ASOS-ELM建模方法及在汽轮机热耗率预测中的应用

doi:10.11949/j.issn.0438-1157.20180293

化工学报 ›› 2018, Vol. 69 ›› Issue (9): 3924-3931.DOI: 10.11949/j.issn.0438-1157.20180293

ASOS-ELM建模方法及在汽轮机热耗率预测中的应用

牛培峰¹, 王枭飞¹, 刘楠², 王愿宁³, 常玲芳¹, 张先臣¹

1. 燕山大学电气工程学院, 河北秦皇岛 066004;
2. 燕山大学, 河北秦皇岛 066004;
3. 大连海事大学环境科学与工程学院, 辽宁大连 116026

收稿日期:2018-03-19 修回日期:2018-06-03 出版日期:2018-09-05 发布日期:2018-09-05
通讯作者: 王枭飞
基金资助:
国家自然科学基金项目（61573306）。

Modeling method of ASOS-ELM and its application in prediction of heat rate of steam turbine

NIU Peifeng¹, WANG Xiaofei¹, LIU Nan², WANG Yuanning³, CHANG Lingfang¹, ZHANG Xianchen¹

1. School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China;
2. Yanshan University, Qinhuangdao 066004, Hebei, China;
3. College of Environment Science and Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China

Received:2018-03-19 Revised:2018-06-03 Online:2018-09-05 Published:2018-09-05
Supported by:
supported by the National Natural Science Foundation of China (61573306).

摘要/Abstract

摘要：

针对极限学习机（ELM）不能准确地预测汽轮机热耗率的问题，结合群智能优化算法，提出一种改进的共生生物搜索算法和极限学习机（ASOS-ELM）综合建模的方法。该方法利用改进的共生生物搜索（ASOS）算法优化ELM隐层激活函数的参数，求得最优的ELM模型。再将ASOS-ELM模型应用到热耗率建模中，首先用ELM初始化热耗率预测模型，以输出热耗率的均方根误差（RMSE）作为算法的适应度值，然后通过ASOS算法找到合适的ELM参数，从而得到准确的热耗率预测模型。并将热耗率预测的结果与传统的ELM模型、ASOS算法优化支持向量回归（SVR）模型、改进的粒子群算法（PSO）和基本的共生生物搜索算法（SOS）优化的ELM作对比。结果表明，ASOS-ELM模型在处理复杂的数据模型中，具有精确的预测能力与快速的收敛速度，为汽轮机热耗率建模提供了新思路。

关键词: 汽轮机, 热耗率, 算法, 极限学习机, 优化, 模型

Abstract:

The extreme learning machine (ELM) problem can not quickly and accurately predict heat rate. Combined with swarm intelligence optimization algorithm, an ameliorated symbiotic organisms search algorithm and extreme learning machine (ASOS-ELM) comprehensive modeling method is proposed. This method uses the ameliorated symbiotic organisms search (ASOS) algorithm to optimize the parameters of the ELM hidden layer activation function to obtain the optimal ELM model. Firstly, the initial heat rate prediction model is established with ELM, and the root mean square error (RMSE) of the output heat rate is used as the fitness value of the algorithm. Then the appropriate ELM parameters are found through the ASOS algorithm to obtain an accurate heat rate prediction model. The performance of the heat rate prediction is compared with the traditional ELM model, support vector regression (SVR) model optimized by the ASOS algorithm, ELM optimized by improved particle swarm optimization (PSO) and basic symbiotic organisms search algorithm (SOS). The results show that the ASOS-ELM model has a precise forecasting ability and rapid convergence speed when dealing with complex data models, which provides a new idea for modeling the heat rate of a steam turbine.

Key words: steam turbine, heat rate, algorithm, extreme learning machine, optimization, model

中图分类号:

TK267

牛培峰, 王枭飞, 刘楠, 王愿宁, 常玲芳, 张先臣. ASOS-ELM建模方法及在汽轮机热耗率预测中的应用[J]. 化工学报, 2018, 69(9): 3924-3931.

