热力发电2025，Vol.54Issue(3)：140-149,10.DOI:10.19666/j.rlfd.202405129
基于动态记忆矩阵和加权多元状态估计的电站辅机故障预警和定位方法
Fault early warning and recognition of power plant auxiliary equipment based on dynamic memory matrix and weighted MSET
摘要
Abstract
It is of great significance to carry out health condition assessment and fault early warning of auxiliary equipment for safe operation of thermal power units in new power system.By taking the forced draft fan of a supercritical 660 MW thermal power unit as the research object,a method to construct dynamic memory matrix based on multiple characteristic parameters is proposed.The application shows that the proposed method can improve calculating speed of model effectively while ensuring the accuracy of calculated results.This work also presents a calculation method of weighted coefficients to modify the multivariate state estimation technique(MSET).The global similarity and parameter similarity indexes are introduced for fault early warning and recognition.An early fault warning model based on dynamic matrix and weighted MSET is utilized to simulate faults of forced draft fan.The results indicate that the weighted MSET model can not only improve the prediction accuracy of abnormal parameters under fault conditions effectively,but also reduce the influence of abnormal parameters on the predicted results of normal parameters.Consequently,the model proposed can realize both early warning of forced draft fan faults and recognition of abnormal parameters.关键词
故障预警和定位/动态记忆矩阵/特征参数/多元状态估计/权重系数Key words
fault early warning and recognition/dynamic memory matrix/characteristic parameters/multivariate state estimation/weighted coefficients引用本文复制引用
余兴刚,王日成,曾俊,魏鑫,邱斌斌..基于动态记忆矩阵和加权多元状态估计的电站辅机故障预警和定位方法[J].热力发电,2025,54(3):140-149,10.基金项目
国家重点研发计划资助(2022YFB4100700) （2022YFB4100700）
湖南省湘电试验研究院有限公司科技资助项目(XDKY-2021-08) National Key Research and Development Program(2022YFB4100700) （XDKY-2021-08）
Science and Technology Project of Hunan Xiangdian Test&Research Institute Co.,Ltd.(XDKY-2021-08) （XDKY-2021-08）
