摘要
高压加热器(高加)是汽轮机回热系统重要的组成部件,由于长时间工作在高温高压环境下,容易发生管束泄漏等故障,严重时会影响机组的安全运行。本文针对卧式高加系统,采用分段数值热力计算方法建立管束泄漏故障仿真模型,得到与故障相关的数据集,运用改进的BP神经网络完成故障诊断模型的构建。采用某600 MW机组3号高加管束泄漏故障对模型进行验证,并与多种模型对比分析表明,本文改进BP神经网络模型具有较高的准确度和可靠性。
High-pressure heater is an important component in turbine regenerative system. Long-term operation at high temperatures and pressures can easily cause the pipe leakage, which affects the safe running of units when the leakage turns serious. In this paper, a horizontal high-pressure heater of a large thermal power unit was taken as the research object, a fault simulation model of high heater leakage was built up on the basis of a discrete numerical thermal calculation method. The data set related to the faults was obtained. Moreover, a fault diagnosis model was constructed through the improved BP neural network. Furthermore, the model was used to solve the leakage fault of No.3 high-pressure heater in a 600 MW steam turbine system. Compared with other diagnostic models, the results show that this improved BP neural network model has high accuracy and reliability.
作者
秦刚华
雷丽君
郭鼎
司风琪
QIN Ganghua;LEI Lijun;GUO Ding;SI Fengqi(Zhejiang Energy Group Research Institute Co., Ltd., Hangzhou 311121, China;School of Energy and Environment, Southeast University, Nanjing 210096, China)
出处
《热力发电》
CAS
北大核心
2019年第6期108-114,共7页
Thermal Power Generation
基金
浙江省重点研发计划(2017C01082)~~
关键词
高压加热器
故障诊断
热力计算
管道泄漏
BP神经网络
仿真模型
high pressure heater
fault diagnosis
thermal calculation
pipe leakage
BP neural network
simulation model
