基于互信息和PCA理论的湿法烟气脱硫工况特征提取方法

doi:10.11930/j.issn.1004-9649.201910099

中国电力 ›› 2020, Vol. 53 ›› Issue (8): 158-163.DOI: 10.11930/j.issn.1004-9649.201910099

基于互信息和PCA理论的湿法烟气脱硫工况特征提取方法

刘文慧¹, 徐遵义¹, 张旭冉¹, 张海燕²

1. 山东建筑大学计算机科学与技术学院，山东济南 250101;
2. 华电国际电力股份有限公司技术服务中心，山东济南 250014

收稿日期:2019-10-24 修回日期:2020-03-07 发布日期:2020-08-05
作者简介:刘文慧(1994-),女,硕士研究生,从事数据挖掘与火电脱硫优化研究,E-mail:sdjzulwh@163.com;徐遵义(1969-),男,通信作者,副教授,从事火电机组优化与风电机组故障预测与数据挖掘研究,E-mail:zunyixu@sdjzu.edu.cn;张旭冉(1993-),女,硕士研究生,从事数据挖掘与火电脱硫优化研究,E-mail:935756114@qq.com;张海燕(1977-),女,高级工程师,从事数据挖掘与火电脱硫优化研究,E-mail:2458957941@qq.com
基金资助:
中国华电集团有限公司2019年度科技项目(CHDKJ18-02-52)

Feature Extraction Method for Wet Flue Gas Desulfurization Under Operating Conditions Based on Mutual Information and PCA Theory

LIU Wenhui¹, XU Zunyi¹, ZHANG Xuran¹, ZHANG Haiyan²

1. School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, China;
2. Technical Service Center, Huadian Power International Co., Ltd., Jinan 250014, China

Received:2019-10-24 Revised:2020-03-07 Published:2020-08-05
Supported by:
This work is supported by 2019 Science and Technology Project of China Huadian Corporation Ltd. (No.CHDKJ18-02-52)

摘要/Abstract

摘要： 目前火电厂湿法烟气脱硫系统优化研究中主要采用主成分分析法进行特征提取，但由于湿法烟气脱硫系统能耗影响因素之间存在高耦合、非线性特征，现有特征提取方法无法评估特征间非线性关系。为此提出了一种基于互信息和主成分分析理论的特征提取方法。该方法用特征间的互信息矩阵取代主成分分析中的协方差矩阵，其特征向量表示新的主成分空间中各主成分的方向，特征值作为评价准则判断主成分维数。使用该方法对某电厂脱硫实测数据进行特征提取，实验结果表明：该方法降维效果更好，使用基于网格搜索法的支持向量机作为分类器，相同维度的主成分提出方法分类正确率更高；使用该方法进行浆液循环泵运行方式优化，耗电量平均降低约14.69%。

关键词: 湿法烟气脱硫, 特征提取, 互信息, 主成分分析, 支持向量机

Abstract: So far the principal component analysis is still the most commonly used method for feature extraction in the optimization research on wet flue gas desulfurization (WFGD) system. However, due to the nonlinearities and the high mutual coupling of the factors affecting the energy consumption of the WFGD system, there is no feasible way for the existing feature extraction methods to evaluate the defects of the non-linear relationship between the features. Therefore this paper proposes a feature extraction method based on mutual information and principal component analysis (MI-PCA), in which the covariance matrix in principal component analysis is replaced with a mutual information matrix between features. The eigenvector represents the direction of each principal component in the new principal component space, while the eigenvalue determines the principal component dimension as the evaluation criterion. Through the implementation of the proposed method the characteristics of the measured desulfurization data of a power plant is therefore extracted. From the experimental results this method has demonstrated better dimensionality reduction effect. With the aid of the support vector machine based on grid search method as the classifier, higher classification accuracy is observed assuming the same dimensions of the principal components. Finally, by using this method in the optimization of the operation mode of the slurry circulation pump, the average power consumption has been reduced by about 14.69%.

Key words: wet flue gas desulfurization, feature extraction, mutual information, principal component analysis, support vector machine

刘文慧, 徐遵义, 张旭冉, 张海燕. 基于互信息和PCA理论的湿法烟气脱硫工况特征提取方法[J]. 中国电力, 2020, 53(8): 158-163.

LIU Wenhui, XU Zunyi, ZHANG Xuran, ZHANG Haiyan. Feature Extraction Method for Wet Flue Gas Desulfurization Under Operating Conditions Based on Mutual Information and PCA Theory[J]. Electric Power, 2020, 53(8): 158-163.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201910099

