Research on Condenser Deterioration Evolution Trend Based on ANP-EWM Fusion Health Degree  

作　　者：Hong Qian Haixin Wang Guangji Wang Qingyun Yan 

机构地区：[1]College of Automation Engineering,Shanghai University of Electric Power,Shanghai,200090,China [2]Shanghai Key Laboratory of Power Station Automation Technology,Shanghai,200072,China [3]Empower Information Technology(Shanghai)Co.,Ltd.,Shanghai,201508,China [4]China Nuclear Power Engineering Design Co.,Ltd.,Shenzhen,518052,China

出　　处：《Computer Modeling in Engineering & Sciences》2024年第4期679-698,共20页工程与科学中的计算机建模（英文）

基　　金：supported by the National Natural Science Foundation of China (51906133).

摘　　要：This study presents a proposed method for assessing the condition and predicting the future status of condensers operating in seawater over an extended period.The aim is to address the problems of scaling and corrosion,which lead to increased loss of cold resources.The method involves utilising a set of multivariate feature parameters associated with the condenser as input for evaluation and trend prediction.This methodology offers a precise means of determining the optimal timing for condenser cleaning,with the ultimate goal of improving its overall performance.The proposed approach involves the integration of the analytic network process with subjective expert experience and the entropy weightmethod with objective big data analysis to develop a fusion health degreemodel.The mathematical model is constructed quantitatively using the improved Mahalanobis distance.Furthermore,a comprehensive prediction model is developed by integrating the improved Informer model and Markov error correction.This model takes into account the health status of the equipment and several influencing factors,includingmultivariate feature characteristics.This model facilitates the objective examination and prediction of the progression of equipment deterioration trends.The present study involves the computation and verification of the field time series data,which serves to demonstrate the accuracy of the condenser health-related models proposed in this research.These models effectively depict the real condition and temporal variations of the equipment,thus offering a valuable method for determining the precise cleaning time required for the condenser.

关 键 词：CONDENSER health degree improved Mahalanobis distance GC-Informer model Markov error correction 

分 类 号：TQ051.61[化学工程]