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An accurate model of electric arc furnaces (EAFs) is required to analyse their impact on the system's power quality. One of the well‐known approaches to model EAF is using the power balance equation. This model has two terms, each one has a coefficient named the model parameter. Also, both terms are powered to values, which are called model orders. So, the model includes two model parameters and two model orders. Model orders highly affect the modelling precision. The optimum model orders have not been obtained yet in previous studies. Here, based on many actual records from EAFs, the best model orders for refining, melting, and scrapping stages are attained. Unlike the original power balance equation which has constant model parameters, the first proposed model uses the attained optimal model orders with the time‐varying model parameters. In the second proposed model, the model orders are also considered time‐varying. Model parameters, which are updated every 10 ms, are obtained using autoregressive moving average (ARMA) models. The correctness of the models is assessed by calculating some important electric quantities using the actual data and the proposed models. Finally, the time‐varying parameters are generated using two MATLAB codes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study deals with a new approach to tackle the challenges encountered in the filters-based reactive power calculation techniques utilized in the control system of static VAr compensators. In this approach, step and time-varying responses are improved by a combination of differential and filter-based methods. Then, optimum values of the main parameters of the low-pass and band-stop filters are determined considering three criteria: accuracy, speed, and capability for following the time-varying loads. To assess the performance of the proposed method, a new objective function is introduced, calculated for two sets of signals, captured from a real field in Mobarakeh Steel Company in Isfahan/Iran. Then, the proposed and the other reactive power calculation methods are assessed in practice and simulation. Also, the algorithm is implemented using a TMDSCNCD28335 as the processor to assess the real-time feasibility of the method. To do so, the reactive power variation of an electrical arc furnace is emulated in a prototype test bench and the algorithm is run in real-time by the processor during the system operation. [ABSTRACT FROM AUTHOR]

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 non‐stationary 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 inter‐harmonics. To evaluate the accuracy of the proposed model, the derived power spectral density (PSD) of the EAF time‐varying parameters, the instantaneous flicker, and short‐term flicker by the proposed model are compared with the actual recorded data. [ABSTRACT FROM AUTHOR]

• Four new indices are defined to quantize branches responsibility to sag/swell and flicker. • The indices are calculated using a cumulative approach in real-time, so the decision can be made upon a long-time duration. • The indices are based on the RMS values of the voltage and current which can be communicated every cycle. • The calculation algorithms are implemented in a recursive way to minimize the computational burden. The purpose of this study is to find simple cost-efficient algorithms for real-time quantizing the responsibility for power quality issues in every branch in an industrial network. The study is focused on voltage sag/swell and flicker which are caused by the time variation characteristics of loads. Here by using the RMS values of the voltage and current, four indices are defined. Cumulative-recursive equations are used to reduce the computation load and to be able to evaluate the power quality problems in real-time. Two indices quantize the loads' responsibility in sag/swell events. The duration of the sag/swell events is considered in the second index. Two indices are proposed for flicker. The first flicker index is based on the FFT of the flicker powers in the individual flicker frequencies. The second flicker index is based on the FFT of the calculated time-varying load admittance corresponding to flicker range frequencies. The load variation coherency of the different branches affects the first flicker index while it has no impact on the second one. The performance of the proposed indices is tested using simulation data, actual records from electric arc furnace and a wind farm, and a laboratory test. Results reveal that the proposed indices efficiently quantize branches' responsibility in the sag/swell and flicker events. The sag/swell indices increase after every event caused by the responsible branch in proportion with the event depth and duration. The flicker indices increase in proportion to the flicker powers according to IEC868 flicker sensitivity coefficients. [ABSTRACT FROM AUTHOR]

• Two improved types of accurate models based on the Schavemaker model are developed. • Unlike the original Schavemaker model which uses invariant parameters, the time-varying nature of the arc's parameters is considered in the proposed models. • The first improved model has time-varying coefficients. • In the second improved model, a new term has been added to the schavemaker model that includes time-varying coefficients. • A large number of the actual data are used. Electric arc furnace (EAF) is more and more applied in large industrial systems and represent one of the major sources of perturbations for the power system because it is highly non-linear and time-varying. New models are suggested in this research based on instantaneous EAF voltage and current measurements taken from EAFs at the Mobarakeh steel mill in Isfahan, Iran. This paper aims to develop two improved types of accurate models based on the Schavemaker model. The original Schavemaker model has three constant parameters. Unlike the original Schavemaker model which uses invariant parameters, the time-varying nature of the arc's parameters is considered in the proposed models. The first improved model has time-varying parameters. Additionally, to further enhance the model's accuracy, in the second improved model, a term has been added to the schavemaker model that includes time-varying parameters. Compared to the original Schavemaker model, all of the developed Schavemaker models are more precise. Finally, auto-regressive moving average (ARMA) models are used to attain the time variation characteristic of the parameters. [ABSTRACT FROM AUTHOR]

