汽水循环系统泄漏故障分层次智能定位及诊断  

Leakage Fault Intelligent Positioning and Diagnosis for the Water and Steam Circulation System

作　　者：马良玉[1] 帅禹 王晓霞[1] 刘婷 

机构地区：[1]华北电力大学控制与计算机工程学院,河北保定071003 [2]国华定州发电厂,河北定州073000

出　　处：《仪器仪表用户》2017年第11期97-104,共8页Instrumentation

基　　金：国家自然科学基金项目(61174111);河北省自然科学基金项目(F2016502093);中央高校基本科研业务费专项资金资助项目(2016MS119)

摘　　要：提出一种基于二级神经网络的超临界机组汽水循环系统泄漏故障分层次定位及诊断方法。其中,第一级神经网络通过构建与泄漏密切相关的比参数作为神经网络输入,对故障发生的子系统进行初步定位;第二级神经网络则采用BP网络对汽水循环系统各子系统内("四管"、高加、低加)故障的具体类型进行诊断,并结合故障征兆缩放方法来完成不同负荷工况、不同严重程度故障的准确判别。以某超临界600MW机组为对象,借助电站全工况仿真机对汽水循环泄漏故障诊断进行详细的仿真实验,结果表明:该方法可以较好地实现不同负荷工况、不同严重程度的汽水循环系统的泄漏故障定位及诊断。A novel approach based on two-stage neural networks is proposed for leakage fault positioning and diagnosis in the water-steam circulation system of a supercritical power plant. By constructing leakage-related ratio-parameters as the model inputs, a primary neural network model is employed to locate the subsystem fault occurred. Then, secondary BP neural network models are applied to identify the specific fault types in each subsystem. Symptom optimization technique is also employed to improve the fault diagnostic effect for faults of varying severity under different load conditions. By taking the water-steam circulation system of a 600 MW SC power unit as object studied, detailed fault diagnosis tests are carried out on the full-scope simulator of the given unit. The results show that the proposed method can achieve good diagnostic results for varying-degree leakage faults in the water-steam circulation system under different load conditions.

关 键 词：超临界机组 汽水循环系统 泄漏 神经网络 故障定位 故障诊断 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置] TM611.1[自动化与计算机技术—控制科学与工程]