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Journal ArticleDOI

Multivariate Control Loop Performance Assessment With Hurst Exponent and Mahalanobis Distance

TL;DR: A novel data-driven technique for performance assessment of multivariate control loops that takes into account the interactions within the system is proposed, establishing the proposed approach as a promising tool for interactor-matrix-independent MIMO control loop performance assessment.
Abstract: A novel data-driven technique for performance assessment of multivariate control loops that takes into account the interactions within the system is proposed. The technique merges the Hurst-exponent-based single-input single-output controller performance index with Mahalanobis distance to devise a multiple-input multiple-output (MIMO) controller performance index. The distinct advantage over the standard minimum variance index and novelty of the proposed approach lies in its ability to quantify the performance of MIMO controller without the knowledge of interactor matrix or system description, which leads to the technique being insensitive to model plant mismatch and easily applicable to nonlinear systems. Only closed-loop routine operating data are required. This new methodology is tested on benchmark systems from the literature and simulation results are presented. Comparison with minimum variance index-based techniques reveals excellent agreement in the trends of both approaches. The results establish the proposed approach as a promising tool for interactor-matrix-independent MIMO control loop performance assessment.
Citations
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01 Jan 2016
TL;DR: The introduction to stochastic control theory is universally compatible with any devices to read, and will help you to get the most less latency time to download any of the authors' books like this one.
Abstract: Thank you for downloading introduction to stochastic control theory. Maybe you have knowledge that, people have look hundreds times for their chosen novels like this introduction to stochastic control theory, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some infectious bugs inside their laptop. introduction to stochastic control theory is available in our digital library an online access to it is set as public so you can get it instantly. Our book servers saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the introduction to stochastic control theory is universally compatible with any devices to read.

312 citations

Journal ArticleDOI
01 Jan 2021
TL;DR: In this article, an alternative straightforward technical route, embedded in the cyber-physical-social system framework, is proposed to evaluate the impact of the detected fault on the plant-wide performance.
Abstract: The intensive research and development efforts directed towards large-scale complex industrial systems in the context of Industry 4.0 indicate that safety and reliability issues pose significant challenges. During online operation, system performance degradation will lead, not only to economic losses, but also potential safety hazards. In the existing research and technical routes, the target of the fault diagnosis systems is to trigger alarms to report the fact of the existence of malfunctions as well as the underlying reasons accurately. However, it remains unanswered how urgent it is to fix it, and what degrees of fault-tolerance, maintenance, and fault recovery are needed. Further analyses are necessary to evaluate the impact of the detected fault on the plant-wide performance. In this article, to enable a more comprehensive and precise description of the plant-wide operational status, the roles of the commonly used performance metrics, the state-of-the-art performance evaluation approaches, as well as the performance-oriented and plant-wide process monitoring techniques are investigated. On this basis, an alternative straightforward technical route, embedded in the cyber-physical-social system framework is proposed. A roadmap including the key research questions, the future research directions, and an outlook about the future vision is presented.

76 citations

Journal ArticleDOI
TL;DR: An optimal battery sizing methodology is proposed to improve renewable generation predictability using “Seasonal-Trend decomposition based on LOESS”11locally weighted regression, and improving self-similarity index in the PV production time series and economic viability of the proposed methodology in a particular application.
Abstract: The number of large-scale photovoltaic (PV) and wind farms is rapidly growing in Australia and all around the world. When these resources participate in the wholesale electricity market, their uncertain nature of generation results in revenue loss due to the penalty incurred by deviating from day-ahead and real-time commitments. In an attempt to avoid financial losses, they typically bid in the market conservatively. This, in turn, might lead to wasting clean energy and lowering overall profit for the producers. To address these issues, various energy storage devices are considered as a potential solution by academic and industrial researchers alike. In this study, an optimal battery sizing methodology is proposed to improve renewable generation predictability using “Seasonal-Trend decomposition based on LOESS” 1 1 locally weighted regression. decomposition technique, self-similarity estimation, and enhancing it through filtering. The ultimate goal is to determine the optimal battery size that enhances predictability of renewable generation regardless of the prediction technique and time horizon, which necessarily improves the accuracy of predicted values. The goal is achieved by the proposed method through designing a forecasting-technique-agnostic algorithm. For optimal battery sizing, an optimization formulation is proposed including battery degradation through its useful lifetime. Moreover, prediction studies are carried out to prove predictability enhancement using four prediction techniques and three prediction horizons. The simulation results show the effectiveness of the proposed method in improving self-similarity index (i.e., Hurst exponent) in the PV production time series and economic viability of the proposed methodology in a particular application.

32 citations


Cites methods from "Multivariate Control Loop Performan..."

  • ...the stationary time series using Hurst exponent [20], which is extensively used in other research areas such as stock markets [21] and biomedical engineering [22] for predictability mea-...

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Journal ArticleDOI
TL;DR: In this article, a novel approach to the task of control performance assessment is presented, which does not require any a priori knowledge on process model and uses control error time series data using nonlinear dynamical fractal persistence measures.
Abstract: This paper presents novel approach to the task of control performance assessment. Proposed approach does not require any a priori knowledge on process model and uses control error time series data using nonlinear dynamical fractal persistence measures. Notion of the rescaled range R/S plots with estimation of Hurst exponent is applied. Crossover phenomenon is observed in data being investigated and discussed. Paper starts with industrial engineering rationale. Review of the control error histogram is followed by statistical analysis of probabilistic distribution functions (PDFs). Levy $$\alpha $$ -stable PDF parameters seem to be best fitted. They directly lead to the fractal analysis using Hurst exponents and R/S plot crossover points. The evaluation aims at performance of the generalized predictive control (GPC) and discusses freshly introduced loop performance quality sensitivity against design parameters of the GPC controller.

