journal article·2023-05-05Open access

Effects of Leading Signals on Metrics of Control Quality Indicators

Richard Kephart, Sebastian Plamowski, Paweł D. Domański

Applied Sciences· Volume 13· Issue 9

Abstract

The paper demonstrates the influence of leading variables on the quality evaluation of the operation of univariate control loops of a multi-loop control system used in a 1000 MW power unit. We analyse the course of the setpoint and process values, the calculated value of the control signal and the disturbances values and leading variables. The variables that determine the operating point of the object are used as leading variables. In the case of a power unit, the main leading variable is the power setpoint at which the unit operates. The power setpoint determines the flow of fuel, air and steam—the values of these variables were also considered in the control qualityassessment. Methods based on the characteristics of control signals and error values (residual) are used in the loop control quality assessment. Basic metrics, such as mean error, mean absolute error and mean squared error, are used to analyse the control error. In addition, statistical measures in the form of higher degree moments are applied using the difference in their estimations. The study indicates that once the leading signal values are taken into account during the assessment, the risk of incorrectly interpreting decreased values for the considered indicators is minimized. It is shown that it is important to consider the values of leading variables because they determine the operating point. Particular attention is paid to changes in the leading values since they affect the process conditions. This paper discusses mechanisms that could allow control quality to be monitored both under stable operating conditions and during operating point changes. It is shown that even relatively simple metrics can give very good results when leading signals are considered.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Fault Detection and Control Systems · Advanced Data Processing Techniques · Control Systems and Identification

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Emerson Process Management, 200 Beta Dr, Pittsburgh, PA 15238, USA

Institute of Control and Computation Engineering, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland

References (23)

Show source-provided references
1. Optimal disturbance rejection for PI controller with constraints on relative delay margin10.1016/j.isatra.2016.03.014 ↗
2. A Survey on Industry Impact and Challenges Thereof [Technical Activities]10.1109/MCS.2016.2621438 ↗
3. Jelali, M. (2013). Control Performance Management in Industrial Automation: Assessment, Diagnosis and Improvement of Control Loop Performance, Springer.10.1007/978-1-4471-4546-2 ↗
4. The current state of control loop performance monitoring—A survey of application in industry10.1016/j.jprocont.2015.11.002 ↗
5. Computer control of a paper machine—An application of linear stochastic control theory10.1147/rd.114.0389 ↗
6. Domański, P.D. (2020). Control Performance Assessment: Theoretical Analyses and Industrial Practice, Springer International Publishing.10.1007/978-3-030-23593-2 ↗
7. Data-based automated diagnosis and iterative retuning of proportional-integral (PI) controllers10.1016/j.conengprac.2014.03.005 ↗
8. Performance analysis of real-time PSO tuned PI controller for regulating voltage and frequency in an AC microgrid
9. Domański, P.D. (2020). Performance Assessment of Predictive Control—A Survey. Algorithms, 13.10.3390/a13040097 ↗
10. Performance evaluation of two degree of freedom conventional controller adopting the smith principle for first order process with dead time
11. Method for Proportional-Integral Controller Tuning Assessment10.1021/ie0508482 ↗
12. Controller performance benchmarking and tuning using generalised minimum variance control10.1016/S0005-1098(02)00141-3 ↗
13. Schlegel, M., Skarda, R., and Cech, M. (2013, January 18–21). Running discrete Fourier transform and its applications in control loop performance assessment. Proceedings of the 2013 International Conference on Process Control (PC), Strbske Pleso, Slovakia.10.1109/PC.2013.6581393 ↗
14. A neural network approach to control performance assessment10.1108/17563780810919159 ↗
15. Lacarbonara, W., Balachandran, B., Ma, J., Tenreiro Machado, J.A., and Stepan, G. (2020). Nonlinear Dynamics and Control, Springer International Publishing.10.1007/978-3-030-34747-5 ↗
16. Li, Y., and O’Neill, Z. (2015, January 7–9). Evaluating control performance on building HVAC controllers. Proceedings of the International Building Performance Simulation Association, Hyderabad, India.10.26868/25222708.2015.2697 ↗
17. Minimum entropy-based performance assessment of feedback control loops subjected to non-Gaussian disturbances10.1016/j.jprocont.2014.08.003 ↗
18. Zhou, J., Jia, Y., Jiang, H., and Fan, S. (2018). Non-Gaussian Systems Control Performance Assessment Based on Rational Entropy. Entropy, 20.10.3390/e20050331 ↗
19. Multifractal properties of process control variables10.1142/S0218127417500948 ↗
20. Control performance assessment based on sensor fusion techniques10.1016/j.conengprac.2016.01.008 ↗
21. Knierim-Dietz, N., Hanel, L., and Lehner, J. (2012). Definition and Verification of the Control Loop Performance for Different Power Plant Types, Institute of Combustion and Power Plant Technology, University of Stutgart. Technical Report.
22. Optimal Use of Hybrid Renewable Energy System for Educational Building10.1615/InterJEnerCleanEnv.2022038566 ↗
23. Performance evaluation of vapor compression refrigeration system by varying air flow rates in air-cooled and evaporative cooled condensers10.1615/InterJEnerCleanEnv.2020029330 ↗