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针对中电投景德镇发电厂660 MW超超临界机组存在变负荷速率低、主要控制参数波动大、主再热汽温偏低的问题,本研究通过有机融合预测控制技术、神经网络学习技术及自适应控制技术,提出了现代大型超超临界火电机组AGC控制的先进解决方案,并利用INFIT(基于PLC的先进热工控制应用平台)实时优化控制系统将上述控制方案在现场成功实现。机组上的实际应用表明,采用INFIT平台实现的先进控制技术明显提高了机组的负荷调节性能、机组运行稳定性和平均主再热汽温,全方面提高了机组的整体运行安全性和经济性。
In order to solve the problem that the 660 MW ultra-supercritical unit of China Power Investment Jingdezhen Power Plant has such problems as low variable load rate, large fluctuation of main control parameters and low main reheat steam temperature, this study adopted organic fusion predictive control technology, neural network learning technology and adaptive control Technology, advanced advanced solutions for AGC control of large-scale ultra-supercritical thermal power units are put forward. The above control scheme has been successfully implemented in the field by using INFIT (Advanced Thermal Control Application Platform based on PLC) real-time optimization control system. The practical application on the unit shows that the advanced control technology implemented by INFIT platform obviously improves the unit load regulation performance, unit operation stability and average main reheat steam temperature, and improves the overall operation safety and economy of the unit in all aspects.