工程科学学报2016，Vol.38Issue(6)：861-866,6.DOI:10.13374/j.issn2095-9389.2016.06.017
基于 OS-ELM 的 CCPP 副产煤气燃料系统在线性能预测
Online performance prediction of CCPP byproduct coal-gas system based on online sequential extreme learning machine
摘要
Abstract
Aiming at the problem of mismatch between the model and the process for a byproduct coal-gas system in a combined cycle power plant ( CCPP) due to frequent changes in working conditions, this article introduces a method for online performance prediction of the CCPP byproduct coal-gas system based on an online sequential extreme learning machine (OS-ELM). Firstly, by analyzing the working principle of each main component in the byproduct coal-gas system and using the fluid mechanics, energy conservation and mass conservation principles, a mechanistic model is established for performance prediction of the byproduct coal-gas system, which essentially consists of scrubbers, centrifugal compressors, and coolers. Further, the OS-ELM and the sliding window technique are also used to correct the error of the mechanistic model, thus we realize the accurate prediction of export parameters and the update of the model in time. Simulation results show that this method can accurately predict the pressure ratio and temperature ratio of the byproduct coal-gas system and track the change in coal-gas system working conditions and the characteristics drift, which meet the needs of actual industrial production.关键词
联合循环发电厂/煤气/性能预测/学习机/在线系统Key words
combined cycle power plants/coal gas/performance prediction/learning machines/online systems分类
信息技术与安全科学引用本文复制引用
褚菲,叶俊锋,马小平,张淑宁,吴奇..基于 OS-ELM 的 CCPP 副产煤气燃料系统在线性能预测[J].工程科学学报,2016,38(6):861-866,6.基金项目
国家自然科学基金资助项目(61503384,61473299,61374043) （61503384,61473299,61374043）
江苏省自然科学基金资助项目(BK20150199) （BK20150199）
江苏省博士后基金资助项目(1501081B) （1501081B）
中国博士后基金资助项目(2015M581885) （2015M581885）
