基于PSO-Elman神经网络的燃煤机组受热面清洁状态预测

doi:10.11930/j.issn.1004-9649.201809054

中国电力 ›› 2019, Vol. 52 ›› Issue (5): 48-53.DOI: 10.11930/j.issn.1004-9649.201809054

• 智能发电关键技术专栏 • 上一篇下一篇

基于PSO-Elman神经网络的燃煤机组受热面清洁状态预测

李强, 史元浩, 曾建潮, 陈晓龙

中北大学电气与控制工程学院, 山西太原 030051

收稿日期:2018-09-17 修回日期:2019-01-15 出版日期:2019-05-05 发布日期:2019-05-14
作者简介:李强(1993-),男,硕士研究生,从事复杂系统预测与健康管理研究,E-mail:861798449@qq.com;史元浩(1982-),男,通信作者,博士,讲师,从事复杂系统预测与健康管理、工业工程建模与优化研究,E-mail:yhshi@nuc.edu.cn
基金资助:
国家自然科学基金资助项目（61533013）；山西省重点研发计划资助项目（201703D111011）；山西省自然科学基金资助项目（201801D121159）；山西省青年自然科学基金资助项目（201801D221208）；中北大学自然科学基金资助项目（2016032，2017025）。

Forecast of Heating Surface Cleanliness of Coal-Fired Power Plants Based on PSO-Elman Neural Network

LI Qiang, SHI Yuanhao, ZENG Jianchao, CHEN Xiaolong

School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China

Received:2018-09-17 Revised:2019-01-15 Online:2019-05-05 Published:2019-05-14
Supported by:
This work is supported by National Natural Science Foundation of China (No.61533013), the Key Program of Research and Development of Shanxi Province (No.201703D111011), the Natural Science Foundation of Shanxi Province (No.201801D121159), the Youth Natural Science Foundation of Shanxi Province (No.201801D221208), and the Natural Science Foundation of North University of China (No.2016032, No.2017025).

摘要/Abstract

摘要： 随着节能减排政策力度的加大，国家十分重视火电厂节能降耗技术的开发研究。针对目前锅炉受热面吹灰方式不合理的情况，以污染率（FF）表征受热面清洁状态对锅炉受热面传热的影响，建立了基于PSO-Elman神经网络的受热面清洁状态预测模型，实现对受热面清洁状态的预测。采用粒子群算法（PSO）和Elman动态神经网络相结合的预测方法，首先根据输入、输出参数个数确定Elman神经网络结构，然后利用PSO优化网络的权值和阈值，将优化后的最优权值、阈值赋给Elman神经网络作为初始值进行网络训练，建立基于PSO-Elman神经网络的受热面清洁状态预测模型。通过具体实例仿真证实了所提方法的有效性，获得了较满意的预测精度，验证了该方法的有效性。

关键词: 智能发电, 受热面, 清洁状态, PSO-Elman, 预测

Abstract: With the efforts intensified in energy conservation and emission reduction policies, great importance has been attached to the development and research of energy-saving and consumption-reducing technologies for thermal power plants from state level. Aiming at current unreasonable way of boiler blowing on the heating surface of the boiler, the pollution rate (FF) is used to characterize and represent the impacts of the cleanliness of the heated surface on the heat transfer of the boiler heating surface. The forecasting model of the heating surface cleanliness based on PSO-Elman neural network is established. Using the particle swarm optimization (PSO) algorithm in combination with Elman dynamic neural network, the Elman neural network structure is first determined according to the number of input and output parameters. Then by taking advantage of PSO algorithm the weights and thresholds of the neural network are optimized. Finally the derived optimal values and thresholds are assigned to the Elman neural network as the initial value for network training such that the heating surface cleanliness forecast model is established based on PSO-Elman neural network. Through simulations of specific examples, satisfactory forecasting accuracy is obtained and hence the effectiveness of the proposed method is verified.

Key words: intelligent generation, heating surface, clean state, PSO-Elman, prediction

中图分类号:

TP273

李强, 史元浩, 曾建潮, 陈晓龙. 基于PSO-Elman神经网络的燃煤机组受热面清洁状态预测[J]. 中国电力, 2019, 52(5): 48-53.

LI Qiang, SHI Yuanhao, ZENG Jianchao, CHEN Xiaolong. Forecast of Heating Surface Cleanliness of Coal-Fired Power Plants Based on PSO-Elman Neural Network[J]. Electric Power, 2019, 52(5): 48-53.

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基于PSO-Elman神经网络的燃煤机组受热面清洁状态预测

Forecast of Heating Surface Cleanliness of Coal-Fired Power Plants Based on PSO-Elman Neural Network

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基于PSO-Elman神经网络的燃煤机组受热面清洁状态预测

Forecast of Heating Surface Cleanliness of Coal-Fired Power Plants Based on PSO-Elman Neural Network

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