International Journal of Industrial and Manufacturing Systems Engineering
Volume 3, Issue 3, May 2018, Pages: 17-24
Received: Aug. 3, 2018;
Accepted: Sep. 7, 2018;
Published: Oct. 13, 2018
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Shizhe Li, School of Control Science and Engineering, North China Electric Power University, Baoding, China
Yinsong Wang, School of Control Science and Engineering, North China Electric Power University, Baoding, China
With the development of science and technology, the control system has become an indispensable means to ensure the safe, stable and efficient operation of the process with the improvement of system capability and modernization level. As time goes on, the characteristics of industrial production process will change, resulting in the degradation of control performance, product quality decline, directly affecting economic benefits. Therefore, performance evaluation of control system is of great significance to improve control performance and economic benefits of enterprises. Combustion control system is an important and typical multivariable control system in thermal power plant. Its performance evaluation is very important for power production process. So a method and detail steps of performance analysis based on data driven for multivariable control systems are presented. Using multivariate statistical analysis, the overall performance of the system and the performance index of the individual variable are defined respectively by the generalized eigenvalue of the covariance matrix. Through the supervisory information system, the data of the combustion control system of a certain thermal power unit is obtained and the operating data for one day is analyzed using the proposed method. The results show that this method can realize the relative accuracy evaluation of the overall performance and the individual performance for each controlled variable of the control system.
Performance Analysis of Multi-Variable Control System Based on Data Driven, International Journal of Industrial and Manufacturing Systems Engineering.
Vol. 3, No. 3,
2018, pp. 17-24.
Faulwasser T, Engelmann A, Mühlpfordt T, et al. Optimal power flow: an introduction to predictive, distributed and stochastic control challenges [J]. at - Automatisierungstechnik, 2018, 66.
Wen L, Li X, Gao L, et al. A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method [J]. IEEE Transactions on Industrial Electronics, 2018, 65 (7):5990-5998.
Lipiński M, Ziaja E. A System for Diagnostics and Automatic Control System Monitoring as a Tool to Supervise Operation and Forecast Power Units Preventive Actions [M]// Advanced Solutions in Diagnostics and Fault Tolerant Control. 2018.
Ye H, Wang Z, Wang L. Effects of PCM on power consumption and temperature control performance of a thermal control system subject to periodic ambient conditions [J]. Applied Energy, 2017, 190:213-221.
Shardt Y A W. Using MATLAB ®, for Statistical Analysis [M]// Statistics for Chemical and Process Engineers. Springer International Publishing, 2015.
Jelali M. An overview of control performance assessment technology and industrial applications [J]. Control Engineering Practice, 2006, 14 (5):441-466.
Harris T J. Assessment of control loop performance [J]. Canadian Journal of Chemical Engineering, 2010, 67 (5):856-861.
Huang B, Shah S L. Practical issues in multivariable feedback control performance assessment [J]. Journal of Process Control, 1997, 8 (5):421-430.
Watson M M, Seliman A F, Bliznyuk V N, et al. Evaluation of Shiryaev-Roberts procedure for on-line environmental radiation monitoring. [J]. Journal of Environmental Radioactivity, 2018.
Yu J, Qin S J. Statistical MIMO controller performance monitoring. Part I: Data-driven covariance benchmark [J]. Journal of Process Control, 2008, 18 (3):277-296.
Mcnabb C A, Qin S J. Projection based MIMO control performance monitoring: II––measured disturbances and setpoint changeS [J]. Journal of Process Control, 2005, 15 (1):89-102.
Yin S, Ding S X, Xie X, et al. A Review on Basic Data-Driven Approaches for Industrial Process Monitoring [J]. IEEE Transactions on Industrial Electronics, 2014, 61 (11):6418-6428.
Yin S, Li X, Gao H, et al. Data-Based Techniques Focused on Modern Industry: An Overview [J]. IEEE Transactions on Industrial Electronics, 2015, 62 (1):657-667.
Yin S, Huang Z. Performance Monitoring for Vehicle Suspension System via Fuzzy Positivistic C-Means Clustering Based on Accelerometer Measurements [J]. IEEE/ASME Transactions on Mechatronics, 2015, 20 (5):2613-2620.
Yin S, Zhu X, Kaynak O. Improved PLS Focused on Key-Performance-Indicator-Related Fault DiagnosiS [J]. IEEE Transactions on Industrial Electronics, 2015, 62 (3):1651-1658.
Arriagada G, Sanchez J, Stryhn H, et al. A multivariable assessment of the spatio-temporal distribution of pyrethroids performance on the sea lice Caligus rogercresseyi, in Chile [J]. Spatial and Spatio-temporal Epidemiology, 2018.
Wang Z, Han Y, Geng Z, et al. PID control loop performance assessment and diagnosis based on DEA-related MCDA [C]// International Symposium on Advanced Control of Industrial Processes. IEEE, 2017:535-540.
Feng J, Turksoy K, Cinar A. Performance Assessment of Model-Based Artificial Pancreas Control SystemS [M]// Prediction Methods for Blood Glucose Concentration. Springer International Publishing, 2016:997-1000.
Saha P. Performance Assessment of Control Loops: Theory and ApplicationS [C]// Springer-Verlag, 1999.
Wu M L, Dong J D, Wang Y S. Identification of Seawater Quality by Multivariate Statistical Analysis in Xisha Islands, South China Sea [M]// Water Quality. 2017.
Shang L Y, Tian X M, Cao Y P, et al. MPC Performance Monitoring and Diagnosis Based on Dissimilarity Analysis of PLS Cross-product Matrix [J]. Acta Automatica Sinica, 2017, 43 (2):271-279.