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电极调节系统是电弧炉炼钢过程不可缺少的基本装备。目前常用的电极调节器大多是基于单相意识,导致电极调节中电极往往误动作,大大影响了电弧炉的运行效益。在模糊控制和神经网络的基础上,探索研究一种基于三相意识的电弧炉控制新方法,采用模糊神经网络调节器控制对电弧炉电极进行调节,控制弧流弧压稳定在一定范围之内,使电弧炉冶炼达到有功功率最大化,进一步提高电弧炉的综合运行效益,降低能耗、减轻对电网危害。并用matlab对模糊神经网络进行仿真。结果表明,本网络具有较快的训练速度和较高的泛化能力,满足电弧炉控制的要求,其控制性能优于常规电弧炉控制系统。
Electrode adjustment system is EAF steelmaking process indispensable basic equipment. At present, most commonly used electrode regulators are based on the single-phase awareness, which leads to the electrode in the electrode adjustment often misoperation, greatly affecting the operating efficiency of the electric arc furnace. On the basis of fuzzy control and neural network, a new method of arc furnace control based on three-phase awareness is explored. The control of arc furnace electrode is controlled by fuzzy neural network regulator to control arc arc voltage stability within a certain range , So that the electric arc furnace smelting to achieve the maximum active power to further improve the comprehensive operation of the electric arc furnace efficiency and reduce energy consumption and reduce the hazards on the grid. And use matlab to carry on the simulation to the fuzzy neural network. The results show that this network has faster training speed and higher generalization ability to meet the requirements of EAF control, and its control performance is superior to that of conventional EAF control system.