ANN-BASED REAL-TIME PARAMETER OPTIMIZATION VIA GA FOR SUPERHEATER MODEL IN POWER PLANT SIMULATOR

摘要：

In order to rapidly optimize superheater model parameters to achieve the required precision, ANN and GA are combined to solve the problem. Since the classic optimization methods are not appropriate for mechanism model in power plant simulator, GA is applied to optimize model parameters. Input data, output data of model and optimized parameters are normalized to make learning sample. After ANN is trained with back-propagation algorithm, it is able to optimize model parameters in real-time. Simulation result shows that superheater model optimized by this method achieves the required accuracy. The method replaces manual parameter regulation and shortens optimization time. It is a general method, provides a new way for parameter optimization for thermal equipment model in power plant simulator.

关键词： ANN GA parameter optimization real-time superheater model power plant simulator

作者: JIN MA BING-SHU WANG YONG-GUANG MA

作者单位: School of Control Science and Engineering, North China Electric Power University, Baoding 071003,China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 2269-2273

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）