Summary: Two approaches exist for modeling electric arc furnace, the largest electrical load. Distinct mathematical equations are used as the first approach. However, the process is nonstationary and the equation is limited to 1 form and unable to extract all nonlinear components. Black‐box models such as artificial neural networks are used as the second approach. However, using a black‐box model for a process with partially known equations is not efficient, as it cannot benefit from known knowledge of the process. This paper proposes a combination of the 2 mentioned approaches. The first phase consists of nonlinear and dynamic terms. The appropriate model orders relate to dynamic and nonlinear terms are attained. In the second phase, artificial neural network is used to model the rest of the nonlinear components, which are not captured in the first phase. Numerous actual records are used to compare the proposed model with several electric arc furnace models. [ABSTRACT FROM AUTHOR]

Electric arc furnace (EAF) is one of the largest non-linear loads in power systems. It is highly time-varying which leads to low-frequency voltage fluctuations in the system, called flicker. Static VAr compensator (SVC) is always used in EAF plants to compensate the reactive power and reduce voltage fluctuations. The reactive power calculation unit is one of the most important units in SVC control system. Since SVC can only compensate the fundamental component, the calculated reactive power should not be sensitive to other harmonics. On the other hand, since the reactive power changes every half cycle, its calculation should be fast enough to track the changes. Hence, a calculation method with minimum delay and lowest harmonic sensitivity should be utilised to achieve an effective compensation. Among different already proposed methods for calculation of reactive power, differential method has the lowest delay. However, it is very sensitive to harmonics. In this study, Kalman filter is used to extract the fundamental components of voltage and current with a little delay and then they are used as inputs for the differential method. The proposed method is evaluated using some simulation signals and practical records from several EAFs in Mobarekeh steel company in Esfahan – Iran. [ABSTRACT FROM AUTHOR]

In this study main reactive power calculation methods are evaluated and compared. Furthermore, a novel simple method that can be used for calculation of reactive power in electric arc furnace (EAF) plants is proposed. The focus here is on their application in the control system of static VAr compensators (SVCs). SVC can compensate only the fundamental harmonic component. So the reactive power signal used in the control system should be sensitive only to the main harmonic component. In addition, the reactive power calculation should be fast enough that SVC can follow the quick changes of the load to mitigate the flicker efficiently. To compare various reactive power calculation methods and to evaluate the proposed method, the simulated known loads are used beside huge amount of practical data recorded in Mobarakeh Steel Company in Isfahan/Iran. Results show the proposed method has the best performance for SVC control system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In order to increase production of an EAF (electric arc furnace) by reduction of melting time, one can increase transferred power to the EAF. In other words a certain value of energy can be transferred to the EAF in less time. The transferred power to the EAF reduces when series reactors are utilized in order to have stable arc with desired characteristics. To compensate the reduced transferred power, the secondary voltage of the EAF transformer should be increased by tap changing of the transformer. On the other hand, after any tap changing of the EAF transformer, improved arc stability is degraded. Therefore, the series reactor and EAF transformer tap changing should be simultaneously determined to achieve arc with desired characteristics. In this research, three approaches are proposed to calculate the EAF system parameters, by which the optimal set-points of the different series reactor and EAF transformer taps are determined. The electric characteristics relevant to the EAF for the all transformer and series reactor taps with and without SVC (static VAr compensator) are plotted and based on these graphs the optimal set-points are tabulated. Finally, an economic evaluation is also presented for the methods. [ABSTRACT FROM AUTHOR]

There are several definitions for reactive power in non sinusoidal networks. However, there is still no unique definition that is accepted by the electrical communities. In this study seven of the most common power decompositions, are simulated and compared in AC electric arc furnace (EAF) plants. For this purpose a large amount of data was collected for a considerable number of melting processes, including currents and voltages of eight ac EAFs installed in Mobarakeh steel company (MSC), Isfahan, Iran. The reactive power definitions are compared to each other utilizing different methods and some statistic indices. By calculating the average of each definition over all recorded data the definitions that are beneficial to customers and those that are beneficial to the utilities are obtained. The problem encountered here is that the ideal definition for reactive power is unknown and there is no criterion to evaluate and compare the different definitions. So here the mean of the seven most common definitions is considered as the criterion and the distances of various definitions from it are calculated. Also, the capacitance that completely compensates the EAF reactive power is calculated according to each of the discussed definitions. [ABSTRACT FROM AUTHOR]

