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

作　　者：余兴刚 王日成 曾俊 魏鑫 邱斌斌[3] YU Xinggang;WANG Richeng;ZENG Jun;WEI Xin;QIU Binbin(Hunan Province Key Laboratory of Efficient&Clean Power Generation Technologies,(State Grid Hunan Electric Power Corporation Limited Research Institute),Changsha 410017,China;Hunan Xiangdian Test&Research Institute Co.,Ltd.,Changsha 410017,China;State Key Laboratory of Multiphase Flow in Power Engineering,Xi’an Jiaotong University,Xi’an 710049,China)

机构地区：[1]高效清洁发电技术湖南省重点实验室(国网湖南省电力有限公司电力科学研究院),湖南长沙410017 [2]湖南省湘电试验研究院有限公司,湖南长沙410017 [3]西安交通大学动力工程多相流国家重点实验室,陕西西安710049

出　　处：《热力发电》2025年第3期140-149,共10页Thermal Power Generation

基　　金：国家重点研发计划资助(2022YFB4100700);湖南省湘电试验研究院有限公司科技资助项目(XDKY-2021-08)。

摘　　要：电站辅机设备健康状态评估与故障预警对新型电力系统火电机组的安全运行具有重要意义。以某超临界660 MW火电机组送风机为研究对象,提出了一种基于多重特征参数的送风机故障模型动态记忆矩阵构建方法,该方法可在确保计算结果精度的同时有效提升模型计算速度。同时引入权重系数改进多元状态估计(multivariate state estimation technique,MSET)算法,提出了一种权重系数计算方法;采用总体相似度和参数相似度指标进行故障预警和定位,构建了基于动态记忆矩阵和加权MSET算法的送风机故障预警模型。运用该模型对送风机故障进行仿真,仿真结果表明:加权MSET算法不仅能够有效提高故障工况下异常参数的预测精度,还能降低异常参数对正常参数预测结果的影响,进而在实现送风机故障提前预警的同时准确定位出故障点参数。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 

分 类 号：TM6[电气工程—电力系统及自动化]