Enhancement of SVC performance in electric arc furnace for flicker suppression using a Gray‐ANN based prediction method

Abstract

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.

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

“…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].…”

“…In each iteration, the OF value corresponding to each particle is evaluated, and the global best position denoted as g best and the individual best position denoted as P i best are updated. e velocity of particles is updated using (9), and they are transferred to the new positions using (10).…”

Power Quality Enhancement in Electric Arc Furnace Using Matrix Converter and Static VAR Compensator

“…The proposed scheme uses a matrix converter and an input filter that supplies power to the electric furnace, as illustrated in Figure 2. The output voltage and current of the MC to input voltage and current are derived as follows [30][31][32][33]:…”

“…The proposed scheme uses a matrix converter and an input filter that supplies power to the electric furnace, as illustrated in Figure 2. The output voltage and current of the MC to input voltage and current are derived as follows [30][31][32][33]: where S T denotes a transpose of matrix S, and states of each bidirectional switches are represented as Sxy (x indicates A, B, and C and y denote a state a, b, and c) that can be derived as follows:…”

Optimal coordination of static VAR compensators, fixed capacitors, and distributed energy resources in Egyptian distribution networks

“…The coordination problem between OLTC and SVC has been considered for voltage regulation in unbalanced distribution systems with dispersed generation 23 . Also, the reference signal of the SVC in electrical arc furnace has been controlled based on Gray artificial neural prediction of its reactive demand for half cycle ahead 24 …”