Nina F. Thornhill

Diagnosis of poor control-loop performance using higher-order statistics

Higher-order statistical (HOS) techniques were first proposed over four decades ago. This paper is concerned with higher-order statistical analysis of closed-loop data for diagnosing the causes of poor control-loop performance. The main contributions of this work are to utilize HOS tools such as cumulants, bispectrum and bicoherence to develop two new indices: the non-Gaussianity index (NGI) and the nonlinearity index (NLI) for detecting and quantifying non-Gaussianity and nonlinearity that may be present in regulated systems, and to use routine operating data to diagnose the source of nonlinearity. The new indices together with some graphical plots have been found to be useful in diagnosing the causes of poor performance of control loops. Successful applications of the proposed method are demonstrated on simulated as well as industrial data. This study clearly shows that HOS-based methods are promising for closed-loop performance monitoring.

Detection of multiple oscillations in control loops

This article addresses the detection of oscillations in measurements from chemical processes including the case when two or more oscillations of different frequency are present simultaneously. The presence of oscillations in selected frequency ranges is determined using a new method based on the regularity of the zero crossings of filtered autocovariance functions. The work is motivated by and illustrated with industrial data that exhibit multiple plant-wide oscillations. Issues of practical implementation in an automated tool are discussed.

Detection and diagnosis of oscillation in control loops

Abstract Previous work presented a real-time algorithm for the detection of oscillations in PI and PID feedback control loops. This paper examines further opportunities for oscillation detection in the off-line analysis of ensembles of data from control loops. Its use with measurements from routine operations is emphasised. The paper presents operational signatures that indicate the cause of an oscillation. Typically, a full diagnosis requires a special test for which the loop is taken out of routine operation. The signatures lead to recommendations about which special test is most appropriate. They therefore reduce the time spent in trouble-shooting by guiding the choice of test.

Finding the Direction of Disturbance Propagation in a Chemical Process Using Transfer Entropy

In continuous chemical processes, variations of process variables usually travel along propagation paths in the direction of the control path and process flow. This paper describes a data-driven method for identifying the direction of propagation of disturbances using historical process data. The novel concept is the application of transfer entropy, a method based on the conditional probability density functions that measures directionality of variation. It is sensitive to directionality even in the absence of an observable time delay. Its performance is studied in detail and default settings for the parameters in the algorithm are derived so that it can be applied in a large scale setting. Two industrial case studies demonstrate the method

Refinery-wide control loop performance assessment

This paper discusses the application of control loop performance assessment (CLPA) in a refinery setting. The CLPA algorithm has several parameters that have to be adjusted correctly to give the best results. Procedures are described for selecting these parameters which make it feasible to implement the algorithm on a refinery-wide scale. We report practical experiences with the use of the techniques.

Spectral principal component analysis of dynamic process data

Abstract This article describes principal component analysis (PCA) of the power spectra of data from chemical processes. Spectral PCA can be applied to the measurements from a whole unit or plant because spectra are invariant to the phase lags caused by time delays and process dynamics. The same comment applies to PCA using autocovariance functions, which was also studied. Two case studies are presented. One was derived from simulation of a pulp process. The second was from a refinery involving 37 tags. In both cases, PCA clusters were observed which were characterised by distinct spectral features. Spectral PCA was compared with PCA using autocovariance functions. The performance was similar, and both offered an improvement over PCA using the time domain signals even when time shifting was used to align the phases.

Diagnosis of plant-wide oscillation through data-driven analysis and process understanding

Disturbances that propagate throughout a plant due to recycle streams, heat integration or other means can have an especially large impact on product quality and running costs. There is thus a motivation for automated detection of a plant-wide disturbance and for determination of the root cause so that the disturbance may be removed. In this article, data-driven techniques are used to diagnose a plant-wide oscillation in an Eastman Chemical Company plant. A numerical non-linearity index derived from routine measurements was able to suggest the root cause. Process understanding possessed by the plant control engineers then enhanced the data-driven analysis, for instance by identifying a proxy measurement for an unmeasured flow through the valve suspected of being the root cause. In situ tests of just one valve confirmed the suspected root cause and the plant-wide oscillation disappeared after repairing the valve. The diagnosis was right first time and the maintenance effort was thus minimized. The success of the study suggests there exists a fruitful direction for future research in the automated linkage of data-driven analysis with information about the structure and connectivity of the process.

Coherency identification in power systems through principal component analysis

In this letter, a new technique to identify coherent generators in large interconnected power system using measurements of generator speed and bus angle data has been presented. This is based on the application of principal component analysis (PCA) to measurements obtained from simulation studies that represent examples of interarea events. The results of application of PCA separately to data sets of generator speeds and bus angles, respectively, are presented. The approach of PCA was able to highlight clusters of generators showing common features when compared with the conventional modal analysis technique.

Finding the source of nonlinearity in a process with plant-wide oscillation

A plant-wide oscillation in a chemical process often has an impact on product quality and running costs and there is, thus, a motivation for automated diagnosis of the source of such a disturbance. This brief describes a method of analyzing data from routine operation to locate the root cause oscillation in a dynamic system of interacting control loops and to distinguish it from propagated secondary oscillations. The novel concept is the application of a nonlinearity index that is strongest at the source. The index is large for the nonsinusoidal oscillating time trends that are typical of the output of a control loop with a limit cycle caused by nonlinearity. It is sensitive to limit cycles caused both by equipment and by process nonlinearity. The performance of the index is studied in detail and default settings for the parameters in the algorithm are derived so that it can be applied in a large scale setting such as a refinery or petrochemical plant. Issues arising from artifacts in the nonlinearity test when applied to strongly cyclic data have been addressed to provide a robust, reliable and practical method. The technique is demonstrated with three industrial case studies.

Comparison of Three Electromechanical Oscillation Damping Estimation Methods

This paper describes three data driven methods to monitor electromechanical oscillations in interconnected power system operation. The objective is to compare and contrast the performance of the methods. The accuracy of damping ratio and frequency of oscillations are the measures of the performance of the algorithms. The advantages and disadvantages of various techniques and their limitations to measurement noise have been considered while assessing performance. The target frequency and damping are computed using the Nordic power system simulation model.