Oscillations In Control Loops Research Articles

COMPARISON OF METHODS FOR OSCILLATION DETECTION – CASE STUDY ON A COAL-FIRED POWER PLANT

This paper compares a selection of methods for detecting oscillations in control loops. The methods are tested on measurement data from a coal-fired power plant, where some oscillations are occurring. Emphasis is put on being able to detect oscillations without having a system model and without using process knowledge. The tested methods show potential for detecting the oscillations, however, transient components in the signals cause false detections as well, motivating usage of models in order to remove the expected signals behavior.

Industrial implementation of on-line performance monitoring tools

This paper treats implementation of on-line procedures for automatic performance monitoring. Two on-line detection procedures, one that detects oscillations in control loops and one that detects sluggish control loops, are first summarized. The implementation aspects of these procedures are then discussed and experiences of their use in various industrial products are demonstrated.

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.

INDUSTRIAL APPLICATIONS OF AUTOMATIC PERFORMANCE MONITORING TOOLS

This paper describes two procedures for automatic monitoring of control loop performance. The first is a procedure for detecting oscillations in control loops, and the second a procedure for detecting sluggish control loops. The focus of the paper is the industrial application of the procedures, and the various implementations possible and available.

A qualitative shape analysis formalism for monitoring control loop performance

Optimal performance of controllers and control loops is crucial for process economy, quality and safety in chemical plants. Industrial statistics show that often a significant percentage of them are performing sub-optimally at any given time. Effective real-time monitoring of control loops is a difficult task as there may be dozens of loops to monitor in a typical process. In addition, abnormal or suboptimal performance is often not apparent under cursory inspection. Hence, automated approaches for the real-time monitoring of control loop performance is of considerable interest. In this paper, we propose an automated qualitative shape analysis (QSA) formalism for detecting and diagnosing different kinds of oscillations in control loops. We extend our earlier QSA methodology to make it more robust by developing an algorithm for automatic identification of the appropriate global time-scales. We demonstrate this formalism on three case studies to detect and diagnose control loop oscillations.

A simple method for detection of stiction in control valves

A simple method diagnosing oscillations in process control loops is presented. The new method is based on the cross-correlation between control input and process output and it is shown to correctly distinguish between two important reasons for oscillations in control loops in the process industry, namely external oscillating disturbances and static friction (stiction) in control valves.

Detection and diagnosis of oscillation in control loops

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.