摘要
本文针对热工对象的特性以及传统辨识方法的不足,选取某600MW超临界机组SIS系统的实际运行数据,并通过粒子群算法和神经网络算法这两种智能算法对燃料量影响下的主汽压动态特性进行了系统辨识。结果表明,智能算法在系统辨识过程中具有较高的可靠性和有效性。
Aiming at the characteristic of thermal system and shortcomings of traditional identification methods, this paper selected the actual operating data from supervisory information system of a 600MW super-critical power unit and adopted particle swarm optimization and neural network the two intelligent identification algorithms to identify the main-steam pressure system under the influence of fuel flow. The result showed that the two intelligent identification algorithms had the high reliability and validity.
出处
《仪器仪表用户》
2016年第7期86-89,90,共5页
Instrumentation
关键词
主汽压
现场数据
粒子群算法
神经网络
辨识
main-steam pressure
actual operating data
particle swarm optimization
neural network
identification