NIU Peifeng, WANG Xiaofei, LIU Nan, WANG Yuanning, CHANG Lingfang, ZHANG Xianchen. Modeling method of ASOS-ELM and its application in prediction of heat rate of steam turbine[J]. CIESC Journal, 2018, 69(9): 3924-3931.

参考文献 31

[1]	邵峰, 黄启龙, 戴维葆, 等. 实用机组定滑压运行曲线试验研究[J]. 动力工程学报, 2013, 33(8):643-647. SHAO F, HUANG Q L, DAI W B, et al. Experimental research on practical rated-sliding pressure operation curve of power units[J]. Journal of Chinese Society of Power Engineering, 2013, 33(8):643-647.
[2]	VOUMVOULAKIS E M, HATZIARGYRIOU N D. A particle swarm optimization method for power system dynamic security control[J]. IEEE Transactions on Power Systems, 2010, 25(2):1032-1041.
[3]	闫顺林, 兰红颖. 进汽参数变化对汽轮机热耗率影响的分析[J]. 汽轮机技术, 2013, 55(1):62-64. YAN S L, LAN H Y. The analysis of effect of steam parameters on turbine heat rate[J]. Turbine Technology, 2013, 55(1):62-64.
[4]	张文琴, 付忠广, 靳涛, 等. 基于偏最小二乘算法的热耗率回归分析[J]. 现代电力, 2009, 26(5):56-59. ZHANG W Q, FU Z G, JIN T, et al. Based on partial least squares algorithm, the heat rate regression analysis[J]. Modern Electric Power, 2009, 26(5):56-59.
[5]	王惠杰, 陈林霄, 李洋, 等. 基于υ-SVM的汽轮机热耗率回归模型研究[J]. 动力工程学报, 2014, 34(8):606-611. WANG H J, CHEN L X, LI Y, et al. Study on heat rate regression model of steam turbines based on υ-SVM[J]. Journal of Chinese Society of Power Engineering, 2014, 34(8):606-611.
[6]	ZHANG W P, NIU P F, LI G Q, et al. Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm[J]. Knowledge-Based Systems, 2013, 39(2):34-44.
[7]	李辉. 基于在线支持向量回归算法的电站热耗率模型[J]. 中国电力, 2014, 47(7):21-25. LI H. Power plant heat rate model based on online support vector regression algorithm[J]. Electric Power, 2014, 47(7):21-25.
[8]	刘超, 牛培峰, 段晓龙, 等. 基于相关向量机的汽轮机最优运行初压的确定[J]. 化工学报, 2016, 67(9):3812-3816. LIU C, NIU P F, DUAN X L, et al. Determination of optimal initial steam pressure of turbine based on relevance vector machine[J]. CIESC Journal, 2016, 67(9):3812-3816.
[9]	朱誉, 冯利法, 徐治皋. 基于BP神经网络的热经济性在线计算模型[J]. 热力发电, 2008, 37(12):17-19. ZHU Y, FENG L F, XU Z G. Online calculation of steam turbine thermal performance based on BP neural network[J]. Thermal Power Generation, 2008, 37(12):17-19.
[10]	HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine:theory and applications[J]. Neurocomputing, 2006, 70(1):489-501.
[11]	刘厦, 刘石, 任婷. 基于SA-ELM的声学层析成像温度分布重建算法[J]. 化工学报, 2017, 68(6):2434-2446. LIU S, LIU S, REN T. SA-ELM based method for reconstructing temperature distribution in acoustic tomography measurement[J]. CIESC Journal, 2017, 68(6):2434-2446.
[12]	CHENG M Y, PRAYOGO D. Symbiotic organisms search:a new metaheuristic optimization algorithm[J]. Computers & Structures, 2014, 139:98-112.
[13]	EZUGWU E S, ADEWUMI A O, FRINCU M E. Simulated annealing based symbiotic organisms search optimization algorithm for traveling salesman problem[J]. Expert Systems with Applications, 2017, 77:189-210.
[14]	SAHA S, MUKHERJEE V. Optimal placement and sizing of DGs in RDS using chaos embedded SOS algorithm[J]. Iet Generation Transmission & Distribution, 2016, 10(14):3671-3680.
[15]	GUHA D, ROY P, BANERJEE S. Quasi-oppositional symbiotic organism search algorithm applied to load frequency control[J]. Swarm & Evolutionary Computation, 2017, 33:46-67.
