基于鲸鱼优化算法的汽轮机热耗率模型预测

doi:10.11949/j.issn.0438-1157.20161099

化工学报 ›› 2017, Vol. 68 ›› Issue (3): 1049-1057.DOI: 10.11949/j.issn.0438-1157.20161099

基于鲸鱼优化算法的汽轮机热耗率模型预测

牛培峰, 吴志良, 马云鹏, 史春见, 李进柏

燕山大学电气工程学院, 河北秦皇岛 066004

收稿日期:2016-08-04 修回日期:2016-11-11 出版日期:2017-03-05 发布日期:2017-03-05
通讯作者: 牛培峰,npf882000@163.com
基金资助:
国家自然科学基金项目（61573306，61403331）。

Prediction of steam turbine heat consumption rate based on whale optimization algorithm

NIU Peifeng, WU Zhiliang, MA Yunpeng, SHI Chunjian, LI Jinbai

School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China

Received:2016-08-04 Revised:2016-11-11 Online:2017-03-05 Published:2017-03-05
Contact: 10.11949/j.issn.0438-1157.20161099
Supported by:
supported by the National Natural Science Foundation of China (61573306,61403331).

摘要/Abstract

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。

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

Abstract:

In order to establish an accurate prediction model for heat consumption rate of steam turbines, an integrated modeling method was proposed by combination of oppositely adaptive whale optimization algorithm (AWOA) and fast learning network (FLN). Compared to basic whale algorithm, improved particle swarm optimization algorithm, and differential evolution algorithm, the improved whale algorithm had higher convergence accuracy and faster convergence speed. A prediction model for heat consumption rate of a 600 MW supercritical steam turbine generator set in a thermal power plant was established from the collected operation data, which was also compared to FLN model, improved particle swarm optimization, differential evolution algorithm, and whale optimization algorithm. The results show that the AWOA-FLN prediction model had higher prediction accuracy and stronger generalization ability, which therefore could predict heat consumption rate of steam turbine more accurately.

Key words: steam turbine, heat consumption rate, whale optimization algorithm, fast learning network, opposition-based learning

中图分类号:

TK267

牛培峰, 吴志良, 马云鹏, 史春见, 李进柏. 基于鲸鱼优化算法的汽轮机热耗率模型预测[J]. 化工学报, 2017, 68(3): 1049-1057.

NIU Peifeng, WU Zhiliang, MA Yunpeng, SHI Chunjian, LI Jinbai. Prediction of steam turbine heat consumption rate based on whale optimization algorithm[J]. CIESC Journal, 2017, 68(3): 1049-1057.

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基于鲸鱼优化算法的汽轮机热耗率模型预测

Prediction of steam turbine heat consumption rate based on whale optimization algorithm

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