摘要
针对某电厂在二级过热器喷水减温扰动下的主汽温对象的动态特性,提出了基于电厂运行历史数据的主汽温系统粒子群(PSO,Particle Swarm Optimization)辨识方法,建立了主汽温对象的动态数学模型,并用不同运行期间的数据对模型进行验证,仿真结果表明,建立的主汽温模型是比较准确的。
A kind of identification method based on the power plant operation historical data using Particle Swarm Optimization （PSO） algorithm is proposed according to the main steam temperature dynamic characteristics under the disturbances of the secondary superheater spray water. The dynamic mathematical models of main steam tem- perature system are obtained. The data under the same conditions but different operation intervals are used to verify the obtained model. Results show that the model obtained is convietive.
出处
《电力科学与工程》
2012年第12期1-5,共5页
Electric Power Science and Engineering
关键词
1
000
MW超超临界机组
主汽温系统
粒子群优化
辨识
1 030 MW uhra-supercritical units
main steam temperature system
particle swarm optimization
identification