• The proposed method utilizes the direct features of the electric arc. • Our method detects incipient fault considering the minority and momentary features. • Fault data are obtained from both simulation and experimental recorded data. • Our method guarantees simplicity in implementation, speed, and accuracy. • It distinguishes transients such as inductive load change and capacitor switching. • It discriminates the incipient fault from the harmonic load. Incipient faults are known as the faults mainly occurring on the junctions of underground power cables. Clearing quickly, these faults are difficult to detect by conventional protection equipment, leading to insulation degradation and permanent fault in the power cable over time. In this paper, employing similarity functions, a new method is put forward to distinguish the existing fault from other similar events with both high speed and accuracy. The proposed algorithm consists of a two-stages algorithm including change detection, disturbance identification, and fault detection. The proposed method performs the calculation on the voltage signal that is recorded at the sending end of the line. The cross-correlation function is employed for identifying change detection and determining disturbance time. Afterward, using sinusoidal curve fitting, the algorithm can distinguish the incipient fault from sudden load change, capacitor switching, and harmonic load. The accuracy of the proposed approach is verified by some experimental data and 6272 simulation data obtained from four types of well-known arc models, capacitor switching, and also load variations. [ABSTRACT FROM AUTHOR]

Abstract: Prediction of electric arc furnace (EAF) reactive power is an appropriate solution to compensate for static VAr compensator delay and improve its performance in flicker reduction. A linear autoregressive moving average (ARMA) can only pull out the linear deterministic (LD) component of EAF reactive power time series. For the prediction to be made through both nonlinear deterministic (ND) and LD components, employing nonlinear models is necessary. However, before developing the nonlinear models for prediction, the necessity of the employing them should be verified by investigating the significance of the ND components in the process. This paper presents a novel approach for wide analysis of nonlinear behavior of EAFs reactive power time series related to eight ac EAFs installed in Mobarakeh steel industry, Isfahan, Iran to answer the question about the importance of their ND components. In the approach, a suitable linear auto regressive moving average (ARMA) model with order (4,4) is used for the time series to extract the residual time series. Then, a number of well established nonlinear analysis techniques such as time delay reconstruction, surrogate data, delay vector variance and recurrence plot methods are applied to the original and residual time series. To describe the nonlinear characteristics of the time series, some new indices are defined. They quantify the significance of the ND component in compare with LD component. [Copyright &y& Elsevier]

Abstract: The time varying nature of electric arc furnace (EAF) gives rise to voltage fluctuations which produce the effect known as flicker. The ability of static VAr compensator (SVC) in flicker reduction is limited by delays in thyristor ignition. To improve SVC performance in flicker compensation, EAF reactive power can be predicted for a half-cycle ahead, by using appropriate autoregressive moving average (ARMA) models. This paper uses huge field data collected from ac arc furnaces, and demonstrates that the EAF reactive power models coefficients are different from one data record to another and do not follow any specific law. Therefore, it is necessary to update the model coefficients for prediction purposes. For this purpose, two major adaptation algorithms, the least mean square (LMS) and recursive least square (RLS) are used to determine online the prediction relationship coefficients. By applying the methods to the data records and using some indices such as newly defined indices based on concepts of flicker frequencies and power spectral density, the transient and steady state performances of the methods are studied in EAF reactive power prediction. A simulation example on the application of the predictive models in a SVC control system is presented. [Copyright &y& Elsevier]

• Identifying abnormality and determining time of disturbance occurrence. • Discriminating the incipient fault from capacitive switching and sudden load variations. • Discriminating the incipient fault from harmonic loads considering arc fault models. The incipient faults which mainly occur due to the electric arc occurrence in the power cables with insulation defects are hardly detectable by the conventional protective relays, and over time can develop into a permanent fault in the system. Employing Kalman filter, this paper puts forward a method to detect the incipient faults and to discriminate them from other similar incidents in the power system. The proposed method is established on the comparison between the waveform of the measured voltage and fundamental component of the measured voltage, estimated by Kalman filter algorithm in the sending end of the cable during the fault. Employing the difference between the measured and estimated waveforms, the incipient fault detection and discrimination are carried out within two stages. In the first stage, event detection is relegalized by comparing the standard deviation of the obtained error with a certain threshold. The second stage is conducted to find the incipient fault based on the non-attenuating characteristic and quasi-periodic nature of the incipient fault. The feasibility of the proposed method is verified through computer simulation using four different electric arc models and also the acquired experimental data from real incipient faults. [ABSTRACT FROM AUTHOR]

The ability of static VAr compensators (SVC) to reduce the flicker caused by electric arc furnaces and other variable VAr loads depends on their speed, which is limited by delays in reactive power measurement and thyristor ignition. In this paper, a predictive method is proposed to compensate the delay time and, hence, to improve the SVC performance. Previous samples of the load reactive power are used to predict its future values. Predicted VAr is then applied as a reference for reactive power compensation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]