CPSO-FLN模型在汽轮机热耗率预测中的应用研究

doi:10.11930/j.issn.1004-9649.201903078

中国电力 ›› 2019, Vol. 52 ›› Issue (8): 179-184.DOI: 10.11930/j.issn.1004-9649.201903078

• 发电 • 上一篇

CPSO-FLN模型在汽轮机热耗率预测中的应用研究

胡坚¹, 刘超²

1. 浙江经贸职业技术学院信息技术系, 浙江杭州 310018;
2. 贵州航天电器股份有限公司, 贵州贵阳 550009

收稿日期:2019-03-24 修回日期:2019-04-15 发布日期:2019-08-14
通讯作者: 刘超(1986-),男,通信作者,博士,高级工程师,从事机器视觉与机器学习技术研究,E-mail:564663920@qq.com
作者简介:胡坚(1979-),男,硕士,副教授,从事大数据技术与应用、机器学习与智能算法研究,E-mail:tt_hujian@163.com

Application Study on CPSO-FLN Model in the Steam Turbine Heat Rate Forecast

HU Jian¹, LIU Chao²

1. Information Technology Department, Zhejiang Institute of Economics and Trade, Hangzhou 310018, China;
2. Guizhou Aerospace Electronics Co., Ltd., Guiyang 550009, China

Received:2019-03-24 Revised:2019-04-15 Published:2019-08-14

摘要/Abstract

摘要： 热耗率是汽轮机重要的热经济性指标之一。针对热耗率难以准确计算的问题，提出一种基于云粒子群算法（CPSO）优化快速学习网（FLN）的热耗率短期预测模型。在粒子群算法（PSO）中引入了云模型自适应权值策略，利用云滴的随机性和稳定倾向性特点自适应地调整粒子群算法的权值以提升PSO算法的全局优化性能。采用CPSO算法调整FLN的模型参数并建立CPSO-FLN热耗率预测模型。最后，将CPSO-FLN模型应用于某汽轮机的热耗率预测，输入参数为12个强相关性的可控变量，将热耗率预测结果与标准的FLN模型和PSO-FLN模型预测结果进行对比。结果表明，CPSO-FLN模型具有更高的预测精度和泛化能力，是一种有效的预测方法。

关键词: 汽轮机, 热耗率, 快速学习网, 粒子群算法, 云模型

Abstract: The heat rate is one of the important indexes to assess the thermal economy of thermal power units. Regarding the difficulties in accurate heat rate calculation, this paper proposes a short-term heat rate forecast model based on optimized fast learning network (FLN) by cloud particle swarm optimization (CPSO). The cloud model based adaptive weight strategy is introduced into the particle swarm algorithm such that the weight of the particle swarm algorithm can be self-adaptively adjusted to improve the global optimization performance of the PSO algorithm by taking advantage of the randomness and stability of cloud droplets. Furthermore, the CPSO algorithm is used to tune the model parameters of FLN and establish the CPSO-FLN heat rate forecast model. Finally, the CPSO-FLN model is applied to predict the heat rate of a steam turbine with 12 controllable variables of strong correlation as the input parameters. Through the comparison of the results from the proposed model with those from standard FLN model and PSO-FLN model, the CPSO-FLN model has demonstrated higher accuracy and better generalization capacity, and hence is proved to be an effective forecast method.

Key words: steam turbine, heat rate, fast learning network, particle swarm optimization algorithm, cloud model

中图分类号:

TK267

胡坚, 刘超. CPSO-FLN模型在汽轮机热耗率预测中的应用研究[J]. 中国电力, 2019, 52(8): 179-184.

HU Jian, LIU Chao. Application Study on CPSO-FLN Model in the Steam Turbine Heat Rate Forecast[J]. Electric Power, 2019, 52(8): 179-184.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201903078

