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.
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