22 citations


Cites background from "Multivariate Control Loop Performan..."

  • ...In the recent works authors [11] perform diagnosis of MIMO control loops...

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Journal ArticleDOI
TL;DR: In this article, a new methodology based on a process model evaluation index is proposed for detecting process model mismatch in closed-loop control systems, which is the ratio between the variance of the disturbance innovation and that of the model quality variable.

18 citations

References
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Book
01 Jan 1970
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Abstract: From the Publisher: This is a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It explores the building of stochastic (statistical) models for time series and their use in important areas of application —forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control. Features sections on: recently developed methods for model specification, such as canonical correlation analysis and the use of model selection criteria; results on testing for unit root nonstationarity in ARIMA processes; the state space representation of ARMA models and its use for likelihood estimation and forecasting; score test for model checking; and deterministic components and structural components in time series models and their estimation based on regression-time series model methods.

19,748 citations

Journal ArticleDOI
TL;DR: Time series analysis san francisco state university, 6 4 introduction to time series analysis, box and jenkins time seriesAnalysis forecasting and, th15 weeks citation classic eugene garfield, proc arima references 9 3 sas support, time series Analysis forecasting and control pambudi, timeseries analysis forecasting and Control george e.
Abstract: time series analysis san francisco state university, 6 4 introduction to time series analysis, box and jenkins time series analysis forecasting and, th15 weeks citation classic eugene garfield, proc arima references 9 3 sas support, time series analysis forecasting and control pambudi, time series analysis forecasting and control george e, time series analysis forecasting and control ebook, time series analysis forecasting and control 5th edition, time series analysis forecasting and control fourth, time series analysis forecasting and control amazon, wiley time series analysis forecasting and control 5th, time series analysis forecasting and control edition 5, time series analysis forecasting and control 5th edition, time series analysis forecasting and control abebooks, time series analysis for business forecasting, time series analysis forecasting and control wiley, time series analysis forecasting and control book 1976, time series analysis forecasting and control researchgate, time series analysis forecasting and control edition 4, time series analysis forecasting amp control forecasting, george box publications department of statistics, time series analysis forecasting and control london, time series analysis forecasting and control an, time series analysis forecasting and control amazon it, box g e p and jenkins g m 1976 time series, time series analysis forecasting and control pdf slideshare, time series analysis forecasting and control researchgate, time series analysis forecasting and control 5th edition, time series analysis forecasting and control 5th edition, time series wikipedia, time series analysis forecasting and control abebooks, time series analysis forecasting and control, forecasting and time series analysis using the sca system, time series analysis forecasting and control by george e, time series analysis forecasting and control 5th edition, time series analysis forecasting and control 5th edition, box and jenkins time series analysis forecasting and control, time series analysis forecasting and control ebook, time series analysis forecasting and control, time series analysis and forecasting cengage, 6 7 references itl nist gov, time series analysis forecasting and control george e, time series analysis and forecasting statgraphics, time series analysis forecasting and control fourth edition, time series analysis forecasting and control, time series analysis forecasting and control wiley, time series analysis forecasting and control in

10,118 citations


Additional excerpts

  • ...The framework of minimum variance control (MVC), which was developed in [3] and [4], has been widely adopted for developing a theoretical benchmark for assessing control loop performance....

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Journal ArticleDOI
TL;DR: In this paper, a solution of the problem of determining the reservoir storage required on a given stream, to guarantee a given draft, is presented, where a long-time record of annual total...
Abstract: A solution of the problem of determining the reservoir storage required on a given stream, to guarantee a given draft, is presented in this paper. For example, if a long-time record of annual total...

5,087 citations


"Multivariate Control Loop Performan..." refers methods in this paper

  • ...has been used in several applications including river data analysis [31], biological signal processing [32], and financial...

    [...]

Journal ArticleDOI
01 Jan 1995-Chaos
TL;DR: A new method--detrended fluctuation analysis (DFA)--for quantifying this correlation property in non-stationary physiological time series is described and application of this technique shows evidence for a crossover phenomenon associated with a change in short and long-range scaling exponents.
Abstract: The healthy heartbeat is traditionally thought to be regulated according to the classical principle of homeostasis whereby physiologic systems operate to reduce variability and achieve an equilibrium-like state [Physiol. Rev. 9, 399-431 (1929)]. However, recent studies [Phys. Rev. Lett. 70, 1343-1346 (1993); Fractals in Biology and Medicine (Birkhauser-Verlag, Basel, 1994), pp. 55-65] reveal that under normal conditions, beat-to-beat fluctuations in heart rate display the kind of long-range correlations typically exhibited by dynamical systems far from equilibrium [Phys. Rev. Lett. 59, 381-384 (1987)]. In contrast, heart rate time series from patients with severe congestive heart failure show a breakdown of this long-range correlation behavior. We describe a new method--detrended fluctuation analysis (DFA)--for quantifying this correlation property in non-stationary physiological time series. Application of this technique shows evidence for a crossover phenomenon associated with a change in short and long-range scaling exponents. This method may be of use in distinguishing healthy from pathologic data sets based on differences in these scaling properties.

3,411 citations


"Multivariate Control Loop Performan..." refers methods in this paper

  • ...has been used in several applications including river data analysis [31], biological signal processing [32], and financial...

    [...]

Book
01 Jan 1970

2,565 citations