基于非线性状态估计(NSET)方法的燃机电厂故障检测研究及应用  

Research and Application on Fault Detection of Gas Turbine Power Plant Based on NSET Method

作　　者：李娴静 李勇辉 楼鹏炯 LI Xianjing;LI Yonghui;LOU Pengjiong(China Huadian Corporation Ltd.Zhejiang Company,Hangzhou 31007,Zhejiang,China;Hangzhou Huadian Banshan Power Generation Co.,Ltd.,Hangzhou 310007,Zhejiang,China)

机构地区：[1]中国华电集团有限公司浙江公司,浙江杭州310007 [2]杭州华电半山发电有限公司,浙江杭州310007

出　　处：《电力大数据》2024年第1期28-36,共9页Power Systems and Big Data

基　　金：中国华电集团有限公司总部技改项目(JG0120190686)。

摘　　要：随着“双碳”目标的提出,燃气轮机发电机组的经济、可靠运行成为了行业关注的重点。因此,准确检测故障和提高运行水平具有重要的研究价值。本文针对燃机电厂的故障检测问题,结合小波去噪等数据预处理方法,提出了一种基于非线性状态估计(NSET)的故障检测方法。该方法利用电力大数据,首先构建记忆矩阵并建立数学模型,然后通过比较实际曲线与预测曲线,分析残差值,从而有效地检测出故障。为了验证其有效性,本文以浙江某燃机电厂为例,在仿真平台和生产现场分别进行实验。实验结果表明,该方法能够有效检测33种故障也能够检测生产现场的燃机速比阀故障、热电偶故障等常见故障。最后,本文将该方法落地到模拟数字电厂项目中,实现了数字电厂故障检测技术的工程示范应用。Nowadays,with the goals of"Carbon Peaking and Carbon Neutrality",the economic and reliable operation of gas turbine generator sets has gradually become a focal point in the industry.Therefore,it is of great value to quickly and accurately detect faults and improve operational levels.In this paper,we address the issue of fault detection in gas turbine power plants.By combining wavelet denoising with a fault detection method based on Nonlinear State Estimation Technology(NSET),we propose an approach that involves constructing a memory matrix,establishing a mathematical model,comparing actual and predicted curves,and analyzing residual values to achieve effective fault detection,with the big data in electric power.Taking a gas turbine power plant in Zhejiang Province as an example,we verify the method using a simulation platform,which successfully detect all 33 types of faults on the simulation platform.Moreover,the method has been effectively applied using real industrial data,demonstrating its ability to detect faults such as those in the gas turbine speed ratio valve and thermocouples.Finally,the method has been implemented in the E-powerplant project,serving as an engineering demonstration.

关 键 词：NSET 小波去噪 燃气轮机 故障检测 数字电厂 

分 类 号：TM62[电气工程—电力系统及自动化]