文献类型：期刊文献

中文题名：局部通风机调速控制系统的研究

英文题名：Study on adjustment speed control system of auxiliary fan

作者：王淑芳[1];王剑波[2];张丽[3];王汝琳[3]

第一作者：王淑芳

机构：[1]北京联合大学机电学院;[2]山西潞安潞欣投资咨询有限公司;[3]中国矿业大学(北京)机电学院

第一机构：北京联合大学机器人学院

年份：2006

卷号：31

期号：6

起止页码：813-818

中文期刊名：煤炭学报

外文期刊名：Journal of China Coal Society

收录：CSTPCD;;EI(收录号：20070410389870);Scopus(收录号：2-s2.0-33846344028);北大核心:【北大核心2004】；CSCD:【CSCD2011_2012】；

基金：国家自然科学基金资助项目(50404015)

语种：中文

中文关键词：局部通风机;调速控制系统;瓦斯超限排放;自学习模糊控制系统

外文关键词：auxiliary fan ; adjustment speed control system ; gas discharging ; self learning fuzzy control system

摘要：根据掘进巷道通风需求以及风流特征,论述了正常通风和瓦斯超限排放2种不同工作方式下常规局部通风机双模模糊控制策略的制定,控制策略的目标是既要保证工作面安全又要实现节能效果.鉴于4个输入量对输出的影响不同,采用3层BP神经网络计算各自的权重值.在此基础上,建立了适用于瓦斯排放的自学习模糊控制模型,并选用直接转矩方式实现风机的速度控制.组建了基于TI公司TMS320LF2407DSP与IPM相结合的试验平台,实验结果显示,该控制系统能安全有效地完成掘进巷道的正常通风和瓦斯超限排放功能,并达到节能的目的.
Combining with ventilation requirements and airflow regulation in coalmine, control strategy which aims at both safety and energy saving was established at length. Moreover, conventional double fuzzy control model was found to can＇y out two work mode, normal ventilation and gas discharging. Since four input signals affect output in different degree, three layer BP neural network was employed to compute weight. Based on conventional fuzzy model, self learning fuzzy model for auxiliary fan was built to tackle with normal ventilation and gas discharging problem. Direct torque control style was applied to perform speed adjusting of auxiliary fan. In order to testify the control strategy, an experiment platform including DSP and IPM was founded. The experiment results show that the control strategy is effective to fulfill the function of ventilation and gas discharge in heading laneway.