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A New Dynamic Intelligent Time Domain Arc Furnace Modeling based on Combination Adaptive Neuro-fuzzy Inference System and Chain Code

机译:基于自适应神经模糊推理系统和链码的动态智能时域电弧炉建模

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摘要

Electric arc furnaces have intense non-linear, time variant, and uncertain characteristics bringing about voltage and current harmonics, unbalances, and voltage flicker. In this article, a new three-phase dynamic and intelligent arc furnace in time domain based on adaptive neuro-fuzzy inference system is proposed. In this approach, the adaptive neuro-fuzzy inference system is trained by several actual electric arc furnace operations, and a pattern detection based on the modified chain code method is presented. Hence, electric arc furnace performance is analyzed by applying a preprocessing stage using low-pass average filters and chain code method that recognizes the defects in different patterns such as pulses, impulses, similar and inverse profiles, steps, etc. A chain code is a lossless compression algorithm for detecting the behavior of different data. The adaptive neuro-fuzzy inference system is simultaneously trained by electric arc furnace operation at each stage by means of the patterns. For data collection, a power quality analyzer and oscilloscope are connected to different actual electric arc furnaces. Finally, the electric arc furnace model is presented as a voltage source depending on a current similar to a non-linear black box. This model has no linearity in arc characteristic and can show an actual arc.
机译:电弧炉具有强烈的非线性,时变和不确定的特性,导致电压和电流谐波,不平衡和电压闪烁。本文提出了一种基于自适应神经模糊推理系统的新型时域三相动态智能电弧炉。在这种方法中,通过几次实际的电弧炉操作来训练自适应神经模糊推理系统,并提出了基于改进链码方法的模式检测。因此,通过使用低通平均滤波器和链式编码方法进行预处理阶段来分析电弧炉的性能,链式编码方法可以识别出不同模式(例如脉冲,脉冲,相似和相反的轮廓,阶跃等)中的缺陷。用于检测不同数据行为的无损压缩算法。自适应神经模糊推理系统在每个阶段都通过模式的电弧炉操作同时进行训练。为了进行数据收集,将电能质量分析仪和示波器连接到不同的实际电弧炉。最后,根据类似于非线性黑匣子的电流,将电弧炉模型表示为电压源。该模型的电弧特性没有线性,可以显示实际的电弧。

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