Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Robust Adaptive Voltage Control of Electric Generators for Ships

선박용 발전기 시스템의 강인 적응형 전압 제어

  • 조현철 (울산과학대학교 전기전자공학부)
  • Received : 2016.02.10
  • Accepted : 2016.03.23
  • Published : 2016.05.01
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Abstract

This paper presents a novel robust adaptive AC8B exciter system against synchronous generators for ships. A PID (proportional integral derivative) control framework, which is a part of the AC8B exciter system, is simply composed of nominal and auxiliary control configurations. For selecting these proper parameter values, the former is conventionally chosen based on the experience and knowledge of experts, and the latter is optimally estimated via a neural networks optimization procedure. Additionally, we propose an online parameter learning-based auxiliary control to practically cope with deterioration of control performance owing to uncertainty in electric generator systems. Such a control mechanism ensures the robustness and adaptability of an AC8B exciter to enhance control performance in real-time implementation. We carried out simulation experiments to test the reliability of the proposed robust adaptive AC8B exciter system and prove its superiority through a comparative study in which a conventional PID control-based AC8B exciter system is similarly applied to our simulation experiments under the same simulation scenarios.

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References

  1. P. M. Anderson and A. A. Fouad, Power System Control and Stability, Wiley Inter-Science, New Jersey, 2003.
  2. M. Ibrahim and P. Pillay, "Hysteresis-dependent model for the brushless exciter of synchronous generators," IEEE Transaction on Energy Conversion, vol. 30, no. 4, pp. 1321-1328, 2015. https://doi.org/10.1109/TEC.2015.2432763
  3. A. Griffo, R. Wrobel, P. H. Mellor, and J. M. Yon, "Design and characterization of a three-phase brushless exciter for aircraft starter/generator," IEEE Transaction on Industry Applications, vol. 49, no. 5, pp. 2106-2115, 2013. https://doi.org/10.1109/TIA.2013.2269036
  4. K. De Morais Sousa, W. Probst, F. Bortolotti, C. Martelli, and J. Da Silva, "Fiber bragg grating temperature sensors in a 6.5MW generator exciter bridge and the development and simulation of its thermal model," Sensors, vol. 14, no. 9, pp. 16651-16663, 2014. https://doi.org/10.3390/s140916651
  5. H. C. Cho, H. S. Lee, M. J. Heo, S. T. Oh, and J. H. Ahn, "Dynamic simulation of synchronous generator systems for ships," KIEE Summer Conference, pp. 1302-1303, 2015.
  6. Operation Technology Inc, ETAP(R) 12.6 user guide, 2014.
  7. S. O. Haykin, Neural Networks and Learning Machines, Prentice Hall, Upper Saddle River, 2008.
  8. H. C. Cho and M. S. Fadali, "Nonlinear network induced time delay systems with stochastic learning approach," IEEE Transaction on Control System Technology, vol. 19, no. 4, pp. 843-851, 2011. https://doi.org/10.1109/TCST.2010.2056925
  9. H. C. Cho and S. M. Fadali, "Online learning algorithm of dynamic Bayesian networks for nonstationary signal processing," International Journal of Innovative Computing, Information and Control, vol. 5, no. 4, pp. 1027-1041, 2009.