[16]	UROS S, ANDRE S, AMOR C, et al. Improved adaptive fuzzy backstepping control of a magnetic levitation system based on symbiotic organism search[J]. Applied Soft Computing, 2017, 56:19-33.
[17]	NANDA S J, JONWAL N. Robust nonlinear channel equalization using WNN trained by symbiotic organism search algorithm[J]. Applied Soft Computing, 2017, 57:197-209.
[18]	ZHANG B Y, SUN L J, YUAN H W, et al. An improved regularized extreme learning machine based on symbiotic organisms search[C]//Industrial Electronics and Applications. IEEE, 2016:1645-1648.
[19]	周虎, 赵辉, 周欢, 等. 自适应精英反向学习共生生物搜索算法[J]. 计算机工程与应用, 2016, 52(19):161-166. ZHOU H, ZHAO H, ZHOU H, et al. Symbiotic organisms search algorithm using adaptive elite opposition-based learning[J]. Computer Engineering and Applications, 2016, 52(19):161-166.
[20]	LIAO T W, KUO R J. Five discrete symbiotic organisms search algorithms for simultaneous optimization of feature subset and neighborhood size of KNN classification models[J]. Applied Soft Computing, 2018, 64:581-595.
[21]	EZUGWU A E, ADEWUMI A O. Soft sets based symbiotic organisms search algorithm for resource discovery in cloud computing environment[J]. Future Generation Computer Systems, 2017, 76:33-50.
[22]	RAO R V, PATEL V. A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems[J]. International Journal of Industrial Engineering Computations, 2014, 5(1):1-22.
[23]	LI X, YAO X. Cooperatively coevolving particle swarms for large scale optimization[J]. IEEE Transactions on Evolutionary Computation, 2012, 16(2):210-224.
[24]	YANG M, OMIDVAR M N, LI C, et al. Efficient resource allocation in cooperative co-evolution for large-scale global optimization[J]. IEEE Transactions on Evolutionary Computation, 2017, 21(4):493-505.
[25]	徐圆, 黄兵明, 贺彦林. 基于改进ELM的递归最小二乘时序差分强化学习算法及其应用[J]. 化工学报, 2017, 68(3):916-924. XU Y, HUANG B M, HE Y L. Recursive least-squares TD (λ) learning algorithm based on improved extreme learning machine[J].CIESC Journal, 2017, 68(3):916-924.
[26]	李荣雨, 王立明. 基于ISOMAP-ELM的软测量建模及化工应用[J]. 计量学报, 2016, 37(5):548-552. LI R Y, WANG L M. Soft measurement modeling and chemical application based on ISOMAP-ELM neural network[J]. Acta Metrologica Sinica, 2016, 37(5):548-552.
[27]	OJHA V K, ABRAHAM A, SNASEL V. Metaheuristic design of feedforward neural networks:a review of two decades of research[J]. Engineering Applications of Artificial Intelligence, 2017, 60:97-116.
[28]	OJHA V K, ABRAHAM A, SNASEL V. Simultaneous optimization of neural network weights and active nodes using metaheuristics[C]//International Conference on Hybrid Intelligent Systems. IEEE, 2015:248-253.
[29]	NIU P F, CHEN K, MA Y P, et al. Model turbine heat rate by fast learning network with tuning based on ameliorated Krill Herd algorithm[J]. Knowledge-Based Systems, 2017, 118:80-92.
[30]	LIU C, NIU P, LI G, et al. A hybrid heat rate forecasting model using optimized LSSVM based on improved GSA[J]. Neural Processing Letters, 2017, 45(1):299-318.
[31]	MA Y P, NIU P F, YAN S S, et al. A modified online sequential extreme learning machine for building circulation fluidized bed boiler's NOx emission model[J]. Applied Mathematics & Computation, 2018, 334:214-226.