https://www.electricpower.com.cn/CN/Y2020/V53/I8/158

参考文献

[1] 国家环境保护部. 火电厂大气污染物排放标准: GB13223—2011 [S]. 北京: 中国标准出版社, 2011.
[2] 崔仕文, 铁治欣, 丁成富, 等. 基于偏最小二乘支持向量机的烟气湿法脱硫效率预测模型[J]. 热力发电, 2017, 46(4): 81-87
CUI Shiwen, TIE Zhixin, DING Chengfu, et al. Prediction model for flue gas wet desulfurization efficiency based on partial least squares support vector machine[J]. Thermal Power Generation, 2017, 46(4): 81-87
[3] 李兴华, 何育东. 燃煤火电机组SO₂超低排放改造方案研究[J]. 中国电力, 2015, 48(10): 148-151
LI Xinghua, HE Yudong. Study on modification of ultra-low SO₂ emission in coal-fired power plants[J]. Electric Power, 2015, 48(10): 148-151
[4] 郭彦鹏, 潘丹萍, 杨林军. 湿法烟气脱硫中石膏雨的形成及其控制措施[J]. 中国电力, 2014, 47(3): 152-154, 159
GUO Yanpeng, PAN Danping, YANG Linjun. Formation and control of gypsum rain in wet flue gas desulfurization[J]. Electric Power, 2014, 47(3): 152-154, 159
[5] HUANG C J, TAN G B, CHEN Y Q. Application of wet flue gas desulfurization control system for 2×200 MW units [C]//International Conference on Artificial Intelligence, IEEE. Dengleng, 2011: 318-324.
[6] 刘黎伟. 石灰石-石膏湿法脱硫系统运行优化研究[J]. 电力科技与环保, 2019, 35(1): 35-36
LIU Liwei. Optimal operation research of limestone-gypsum wet flue gas desulfurization[J]. Electric Power Technology and Environmental Protection, 2019, 35(1): 35-36
[7] 段玖祥, 李小龙, 蔡培, 等. 超低排放燃煤电厂WFGD系统优化运行探讨[J]. 环境工程, 2017, 35(12): 73-76
DUAN Jiuxiang, LI Xiaolong, CAI Pei, et al. Optimization operation of WFGD systems in ultra-low emission coal-fired power plants[J]. Environmental Engineering, 2017, 35(12): 73-76
[8] 杨用龙, 苏秋凤, 张杨, 等. 双塔双循环脱硫系统优化与经济性运行研究[J]. 中国电力, 2018, 51(4): 136-142
YANG Yonglong, SU Qiufeng, ZHANG Yang, et al. System optimization and economical operation of the series absorption tower[J]. Electric Power, 2018, 51(4): 136-142
[9] 刘敏, 周然, 郑川江, 等. 火电厂脱硫系统运行优化控制模型研究[J]. 热能动力工程, 2017, 32(6): 95-99
LIU Min, ZHOU Ran, ZHENG Chuanjiang, et al. Study on operation optimization and control model for desulfurization system of thermal power plants[J]. Journal of Engineering for Thermal Energy and Power, 2017, 32(6): 95-99
[10] 许丹, 沈凯, 张亚平, 等. 基于模糊理论的湿法脱硫故障诊断和优化模型研究[J]. 环境工程, 2018, 36(2): 92-97
XU Dan, SHEN Kai, ZHANG Yaping, et al. Study of fault diagnosis and operation optimization models of WFGD based on fuzzy theory[J]. Environmental Engineering, 2018, 36(2): 92-97
[11] YANG Z K, LIU C Y, SONG X L, et al. Application of RBF neural network PID in wet flue gas desulfurization of thermal power plant [C]// International Conference on Machine Learning & Cybernetics, IEEE. Jeju, 2017: 301–306.
[12] 朱竹军. 基于专家控制的炉外湿法脱硫自动控制研究及应用[J]. 自动化与仪表, 2019, 34(1): 24-27, 32
ZHU Zhujun. Research and application of wet desulfurization automatic control based on expert control[J]. Automation & Instrumentation, 2019, 34(1): 24-27, 32
[13] JOLLIFFE I T. Principal component analysis [M]. New York: Springer-Verlag, 1986: 111–137.
[14] 张东平, 孙克勤, 潘效军, 等. 湿法烟气脱硫吸收塔仿真模型开发及应用[J]. 环境科学与技术, 2011, 34(3): 189-192
ZHANG Dongping, SUN Keqin, PAN Xiaojun, et al. A simulation model of wet scrubbers for flue gas desulfurization[J]. Environmental Science & Technology, 2011, 34(3): 189-192
[15] 田丽玲, 张丽萍. 一种数据驱动的湿法烟气脱硫系统的故障诊断方法[J]. 福州大学学报(自然科学版), 2012, 40(1): 82-86
TIAN Liling, ZHANG Liping. A data-driven fault diagnosis of wet flue gas desulfurization system[J]. Journal of Fuzhou University (Natural Science Edition), 2012, 40(1): 82-86
[16] BATTITI R. Using mutual information for selecting features in supervised neural net learning[J]. Neural Networks IEEE Transactions on, 1994, 5(4): 537-550.
[17] AMJADY N, DARAEEPOUR A. Day-ahead price forecasting of electricity markets by combination of mutual information technique and neural network [C]// Power and Energy Society General Meeting—Conversion and Delivery of Electrical Energy in the 21st Century, IEEE. Pittsburgh, PA, 2008: 1–7.
[18] KATO Y, SAITO H, EJIMA T, et al. An application of SVM: alphanumeric character recognition [C]//International 1989 Joint Conference on Neural Networks, IEEE. Washington DC, 1989: 576.
[19] 纪昌明, 周婷, 向腾飞, 等. 基于网格搜索和交叉验证的支持向量机在梯级水电系统隐随机调度中的应用[J]. 电力自动化设备, 2014, 34(3): 125-131
JI Changming, ZHOU Ting, XIANG Tengfei, et al. Application of support vector machine based on grid search and cross validation in implicit stochastic dispatch of cascaded hydropower stations[J]. Electric Power Automation Equipment, 2014, 34(3): 125-131
[20] YANG M Y, LI W Q, ZHANG H Y, et al. Parameters optimization improvement of SVM on load forecasting [C]//International Conference on Intelligent Human—Machine Systems and Cybernetics, IEEE. Hangzhou, 2016: 257–260.
[21] SUN Y Y, WANG Y M, GUO L L, et al. The comparison of optimizing SVM by GA and grid search [C]//International Conference on Electronic Measurement & Instruments, IEEE. Yangzhou, 2017: 354–360.

