基于鲸鱼优化算法的汽轮机热耗率模型预测

被引：125

作者：

牛培峰

吴志良

马云鹏

史春见

李进柏

机构：

[1] 燕山大学电气工程学院

来源：

化工学报 | 2017年 / 68卷 / 03期

关键词：

汽轮机; 热耗率; 鲸鱼优化算法; 快速学习网; 反向学习算法;

D O I：

暂无

中图分类号：

TP18 [人工智能理论]; TM621.3 [发电设备];

学科分类号：

080804 [电力电子与电力传动]; 140502 [人工智能];

摘要：

为了准确地建立汽轮机热耗率预测模型,提出了一种基于反向学习自适应的鲸鱼优化算法(AWOA)和快速学习网(FLN)综合建模的方法。首先将改进后的鲸鱼算法与经典改进的粒子群、差分进化算法和基本鲸鱼算法进行比较,结果证明其具有更高的收敛精度和更快的收敛速度;然后采用某热电厂600 MW超临界汽轮机组现场收集的运行数据建立汽轮机热耗率预测模型,并将改进后的鲸鱼算法优化的快速学习网模型的预测结果与基本快速学习网及经典改进的粒子群、差分进化算法和基本鲸鱼算法优化的快速学习网模型预测结果相比较。结果表明,AWOA-FLN预测模型具有更高的预测精度和更强的泛化能力,更能准确地预测汽轮机的热耗率。

引用

页码：1049 / 1057

页数：9

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