摘要
传统热力发电厂化学水处理中海水淡化系统蒸发器(简称MED)故障方法难以准确诊断,存在因实测误差导致收敛性不足等情况。本文针对印尼雅加达龙湾3×315MW机组电厂化学水补水的海水淡化装置的实际情况,提出基于Matlab的最优化遗传算法(Genetic Algorithm,简称GA),对海水淡化系统各个蒸发器进行故障诊断。遗传算法经过5000次迭代以后,具有迅速收敛性,大大提高了工作效率,使能够迅速找到故障所在位置。
Traditional chemical water treatment in thermal power plants evaporator desalination system （referred to, MED） method is difficult to accurately diagnose faults, there is experimental error caused by lack of convergence and so on. In this paper, Jakarta, Indonesia Longwan 3×315MW power plant unit replenishment of chemical water desalination plant to the actual situation Matlab optimization based on genetic algorithm （Genetic Algorithm, referred to as GA）, each of the evaporator desalination systems for fault diagnosis. After genetic algorithm after 5000 iterations, with rapid convergence, greatly improving efficiency, so can quickly find the fault location.
出处
《电气技术》
2011年第6期19-21,共3页
Electrical Engineering
关键词
海水淡化
蒸发器
遗传算法
故障诊断
最优化
sea water desalination
evaporator
GA Troubleshooting optimization~ fault diagnosis
optimization
