基于CNGWO-LSSVM的汽轮机热耗率预测模型

doi:10.11930/j.issn.1004-9649.201803137

中国电力 ›› 2018, Vol. 51 ›› Issue (8): 148-153.DOI: 10.11930/j.issn.1004-9649.201803137

基于CNGWO-LSSVM的汽轮机热耗率预测模型

左智科¹, 陈国彬¹, 刘超², 牛培峰²

1. 重庆工商大学融智学院大数据研究所, 重庆 400033;
2. 燕山大学电气工程学院, 河北秦皇岛 066004

收稿日期:2018-03-22 修回日期:2018-05-01 出版日期:2018-08-05 发布日期:2018-11-01
作者简介:左智科(1984-),男,硕士,讲师,从事智能信息处理、计算机网络和智能算法研究,E-mail:zuozkrzgs@163.com;陈国彬(1982-),男,博士研究生,讲师,从事网络服务质量评价研究,E-mail:759327776@qq.com;刘超(1986-),男,通信作者,博士,工程师,从事复杂工业系统的智能建模及智能制造研究,E-mail:liuchao8679@126.com;牛培峰(1963-),男,教授,博士生导师,从事复杂工业系统的智能建模与过程优化控制研究,E-mail:npf882000@126.com
基金资助:
国家自然科学基金资助项目（61403331，61573306）。

Prediction Model of Steam Turbine Heat Rate Based on CNGWO-LSSVM

ZUO Zhike¹, CHEN Guobin¹, LIU Chao², NIU Peifeng²

1. Big Data Institute, Rongzhi College of Chongqing Technology and Business University, Chongqing 400033, China;
2. Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China

Received:2018-03-22 Revised:2018-05-01 Online:2018-08-05 Published:2018-11-01
Supported by:
This work is support by the National Natural Science Foundation of China (No.61403331, No.61573306).

摘要/Abstract

摘要： 为了准确计算汽轮机热耗率，提出一种改进灰狼优化算法优化最小二乘支持向量机（LSSVM）的热耗率软测量方法。首先针对灰狼算法收敛精度低的缺点提出一种混沌非线性灰狼优化算法（CNGWO），通过Kent混沌搜索策略和非线性动态递减权值策略来改善灰狼优化算法的性能。然后利用CNGWO算法预先选择LSSVM模型参数，并建立CNGWO-LSSVM的软测量模型。以某600 MW超临界汽轮机组实时运行数据仿真实验，对具有复杂非线性的热耗率变量进行预测，预测结果表明，经过CNGWO算法优化的LSSVM模型取得了较好的预测效果，为汽轮机热耗率的精确计算提供了一种有效方法。

关键词: 火电厂, 汽轮机, 热耗率, 软测量, 最小二乘支持向量机, 灰狼优化算法

Abstract: In order to accurately calculate the heat rate of steam turbines, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the soft-sensing modeling method using least squares support vector machine (LSSVM). Firstly, aiming at the issue of low convergence precision of GWO algorithm, the chaotic nonlinear grey wolf optimization algorithm (CNGWO) is proposed to improve the performance of GWO algorithm by virtue of Kent chaotic search strategy and nonlinear dynamic decline strategy. Then the model parameters of LSSVM is optimized by chaotic nonlinear grey wolf algorithm, and the soft sensor model of CNGWO-LSSVM is established. Based on the real-time operation data of a 600 MW supercritical steam turbine unit in a power plant, the soft sensing technology based on CNGWO-LSSVM is applied to predicate the heat rate of the 600 MW supercritical steam turbine unit. The prediction result indicates that the LSSVM optimized by CNGWO algorithm has achieved satisfactory prediction effect, which can provide an effective way for accurate heat rate calculation.

Key words: thermal power plant, steam turbine, heat rate, soft sensing, least squares support vector machine, grey wolf optimization algorithm

中图分类号:

TK267

左智科, 陈国彬, 刘超, 牛培峰. 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, NIU Peifeng. Prediction Model of Steam Turbine Heat Rate Based on CNGWO-LSSVM[J]. Electric Power, 2018, 51(8): 148-153.

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https://www.electricpower.com.cn/CN/Y2018/V51/I8/148

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基于CNGWO-LSSVM的汽轮机热耗率预测模型

Prediction Model of Steam Turbine Heat Rate Based on CNGWO-LSSVM

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基于CNGWO-LSSVM的汽轮机热耗率预测模型

Prediction Model of Steam Turbine Heat Rate Based on CNGWO-LSSVM

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