Design and implementation of an Inverse Neural Network Controller applied To VSC Converter for active and reactive Power Flow, based on regions of work
Keywords:neural network, inverse neural control, Voltage Source Converter
Voltage Source Converter (VSC) usually used in High Voltage Direct Current (HVDC) systems, where a VSC can be used as inverter or rectifier. VSC systems allow the independent control of active or reactive power flow using different techniques. VSC systems present nonlinear behaviors, multiple inputs and multiple outputs, therefore nonlinear controllers can be used to obtain an adequate behavior. Inverse Neural Control is an alternative of an intelligent control since a mathematical model of the system is not required for designing controllers. Additionally, Inverse Neural Control can easily manage uncertainties and nonlinear behaviors typically presented in VSC systems. In this paper are presented the design, simulation and implementation of an Inverse Neural Control applied to the control of active and reactive power flow in a VSC system. Initially, is presented the simulation of the controller, where is evaluated the behavior of the system using a MIMO controller for the control of two parameters in the same time. Subsequently, the implementation of the controller is done and the e obtained is presented.
Finally, a modular Inverse Neural Network Control is proposed to overcome the drawbacks presented in the behavior of the system when it was controlled in real implementation.
J. Beerten, R. Belmans. “Modeling and Control of Multi-Terminal VSC HVDC Systems”. Energy Procedia. Vol. 24. 2012. pp. 123-130.
C. Trujillo, D. Velasco, J. Guarnizo, N. Díaz. “Design and implementation of a VSC for interconnection with power grids, using the method of identification the system through state space for the calculation of controllers”. Applied Energy. Vol. 9. 2011. pp. 3169-3175.
B. Parkhideh, S. Bhattacharya. “Vector-Controlled Voltage-Source-Converter-Based Transmission Under Grid Disturbances”. IEEE Transactions on Power Electronics. Vol. 28. 2013. pp. 661-672.
A. Leon, J. Mauricio, J. Solsona, A. Gómez. “Adaptive Control Strategy for VSC-Based Systems Under Unbalanced Network Conditions”. IEEE Transactions on Smart Grid. Vol. 1. 2010. pp. 311-319.
N. Díaz, C. Trujillo, J. Guarnizo. “Active and reactive power flow regulation for a grid connected vsc based on fuzzy controllers”. Revista de la Facultad de Ingeniería Universidad de Antioquia. N.° 66. 2013. pp. 118-130.
H. Latorre, M. Ghandhari, L. Söder. “Active and reactive power control of a VSC-HVdc”. Electric Power Systems Research. Vol. 78. 2008. pp. 1756-1763.
E. Acha, B. Kazemtabrizi, L. Castro. “A New VSC-HVDC Model for Power Flows Using the Newton-Raphson Method”. IEEE Transactions on Power Systems. Vol. 28. 2013. pp. 2602-2612.
N. Sadegh. “A perceptron network for functional identification and control of nonlinear systems”. IEEE Transactions on Neural Networks. Vol. 4. 1993. pp. 982- 988.
M. Norgaard, O. Ravn, N. Poulsen. “NNSYSID and NNCTRL tools for system identification and control with neural networks”. Computing & Control Engineering Journal. Vol. 12. 2001. pp. 29-36.
M. Kalantar, S. Mousavi. “Dynamic behavior of a stand-alone hybrid power generation system of wind turbine, microturbine, solar array and battery storage”. Applied Energy. Vol. 87. 2010. pp. 3051-3064.
E. Mamarelis, C. Ramos, G. Petrone, G. Spagnuolo, M. Vitelli, R. Giral. “Reducing the hardware requirements in FPGA based controllers: a photovoltaic application,” Revista Facultad de Ingeniería Universidad de Antioquia. N.° 68. 2013. pp. 75-87.
A. Morahana, P. Dash. “Input-Output Linearization and Robust Sliding-Mode Controller for the VSC-HVDC Transmission Link”. IEEE Transactions on Power Delivery. Vol. 25. 2010. pp. 1952-1961.
L. Haifeng, L. Gengyin, L. Guangkai, L. Peng, Y. Ming. Analysis and Design of Hinf Controller in VSC HVDC Systems. Proceedings of the Asia and pacific IEEE/PES. 2005. pp. 1-6.
J. Guarnizo, C. Trujillo, J. Lopez, J. Soriano. “Identificación de Reductores de Tensión Conmutados con Realimentación de Variables de Estado utilizando Redes Neuronales”. Ingenium. Vol. 20. 2009 pp. 46-53.
L. Aguirre, M. Correa, C. Cassini. “Nonlinearities in NARX polynomial models: representation and estimation”. Control Theory and Applications, IEE Proceedings. Vol. 149. 2002. pp. 343-348.
M. Norgaard, O. Ravn, N. Poulsen, L. Hansen. Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner’s Handbook. 3rd ed. Ed. Springer. London, Great Britain. 2003. pp. 126.
S. Haykin. Neural Networks: A Comprehensive Foundation. 2nd ed. Ed. Prentice Hall International. Hamilton, Canadá. 1999. pp. 183-198.
F. Forero, A. Molina, J. Guarnizo. Design of a Controller Inverse neural Applied to a Converter VSC for the Control of the Active and Reactive Power Flow. Proceedings of the (INTERCON) XIV Congreso Internacional de Ingenierías Eléctrica, Electrónica y Sistemas. Trujillo, Peru. 2008. pp. 213-221.
A. Molina, F. Forero, J. Guarnizo, H. Chamorro. Implementation of Inverse Neural Control To VSC Converter for Active and Reactive Power Flow. Proceedings of the ISAP ‘09. 15th International Conference Intelligent System Applications to Power Systems. Curitiba, Brazil. 2009. pp. 1-6.
How to Cite
Copyright (c) 2018 Revista Facultad de Ingeniería
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Revista Facultad de Ingeniería, Universidad de Antioquia is licensed under the Creative Commons Attribution BY-NC-SA 4.0 license. https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
You are free to:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
NonCommercial — You may not use the material for commercial purposes.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
The material published in the journal can be distributed, copied and exhibited by third parties if the respective credits are given to the journal. No commercial benefit can be obtained and derivative works must be under the same license terms as the original work.