流体机械

2018, v.46;No.558(12) 13-17

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基于差分算法的间歇大流量气动系统优化控制
The Optimization Control of the Intermittent Large-flow Pneumatic System Based on the Differential Algorithm

张业明;李珂;魏绍亮;王帅;蔡茂林;
ZHANG Ye-ming;LI Ke;WEI Shao-liang;WANG Shuai;CAI Mao-lin;Henan Polytechnic University;Beihang University;

摘要(Abstract):

针对间歇大流量用气设备在开启和关闭的瞬间气动系统管网支路压力会产生较大的波动,严重影响供气质量的问题,提出了一种利用管网支路压力的差分算法来识别间歇性大流量用气设备的方法,通过检测管网支路压力信号并计算其平均变化率来判断支路压力平均变化率是否出现突变,进而识别间歇性大流量设备的开启与关闭;接着,搭建了空压机群气动管网测试试验台,验证该识别方法的可行性;最后,利用差分算法,对空气压缩机群的传统压力控制策略进行了整体优化。试验结果表明:(1)该算法能够快速准确地识别间歇性大流量用气设备,备用空压机的启闭响应时间减小60 s以上;(2)优化后的空压机群控制方法的压力波动范围更小,主管路压力波动范围减小了80%。
For the problem of greater pressure fluctuation of the branch pipe network of the pneumatic system at the moment of opening and closing of the intermittent large-flow gas consuming equipment,which seriously affects the quality of gas supply,a new method is put forward to identify the intermittent large-flow gas consuming equipment by using the differential algorithm of the pressure of pipe network branch,judge whether the average change rate of the branch pressure changes suddenly by detecting the pressure signal of the pipe network branch and calculating its average change rate,and further identify the opening and closing of the intermittent large-flow gas consuming equipment. Then, the test bench for pneumatic pipe network of air compressor group was set up to verify the feasibility of this identification method. Finally, the traditional pressure control strategy of air compressor group was optimized using the differential algorithm. The test results show that:(1)The algorithm can identify the intermittent large-flow gas consuming equipment quickly and accurately,and the opening and closing response time of the air compressor was reduced by more than 60 seconds;(2)The pressure fluctuation range for the control method of the optimized air compressor group was smaller,and the pressure fluctuation range of the main pipeline was reduced by 80%.

关键词(KeyWords): 间歇性;大流量;压力突变;差分算法;优化控制
intermittent;large flow;pressure mutation;the differential algorithm;optimization control

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基金项目(Foundation): 国家自然科学基金项目(51505133);; 河南省高校基本科研业务费专项资金资助项目(NSFRF140120);; 河南省科技攻关项目(172102310674);; 河南理工大学研究生案例库课程建设项目(2016YAL09)

作者(Author): 张业明;李珂;魏绍亮;王帅;蔡茂林;
ZHANG Ye-ming;LI Ke;WEI Shao-liang;WANG Shuai;CAI Mao-lin;Henan Polytechnic University;Beihang University;

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