Adaptive control of the electric arc furnace electrodes using Lyapunov design

Parsapoor, Amir; Ataei, Mohammad; Kiyoumarsi, Arash

doi:10.1109/iccas.2007.4406592

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…Thus, the model for studying the influence of EAF on the power supply network should contain a model of the electric circuit taking into account the nonlinearity of the volt-ampere characteristic of the arc, the model of the control system of EAF electrode movement and the model of random arc length processes [9][10][11][12][13]. Depending on the class of tasks, researchers often use simplified models of individual subsystems.…”

Section: Introductionmentioning

confidence: 99%

A Mathematical Model of Electrical Arc Furnaces for Analysis of Electrical Mode Parameters and Synthesis of Controlling Influences

et al. 2022

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The synthesis of a real-time model of the electric mode of an arc steelmaking furnace is shown. This model is based on the method of medium-voltage and can be used to analyze the impact of the arc steelmaking furnace on the supply network at different stages of the melting process at different control systems of the electromechanical electrode movement system, and to create model-predictive control systems of electrode movement, when control using hardware in the loop technology. To implement the chosen method, the dynamic arc resistance was used. The last was obtained on the basis of the averaged experimentally taken volt-ampere characteristics of the arc. In paper was proposed its approximation as a current function and arc length. The simulation results obtained using the created model for different operation modes of the arc steelmaking chipboard for a system with a traditional regulator of the electrode movement system are demonstrated.

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Section: Introductionmentioning

confidence: 99%

A Mathematical Model of Electrical Arc Furnaces for Analysis of Electrical Mode Parameters and Synthesis of Controlling Influences

et al. 2022

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“…The most widespread architecture of the electrode movement control system in an AFS is the MISO structure (several inputs, single output), which, as a rule, implements independent control of each phase. The use of adaptive control or intelligent control technologies in such a structure makes it possible to improve the quality of control compared to the system synthesized on the basis of classical control theory [8][9][10][11][12][13]. However, from an electrical point of view, the phases are interdependent, which causes unproductive movements in the electrodes and, thus, impairs the quality of control.…”

Section: Introductionmentioning

confidence: 99%

Combined Control System for the Coordinates of the Electric Mode in the Electrotechnological Complex “Arc Steel Furnace-Power-Supply Network”

et al. 2022

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To stabilize the electrical mode of an arc steelmaking furnace in the initial stages of melting, it is advisable to use a high-speed current-limiting system, in addition to the traditional electrode movement control system. This system is implemented by including the primary winding of the furnace transformer choke, controlled by thyristors. The use of such a system, on the one hand, reduces the negative impact of the arc steel furnace on the the power supply network operation and, on the other, affects the operation of the electrode movement system, built on the principle of an impedance regulator. In order to analyze the mutual influences between such systems, a mathematical model for the power supply and control system of the arc steelmaking furnace was created. The developed model can work in real time, which corresponds to the world trends of modern control system synthesis for complex technological objects. In the created model, the work of the combined control system with different approaches to the formation of the control effect in the high-speed circuit and the effect of the combined control system on the power supply system of the ASF are analyzed.

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“…Then, an adaptive feedback controller was designed based on this idea. Using Lyapunov design, Parsapoor et al 3 also proposed an adaptive control method for the ERS. Srdic and Nedeljkovic 4 proposed a fast and robust predictive controller to reduce the flicker caused mainly by reactive-power variations during the electrode short-circuits.…”

Section: Introductionmentioning

confidence: 99%

Detecting Outliers in Electric Arc Furnace under the Condition of Unlabeled, Imbalanced, Non-stationary and Noisy Data

Wang

Mao

2018

Measurement and Control

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The presence of outliers is the main reason leading to ineffectiveness of advanced data-driven control methods in electric arc furnace systems. This paper proposes a hybrid method dedicated to detecting outliers in electric arc furnace systems, where process data are characterized as unlabeled, imbalanced, non-stationary and noisy. First, the raw data are divided into certain number of clusters. Then, with each cluster, a one-class classifier can be trained. So with these well-trained sub-models, new test points can be investigated. Those points that are rejected by all sub-models will be labeled as outliers. With the combination of one-class classification and clustering technique, the intricate data in electric arc furnace can be processed effectively. In addition, the detector will be updated with a specific strategy to enhance its adaptiveness. A series of experiments are carried out, and comparative results have shown the effectiveness of our method.

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Adaptive control of the electric arc furnace electrodes using Lyapunov design

Cited by 6 publications

References 4 publications

A Mathematical Model of Electrical Arc Furnaces for Analysis of Electrical Mode Parameters and Synthesis of Controlling Influences

A Mathematical Model of Electrical Arc Furnaces for Analysis of Electrical Mode Parameters and Synthesis of Controlling Influences

Combined Control System for the Coordinates of the Electric Mode in the Electrotechnological Complex “Arc Steel Furnace-Power-Supply Network”

Detecting Outliers in Electric Arc Furnace under the Condition of Unlabeled, Imbalanced, Non-stationary and Noisy Data

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