期刊文献+

STD总线工业控制机在过程控制中的应用 被引量:4

The Application of STD Bus Industry Control Machine in The Process Control
下载PDF
导出
摘要 目的采用抗干扰能力强的STD总线工业控制机作为控制主机,解决铝电解过程中的干扰问题,提高控制性能.方法控制系统硬件采用模块化、组合化方法,具有扩展方便,便于维护等特点;软件设计遵循结构化和模块化设计原则,采用了先进的控制思想,对电解槽不同的工作状态,采用不同的控制策略,使电解槽工作在最佳的工作状态.结果STD总线工业控制机系统可靠性高,便于维护,抗干扰能力强,动态性能好,稳态精度高,运行效果良好.结论STD总线工业控制机对复杂的生产过程,特别是扰动大的生产过程,具有很强的适用性,可大大提高控制效果. In order to reduce the strong interference of aluminum electrolysis process, STD bus industry control machine which has strong anti-jamming ability is used as control host computer to improve control capability. Modularization and combination are applied in the hardware of the control system and the design principle of structure and the modularization are used in the software design. Advanced control idea and different control tactics are adopted in the system, based on the different working state of electrolysis trough to make sure that the electrolysis trough is at its best working state. The system software programs work with VC, and have several characters such as extension and good maintenance. The system has strong dependability and anti-jamming ability, convenient maintenance, good dynamic capability and running effect, and high precision of stabilization. Aiming at the complicated process control especially in the great disturbing producing course, STD bus industry control machine which has strong application can improve the control effect.
出处 《沈阳建筑大学学报(自然科学版)》 CAS 2005年第5期573-576,共4页 Journal of Shenyang Jianzhu University:Natural Science
基金 建设部基金项目(03-2-117)
关键词 工业控制机 过程控制 铝电解 抗干扰 industry control machine, process, control, aluminum electrolysis
  • 相关文献

参考文献3

二级参考文献13

  • 1杨煜普,许晓鸣,张钟俊.基于模糊神经网络的控制规则获取及置信度估计问题[J].模式识别与人工智能,1994,7(1):53-59. 被引量:13
  • 2王耀南.神经网络自适应模糊控制在温度控制系统中的应用[J].信息与控制,1996,25(4):245-251. 被引量:27
  • 3王勇骥,涂健.神经元网络控制[M].北京:机械工业出版社,1998:303-305.
  • 4Dovan T, Dillon T S, Berger C S. "A Microcomputer Based On-Line Identification Approach to power System Dynamic Modeling" [J]. IEEE Trans On Power Systems, Aug. 1987, 2(2): 529 - 536.
  • 5Thomas R J and Bih-yuan ku. "Approximations of power system dynamic load characteristics by artificial networks" [ A]. Proceedings of the 1^st international forum on Applications of Neural Networks to Power Systems[C]. Washington, 1991.
  • 6Cho H S, Park J K, Kim G W, et al." Power System Transient Stability Analysis Using a New Condensed Nearest Neighbor Rule for Kohonen Neural Network"[A]. ISAP'99[C]. Rio de Janeiro(brazil): 1999.
  • 7Alireza Khotanzad, Peng P, Marks R J." Short Term Peak Load Forecast Using a Neuro-Fuzzy Model" [A].ISAP' 99 [ C]. Rio de Haneiro(Brazil): 1999.
  • 8Srinivasan K, Robichaud Y, Rodgers G. "Load Response Coefficient Monitoring System: Theory and Field Experience"[J]. IEEE Trans. On Power Apparatus and System, Vol. PAS - 100 Aug. 1981: 3818 -3827.
  • 9Wprice W, Wirgan K A, EI-kady M A. "Load Modeling Power Flow and Transient Stabulity Computer Studies"[J]. IEEE Trans. On Power Systems, 1988,3(1): 180 - 187.
  • 10Papalexopoulos A D, Hesterberg T C. A Regression-Based Approach to Short-Term System Load Forecasting[J]. IEEE Tran. PWRS, 1990, 5(4): 1535 - 1644.

共引文献10

同被引文献24

引证文献4

二级引证文献7

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部