https://www.electricpower.com.cn/CN/Y2019/V52/I8/179

参考文献

[1] 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 Hui. The turbine heat rate model based on accurate online support vector regression algorithm[J]. Electric Power, 2014, 47(7):21-25
[2] 万忠海, 晏涛, 鲁锦, 等. 汽轮机组变压运行热经济特性研究[J]. 中国电力, 2017, 50(12):81-86 WAN Zhonghai, YAN Tao, LU Jin, et al. Experimental study on and application of steam turbine's variable pressure thermal economic characteristics[J]. Electric Power, 2017, 50(12):81-86
[3] 王惠杰, 古月, 李鑫鑫. 基于BP和RBF神经网络的机组能耗特性研究[J]. 汽轮机技术, 2017, 59(5):371-374 WANG Huijie, GU Yue, LI Xinxin. Study of optimal initial pressure based on BP neural network and RBF neural network[J]. Turbine Technology, 2017, 59(5):371-374
[4] 文乐, 王伟锋, 薛志恒. 基于Elman神经网络的汽轮机组热力性能在线计算[J]. 热力发电, 2015, 44(11):62-67, 80 WEN Le, WANG Weifeng, XUE Zhiheng. Online calculation of steam turbine thermal performance based on Elman neural network[J]. Thermal Power Generation, 2015, 44(11):62-67, 80
[5] 左智科, 陈国彬, 刘超. 基于CNGWO-LSSVM的汽轮机热耗率预测模型[J]. 中国电力, 2018, 51(8):148-153 ZUO Zhike, CHEN Guobin, LIU Chao. Prediction model of steam turbine heat rate based on CNGWO-LSSVM[J]. Electric Power, 2018, 51(8):148-153
[6] LI Guoqiang, NIU Peifeng. Combustion optimization of a coal-fired boiler with double linear fast learning network[J]. Soft Computing, 2016, 20(1):149-156.
[7] LI Guoqiang, NIU Peifeng, DUAN Xiaolong. Fast learning network:a novel artificial neural network with a fast learning speed[J]. Neural Computing and Applications, 2014, 24(7-8):1683-1695.
[8] 曹生让, 丁晓群, 王庆燕, 等. 基于反向云自适应粒子群算法的多目标无功优化[J]. 中国电力, 2018, 51(7):21-27 CAO Shengrang, DING Xiaoqun, WANG Qingyan, et al. Multi-objective reactive power optimization based on opposition-based learning cloud model adaptive particle swarm optimization[J]. Electric Power, 2018, 51(7):21-27
[9] INÁCIO F R, MACHARET D G, CHAIMOWICZ L. PSO-based strategy for the segregation of heterogeneous robotic swarms[J]. Journal of Computational Science, 2019, 31:86-94.
[10] ZHANG Haowei, XIE Junwei, GE Jiaang, et al. An entropy-based PSO for DAR task scheduling problem[J]. Applied Soft Computing, 2018, 73:862-873.
[11] CHEGINI S N, BAGHERI A, NAJAFI F. PSOSCALF:A new hybrid PSO based on Sine Cosine Algorithm and levy flight for solving optimization problems[J]. Applied Soft Computing, 2018, 73:697-726.
[12] AL-BAHRANI L T, PATRA J C. A novel orthogonal PSO algorithm based on orthogonal diagonaization[J]. Swarm and Evolutionary Computation, 2018, 40:1-23.
[13] 庞育才, 刘松. 基于改进人工蜂群算法的MIMO雷达稀疏阵列优化[J]. 系统工程与电子技术, 2018, 40(5):1026-1030 PANG Yucai, LIU Song. Optimization of MIMO radar sparse array based on modified artificial bee colony[J]. Systems Engineering and Electronics, 2018, 40(5):1026-1030
[14] 牛培峰, 吴志良, 马云鹏, 等. 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, et al. Prediction of steam turbine heat consumption rate based on whale optimization algorithm[J]. CIESC Journal, 2017, 68(3):1049-1057
[15] NIU Peifeng, CHEN Ke, MA Yunpeng, 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.

CPSO-FLN模型在汽轮机热耗率预测中的应用研究

Application Study on CPSO-FLN Model in the Steam Turbine Heat Rate Forecast

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	黄宁泊, 高建伟, 许传博, 徐选华, 赵舒通, 缑迅杰, 姜晓静. 基于经验挖掘与混合语言的海上风电制氢加氢港口选址研究[J]. 中国电力, 2024, 57(9): 113-123.
[2]	倪筹帷, 陈杨, 张雪松, 林达, 杜凯健, 陈健. 考虑安全性风险的电热氢系统优化配置方法[J]. 中国电力, 2024, 57(9): 124-135.
[3]	张金良, 胡泽萍. 计及多重不确定性的生物质电厂库存优化模型[J]. 中国电力, 2024, 57(12): 157-168.
[4]	皮志勇, 朱益, 廖玄, 李振兴, 方豪, 吴沛. 智能变电站多信息融合建模的通信链路故障定位方法[J]. 中国电力, 2023, 56(8): 207-215.
[5]	张丽, 刘青雷, 张宏伟. 基于改进二进制粒子群算法的家庭负荷优化调度策略[J]. 中国电力, 2023, 56(5): 118-128.
[6]	于艾清, 丁丽青, 王育飞, 李豪. 基于故障邻接状态的配电网多故障抢修与优化策略[J]. 中国电力, 2023, 56(3): 64-76.
[7]	王晶, 袁懿, 邵尹池, 张晋奇, 丁然, 巩彦江. 考虑资源储备度的主动配电网多目标集群划分与电压控制方法[J]. 中国电力, 2023, 56(12): 69-79.
[8]	易亚文, 赵静朴, 柳灿, 蒋焮焮, 李方玖, 李振兴. 基于改进ED及CM-BPNN算法的保护测量回路误差状态评估方法[J]. 中国电力, 2023, 56(11): 143-150.
[9]	徐杰, 汪石农. 面向智能电网电压崩溃路径的优化粒子群算法[J]. 中国电力, 2023, 56(1): 142-149.
[10]	梁海平, 石皓岩, 王岩, 刘英培, 王鑫明. 基于提升韧性的输电网灾后应急维修策略优化[J]. 中国电力, 2022, 55(3): 142-151.
[11]	易锦桂, 朱自伟, 谢青. 基于改进场景聚类算法的海上风电储能优化配置研究[J]. 中国电力, 2022, 55(12): 2-10.
[12]	张书盈, 励刚, 滕晓毕, 程基峰, 严正, 王晗. 计划与市场并存的日发电计划模型构建与分析[J]. 中国电力, 2022, 55(1): 151-158.
[13]	姚永峰, 王启哲, 王慧萍, 贾佳, 夏西宾. 基于邻接矩阵的低压配电网故障区段定位方法[J]. 中国电力, 2021, 54(11): 91-96,114.
[14]	彭院院, 周任军, 曾子琪, 冯剑, 程远林, 方绍凤. 参与气电市场的虚拟电厂内部优化随机模型[J]. 中国电力, 2020, 53(9): 181-188.
[15]	何国安, 胡相余, 杨铖, 安仕江, 张学延. 轴承振动对油膜涡动的影响分析与试验[J]. 中国电力, 2020, 53(7): 211-216.

模态框（Modal）标题

CPSO-FLN模型在汽轮机热耗率预测中的应用研究

Application Study on CPSO-FLN Model in the Steam Turbine Heat Rate Forecast

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics