Application control model in state spaces from the total loss percentage obtained from the carbon silicon steel sheet
Keywords:
silicon steel sheet, model of state control spaces, total losses, percentage of carbon
Abstract
This research presents the control models in state space representing a mathematical model described by a set of inputs, outputs and state variables related by first-order differential equations from the total percentage obtained carbon losses veneer silicon steel cores which distribution transformers are manufactured. The proposed model allows evaluating current signals in the primary, secondary and magnetizing side from total losses, adjusting the coupling coefficient of the steel sheet. Its application will bring benefits to companies that manufacture or repair this equipment, as they can design the silicon steel sheet under a fault condition versus nominal values, you can also submit the proposed model of canonical form or nature: controllable and not observable and stable (COE).Downloads
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References
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[2] R. Zhang, A. Zue, S. Wang, Z. Ren, “An improved model predictive control approach based on extended non-minimal state space formulation”, Journal of Process Control, vol. 21, number 8. Information and Control Institute, Hangzhou Dianzi University, Hangzhou 310018, China, National Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China, Department of Automation, Donghua University, Shanghai 200051, China, 2011, pp. 1183-1192.
[3] R. Zhang, A. Xue, F. Gao “Temperature control of industrial coke furnace using novel state space model predictive control”, IEEE Transactions on Industrial Informatics, vol. 10, number 4. Control Institute, Hangzhou Dianzi University, Hangzhou, China, Chemical and Biomolecular Engineering Department, Hong Kong University of Science and Technology, Kowloon, Hong Kong, Information and Control Institute, Hangzhou Dianzi University, Hangzhou, China, 2014, 6881729, pp 2084-2092.
[4] R. Zhang, F. Gao, “State space model predictive control using partial decoupling and output weighting for improved model/plant mismatch performance,” Industrial and Engineering Chemistry Research, vol. 52, number 2. Information and Control Institute, Hangzhou Dianzi University, Hangzhou 310018, China, Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, National Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China, 2013, pp. 817-829.
[5] J. Tao, Y. Zhu, Q. Fan, “Improved state space model predictive control design for linear systems with partial actuator failure”, Industrial and Engineering Chemistry Research, vol. 53, number 9. Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China, 2014, pp. 3578-3586.
[6] P. Tatjweski, “Disturbance modeling and state estimation for offset-free predictive control with state-space process models”, International Journal of Applied Mathematics and Computer Science, vol. 24, number 2. Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, Warsaw, Poland, 2014, pp. 313-323.
[7] P. Falugi, S. Olaru, D. Dumur, “Multi-model predictive control based on LMI: From the adaptation of the state-space model to the analytic description of the control law”, International Journal of Control, vol. 83, number 8. Department of Automatic Control, SUPELEC Systems Sciences (E3S), Gif sur Yvette 91192, France, 2010, pp.1548-1563.
[8] C. Shao, J. Sheng, “Stability analysis and control of linear periodic time-delay systems with state-space models based on semi-discretization”, Proceedings of the 2012 UKACC International Conference on Control, Article number 6334729. Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States, Department of Automation, University of Science and Technology of China, Hefei, China, 2012, pp. 784-788.
[9] Y-W. T-seng, Y-N. Wang, “Model following variable structure control design in reciprocal state space framework with dead-zone nonlinearity and lumped uncertainty”, IEEE Conference on Industrial Electronics and Applications, Dept. of Electrical Engineering, I-Shou University, Kaohsiung, Taiwan, Wafer Test Factory, NXP Semiconductors Taiwan Ltd., Kaohsiung, Taiwan, ICIEA 2012 Singapore; 18 July 2012 through 20 July 2012; Category numberCFP1220A-CDR; Code 94705.
[10] W. Ya, C. Xin, W. Ming, H. Yong, “T-S fuzzy model based on time-delay state space for the control of burning through point”, Conference Proceeding IEEE Computer Society, Article number 6639744. School of Information Science and Engineering, Central South University, Changsha 410083, China, Hunan Engineering Laboratory for Advanced Control and Intelligent Automation, Changsha 410083, China, 2013, Category numberCFP1340A-CDR; Code 101424, pp. 1940- 1944.
[11] D. Honc, F. Düsek, “State-space constrained Model Predictive Control”, Conference Proceeding , 27th European Conference on Modelling and Simulation, Code 105094, Department of Process Control, Faculty of Electrical Engineering and Informatics, University of Pardubice, nám. Čs. legií 565, 532 10 Pardubice, Czech Republic, ECMS 2013, pp. 441-445.
[12] J. Zhang, “Improved nonminimal state space model predictive control for multivariable processes using a non-zero-pole decoupling formulation”, Industrial and engineering chemistry research, vol. 52 number 13, State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China, 2013, pp. 4874-4880.
[13] Li. H. HongboZou, “Tuning of PI-PD controller using extended non-minimal state space model predictive control for the stabilized gasoline vapor pressure in a stabilized tower” Chemometrics and Intelligent Laboratory System., vol. 142. Information and Control Institute, Hangzhou Dianzi University, Hangzhou, China, 2015, pp. 1-8
[14] K. Ogata, “Ingeniería de Control Moderna”, Editorial Prentice Hall. Quinta edición. México, 2010.
[15] Norma RA7-60. Valores de Pérdidas Equipos Bajo Condición de Carga. Disponible: http://www.epm.com.co/site/Portals/0/Users/001/01/1/RA7-060.pdf. Consultado: 20-06-12 hora: 10:30 am.
[16] J. Mathews, K. Find, “Métodos Numéricos con Matlab”. Editorial Prentice Hall. Tercera Edición. Madrid, España, 2005.
[17] E. STAFF DEL M.I.T. (1981). Circuitos Magnéticos y Transformadores. Editorial Reverte. Argentina.
[18] Forero., A (1999) Laboratorio de Metales. Universidad Nacional de Colombia. Facultad de Ingeniería. Bogotá, Colombia.
[19] Lajtin, Y (1983). “Metalografía y Tratamiento Térmico de los Metales”. Tercera edición. Editorial MIR. Moscú. URSS.
[20] Smith, W. (2006). “Fundamentos de la Ciencia e Ingeniería de los Materiales”. Editorial Mc Graw Hill. Cuarta edición. España.
Published
2016-09-21
How to Cite
Mago-Ramos, M., Vallés-Defendine, L., Olaya-Flórez, J., & Palomino-Naranjo, C. (2016). Application control model in state spaces from the total loss percentage obtained from the carbon silicon steel sheet. ITECKNE, 13(2), 127-136. https://doi.org/https://doi.org/10.15332/iteckne.v13i2.1477
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Section
Research and Innovation Articles
