steel Company (MSC), Isfahan, Iran. Because of a high level of current (in the range of 100 kA) in the EAF transformers in the MSC, employment of differential protection is quite challenging. Conventional current transformers (CTs) cannot offer reliable performance for very high current levels. The PCBRCs are a branch of Rogowski coils (RCs) that can be manufactured with high precision using computer-aided design approaches and have the capability for the measurement of very large currents. In this study, we have designed and manufactured two sets of PCBRCs to measure current in the primary and secondary sides of the EAF's transformers in the MSC. The PCBRCs are followed by a signal condition circuit (SCC) which includes a low-pass filter, amplifier, and voltage-to-current (V-to-I) converter block. Thanks to the SCC, the high current of the transformers can be properly sensed and transmitted over a long cable that connects the PCBRCs to the relay. Furthermore, a novel model is suggested for the EAF that includes characteristics of both time and frequency domain models. The suggested model is developed using a large number of recorded data from the MSC and can model the non-linear and time-variant behaviours of the EAF. The proposed PCBRCs, SCC, and EAF modelling are evaluated by some simulations and experiments in the MSC.

Section: Introductionmentioning

confidence: 99%

Designing and manufacturing of electric arc furnace currents measurement system in Mobarakeh Steel Company by printed circuit board‐type Rogowski coils

Sadeghi

IET Science Measure & Tech

et al. 2022

Self Cite

“…Therefore, suitable modelling of EAFs is of great importance to a comprehensive study of EAFs operation. Also, EAF modelling is necessary to find optimal operation conditions [10], evaluate the arc stability [11], and improve the safety of EAF plants [12,13].…”

Section: Introductionmentioning

confidence: 99%

Time‐varying frequency model for electric arc furnaces

Sadeghi

IET Generation Trans & Dist

2021

Self Cite

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.

“…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

Mojallal

Int Trans Electr Energ Syst

et al. 2018

Self Cite

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.