{"title":"Hybrid method for multi-rate refined oil pumping station system unsteady state estimation with bad data attacks","authors":"Lei He","doi":"10.1016/j.jprocont.2023.103145","DOIUrl":null,"url":null,"abstract":"<div><p>With the recent advancement of products pipelines<span><span> digitization, a large number of sensors have been installed in pumping stations for real-time flow parameters measurement. In these asynchronous multi-sensor systems, data missing and false data attacks are likely to occur when performing online operation monitoring of the oil pipeline system. In this paper, a hybrid state<span> estimation method is proposed to process both the missing and fault measurement, considering the dynamic operation process of the whole system. Combing frequency-domain analysis method with model-free adaptive control algorithm, the state estimation model with adaptive deviation compensation is established to characterize the nonlinear transient flow process of the pumping station. And the Kalman Filter method is adopted to overcome the interference of sensor noise. In terms of multi-rate observation data processing, this study innovatively proposes an algorithm based on the first principle and generalized </span></span>predictive control theory to improve the accuracy of traditional missing data processing methods based on statistical analysis. Moreover, non-obvious abnormal observations are identified by introducing long short-term memory network characterized by deviations between sensor measurements and multi-rate state estimation results. To verify the effectiveness of proposed method, it is adopted to the unsteady state estimation of a refined oil pumping station system under the attack of noise, nonuniform asynchronous sampling and insignificant abnormal data.</span></p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Process Control","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0959152423002330","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
With the recent advancement of products pipelines digitization, a large number of sensors have been installed in pumping stations for real-time flow parameters measurement. In these asynchronous multi-sensor systems, data missing and false data attacks are likely to occur when performing online operation monitoring of the oil pipeline system. In this paper, a hybrid state estimation method is proposed to process both the missing and fault measurement, considering the dynamic operation process of the whole system. Combing frequency-domain analysis method with model-free adaptive control algorithm, the state estimation model with adaptive deviation compensation is established to characterize the nonlinear transient flow process of the pumping station. And the Kalman Filter method is adopted to overcome the interference of sensor noise. In terms of multi-rate observation data processing, this study innovatively proposes an algorithm based on the first principle and generalized predictive control theory to improve the accuracy of traditional missing data processing methods based on statistical analysis. Moreover, non-obvious abnormal observations are identified by introducing long short-term memory network characterized by deviations between sensor measurements and multi-rate state estimation results. To verify the effectiveness of proposed method, it is adopted to the unsteady state estimation of a refined oil pumping station system under the attack of noise, nonuniform asynchronous sampling and insignificant abnormal data.
期刊介绍:
This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. In addition to the traditional chemical processing and manufacturing applications, the scope of process control problems involves a wide range of applications that includes energy processes, nano-technology, systems biology, bio-medical engineering, pharmaceutical processing technology, energy storage and conversion, smart grid, and data analytics among others.
Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at the application and the development of process control techniques.
Topics covered include:
• Control applications• Process monitoring• Plant-wide control• Process control systems• Control techniques and algorithms• Process modelling and simulation• Design methods
Advanced design methods exclude well established and widely studied traditional design techniques such as PID tuning and its many variants. Applications in fields such as control of automotive engines, machinery and robotics are not deemed suitable unless a clear motivation for the relevance to process control is provided.