基于互信息和PCA理论的湿法烟气脱硫工况特征提取方法

Feature Extraction Method for Wet Flue Gas Desulfurization Under Operating Conditions Based on Mutual Information and PCA Theory

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]	陶磊, 罗萍萍, 林济铿. 基于深度学习的直流微电网虚假数据注入攻击二阶段检测方法[J]. 中国电力, 2024, 57(9): 11-19.
[2]	郑鹏, 韩鹏程, 王国栋, 娄颖. 中压配电线路断线高阻接地故障精细化诊断方法[J]. 中国电力, 2024, 57(4): 220-228.
[3]	张博智, 张茹, 焦东翔, 王龙宇, 周一凡, 周丽霞. 基于VMD-SAST的电能质量扰动分类识别方法[J]. 中国电力, 2024, 57(2): 34-40.
[4]	李丹, 梁云嫣, 缪书唯, 方泽仁, 胡越, 贺帅. 基于高斯混合聚类和改进条件变分自编码的多风电场功率日场景生成方法[J]. 中国电力, 2024, 57(12): 17-29.
[5]	胡云鹏, 都成刚, 齐军, 郑日红, 阿敏夫, 张浩, 梁永亮. 基于D-S证据理论的配电网接地故障原因综合辨识模型[J]. 中国电力, 2024, 57(10): 133-142.
[6]	徐一伦, 张彬桥, 黄婧, 谢枭, 王若昕, 沈丹青, 何丽娜, 杨凯帆. 考虑天气类型和相似日的IWPA-LSSVM光伏发电功率预测[J]. 中国电力, 2023, 56(2): 143-149.
[7]	邹念, 魏梅芳, 苏盛, 郑应俊, 周文晴. 基于水电关联信息的零电量低压用户窃电检测[J]. 中国电力, 2023, 56(12): 206-216.
[8]	韩伟, 段文岩, 杜兴伟, 姚峰, 马伟东, 刘磊. 基于数字孪生的在运安控系统故障诊断方法[J]. 中国电力, 2023, 56(11): 121-127.
[9]	姜龙, 程亮, 李金晶, 李庆. 燃煤电厂湿法脱硫浆液品质及喷淋层运行状况定量评价[J]. 中国电力, 2023, 56(11): 217-225, 235.
[10]	马海飞, 滕伟, 彭迪康, 柳亦兵, 靳涛. 基于DRS与改进Autogram的风电齿轮箱复合故障特征提取[J]. 中国电力, 2023, 56(10): 71-79.
[11]	严亚兵, 褚旭, 肖豪龙, 梁文武, 李辉, 夏振兴. 基于改进支持向量机的数字化变电站安全措施生成技术[J]. 中国电力, 2023, 56(10): 194-201.
[12]	胡京, 邓颖, 蒋兴良, 曾蕴睿. 输电线路覆冰垭口微地形的特征提取与识别方法[J]. 中国电力, 2022, 55(8): 135-142.
[13]	陈正雄, 帕孜来·马合木提, 沈玮. 基于改进支持向量回归的IGBT老化预测[J]. 中国电力, 2022, 55(7): 1-10.
[14]	陈铁, 冷昊伟, 李咸善, 陈一夫. 基于油中气体分析与类重叠特征的变压器分层故障诊断模型[J]. 中国电力, 2022, 55(7): 22-32,41.
[15]	艾欣, 胡寰宇, 任大鹏, 彭冬, 刘汇川, 薛雅玮, 张天琪. 电网发展诊断的改进模糊评价模型与评级[J]. 中国电力, 2022, 55(5): 66-75,165.

模态框（Modal）标题

基于互信息和PCA理论的湿法烟气脱硫工况特征提取方法

Feature Extraction Method for Wet Flue Gas Desulfurization Under Operating Conditions Based on Mutual Information and PCA Theory

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