广东电力2026，Vol.39Issue(6)：40-49,10.DOI:10.3969/j.issn.1007-290X.2026.06.004
火电机组冷端系统真空预测方法与故障诊断
Vacuum Prediction Method and Fault Diagnosis of Cold-end System in Thermal Power Units
摘要
Abstract
The operational status of the cold-end system in thermal power units is closely related to the safety and economic efficiency of the entire unit.However,due to frequent occurrences of data collection and transmission anomalies,such as bad points and breakpoints,operational personnel often face challenges in objectively assessing the operational status of the units.The diagnosis of operational status and performance evaluation of thermal power units,including the cold-end system,remains a significant technical challenge in the industry.This study focuses on the cold-end system and determines the interval of the vacuum reference value using the K means clustering algorithm.Based on the Spearman correlation coefficient,several parameter variables with strong correlations to the condenser vacuum are identified.Subsequently,two models are established including a long short-term memory(LSTM)neural network and a high-order polynomial fitting model based on the least squares method.Results indicate that the prediction accuracy of the high-order polynomial model based on the least squares method reaches 98.96％,outperforming the LSTM model(98.09％).Finally,leveraging the developed local mathematical model,a predictive framework for estimating cold-end system parameters is constructed.Through the integration of data correction,performance evaluation and fault diagnosis,real-time early warnings and system optimizations for vacuum anomalies are achieved.Notably,a polynomial-form parameter relationship model is constructed using historical operational data from the unit.This model exhibits high computational efficiency,excellent prediction accuracy and strong engineering applicability,offering an effective solution for condition monitoring,operational optimization and intelligent diagnosis of the cold-end system in thermal power units.关键词
冷端系统优化/最佳真空/K均值聚类算法/LSTM神经网络/最小二乘法/故障诊断Key words
cold-end system optimization/optimal vacuum/K means clustering algorithm/LSTM neural network/least square method/fault diagnosis分类
信息技术与安全科学引用本文复制引用
曾丽君,李育昆,李建强,刘若飞,杜康康..火电机组冷端系统真空预测方法与故障诊断[J].广东电力,2026,39(6):40-49,10.基金项目
国家自然科学基金项目(52406089) （52406089）