ASOS-ELM建模方法及在汽轮机热耗率预测中的应用

Modeling method of ASOS-ELM and its application in prediction of heat rate of steam turbine

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献 31

相关文章 15

编辑推荐

Metrics

本文评价

[1]	杨欣, 王文, 徐凯, 马凡华. 高压氢气加注过程中温度特征仿真分析[J]. 化工学报, 2023, 74(S1): 280-286.
[2]	宋嘉豪, 王文. 斯特林发动机与高温热管耦合运行特性研究[J]. 化工学报, 2023, 74(S1): 287-294.
[3]	连梦雅, 谈莹莹, 王林, 陈枫, 曹艺飞. 地下水预热新风一体化热泵空调系统制热性能研究[J]. 化工学报, 2023, 74(S1): 311-319.
[4]	金正浩, 封立杰, 李舒宏. 氨水溶液交叉型再吸收式热泵的能量及分析[J]. 化工学报, 2023, 74(S1): 53-63.
[5]	王浩, 王振雷. 基于自适应谱方法的裂解炉烧焦模型化简策略[J]. 化工学报, 2023, 74(9): 3855-3864.
[6]	陈哲文, 魏俊杰, 张玉明. 超临界水煤气化耦合SOFC发电系统集成及其能量转化机制[J]. 化工学报, 2023, 74(9): 3888-3902.
[7]	曹跃, 余冲, 李智, 杨明磊. 工业数据驱动的加氢裂化装置多工况切换过渡状态检测[J]. 化工学报, 2023, 74(9): 3841-3854.
[8]	齐聪, 丁子, 余杰, 汤茂清, 梁林. 基于选择吸收纳米薄膜的太阳能温差发电特性研究[J]. 化工学报, 2023, 74(9): 3921-3930.
[9]	李科, 文键, 忻碧平. 耦合蒸气冷却屏的真空多层绝热结构对液氢储罐自增压过程的影响机制研究[J]. 化工学报, 2023, 74(9): 3786-3796.
[10]	何松, 刘乔迈, 谢广烁, 王斯民, 肖娟. 高浓度水煤浆管道气膜减阻两相流模拟及代理辅助优化[J]. 化工学报, 2023, 74(9): 3766-3774.
[11]	邢雷, 苗春雨, 蒋明虎, 赵立新, 李新亚. 井下微型气液旋流分离器优化设计与性能分析[J]. 化工学报, 2023, 74(8): 3394-3406.
[12]	尹刚, 李伊惠, 何飞, 曹文琦, 王民, 颜非亚, 向禹, 卢剑, 罗斌, 卢润廷. 基于KPCA和SVM的铝电解槽漏槽事故预警方法[J]. 化工学报, 2023, 74(8): 3419-3428.
[13]	陈国泽, 卫东, 郭倩, 向志平. 负载跟踪状态下的铝空气电池堆最优功率点优化方法[J]. 化工学报, 2023, 74(8): 3533-3542.
[14]	李锦潼, 邱顺, 孙文寿. 煤浆法烟气脱硫中草酸和紫外线强化煤砷浸出过程[J]. 化工学报, 2023, 74(8): 3522-3532.
[15]	刘文竹, 云和明, 王宝雪, 胡明哲, 仲崇龙. 基于场协同和耗散的微通道拓扑优化研究[J]. 化工学报, 2023, 74(8): 3329-3341.