Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Adaptive self-structuring fuzzy controller of wind energy conversion systems

풍력 발전 계통의 자기 구조화 적응 퍼지 제어기 설계

  • Park, Jang-Hyun (Dept. of Control and Robot Engineering, Mokpo National University)
  • 박장현 (목포대학교 제어로봇공학과)
  • Received : 2013.01.29
  • Accepted : 2013.03.26
  • Published : 2013.04.25
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Abstract

This paper proposes an online adaptive fuzzy controller for a wind energy conversion system (WECS) that is intrinsically highly nonlinear plant. In real application, to obtain exact system parameters such as power coefficient, many measuring instruments and off-line implementations are required, which is very difficult to perform. This shortcoming can be avoided by introducing fuzzy system in the controller design in this paper. The proposed adaptive fuzzy control scheme using self-structuring algorithm requires no system parameters to meet control objectives. Even the structure of the fuzzy system is automatically grows on-line, which distinguishes our proposed algorithm over the previously proposed fuzzy control schemes. Combining derivative estimator for wind velocity, the whole closed-loop system is shown to be stable in the sense of Lyapunov.

본 논문은 내재적으로 고도의 비선형 계통인 풍력 발전 계통 (wind energy conversion system, WECS)의 온라인(online) 적응 퍼지 제어기를 제안한다. 풍력 발전기를 실제 운전하기 위해서는 전력 계수 등과 같은 계통 파라메터를 사전에 측정해야 하며 다수의 센서들을 이용하여 풍동에서 실험이 수행되는데 많은 측정 장비와 수많은 실험이 요구되므로 어려움이 따른다. 이러한 단점을 극복하고자 본 논문에서는 제어기의 설계에 퍼지 논리 시스템(fuzzy logic system, FLS)을 도입한다. 제안된 적응 퍼지 제어기는 자기구조화 알고리듬을 채택하여 제어 목적에 부합하는 FLS 파라메터들을 미리 결정할 필요 없이 자동으로 결정이 된다. 기존에 재안된 퍼지 제어 알고리듬에 비해서 본 논문은 자기 구조화 알고리듬을 채택하여 FLS의 구조 자체도 온라인으로 점차 수립하게 된다. 또한 풍속의 미분을 추정하는 미분추정기를 도입하였으며 전체 폐루프 제어계의 리아프노브 안정도를 증명하였다.

Keywords

References

  1. S. Heier, Grid integration of wind enery conversion systems, Wiley, New York, 1998.
  2. Ph. Delarue, et. al., "Modelling, control and simulation of an overall wind energy conversion system," Renewable Energy, vol. 28 pp.1169-1185, 2003. https://doi.org/10.1016/S0960-1481(02)00221-5
  3. Y.D. Song, B. Dhinakaran, and X.Y. Bao,"Variable speed control of wind turbines using nonlinear and adaptive algorithms," Journal of Wind Engineering and Industrial Aerodynamics, vol. 85. pp293-308, 2000. https://doi.org/10.1016/S0167-6105(99)00131-2
  4. Andrew Miller, Edward Muljadi, and Donald S. Zinger. "A variable speed wind turbine power control," IEEE Transactions on Energy Conversion, vol. 12, no. 2, 1997.
  5. W. Qiao, et. al.,"Wind speed estimation based sensorless output maximum control for a wind turbine driving a DFIG," IEEE Trans. Power Electronics, vol. 23, no. 3, 2008.
  6. S. Bhomik R. Spec, and J. H. R. enslin, "Performance optimization for doubly fed wind power generation systems," IEEE Trans. Ind. Appl., vol. 35, no. 4, pp. 949-958, 1999. https://doi.org/10.1109/28.777205
  7. W.-M. Lin, C.-M. Hong, and F.-S. Cheng, "Design of intelligent controllers for wind generation system with sensorless maximum wind energy control," Energy Conversion and Management, vol. 52, 2011.
  8. H. Li, K. L. Shi, and P. G. McLaren, "Neural-network based sensorless maximum wind energy capture with compensated power coefficient," IEEE Trans. Ind. Appl., vol. 41, no. 6, pp. 1548-1556, 2005. https://doi.org/10.1109/TIA.2005.858282
  9. M. Pichan, and M. Monfared, "Fuzzy-based direct power control of doubly fed induction generator-based wind energy conversion systems," Proceedings of 2nd International eConference on Computer and Knowledge Engineering, pp. 66-70, Oct., 2012.
  10. M. G. Simoes, B. K. Bose, and R. J. Spieget, "Fuzzy logic based intelligent control of a variable speed cage machine wind generation system," IEEE Trans. Power Electron., vol. 12, no. 1, pp. 87-95, 1997. https://doi.org/10.1109/63.554173
  11. K. Mehrdad, A. Hossein, and K. Amir, "An enhenced MPPT fuzzy control of a wind turbine equipped with permanent magnet synchronous generator," Proceedings of 2nd International eConference on Computer and Knowledge Engineering, pp. 18-19, Oct., 2012.
  12. P. Dewangan, and S. D. Bharti, "Grid connected doubly fed induction generator wind energy conversion system using fuzzy controller," Int. J. Innovative Technology and Exploring Engineering, vol. 2, no. 2, pp. 119-122, Jan., 2013.
  13. J.-H. Park, S.-H. Kim, and C.-J. Moon, "Adaptive neural control for strict-feedback nonlinear systems without backstepping," IEEE Trans., Neural Networks, vol. 20, no. 7, pp. 1204-1209, 2009. https://doi.org/10.1109/TNN.2009.2020982
  14. J-H Park, et. al., "State- and output-feedback adaptive controller for pure-feedback nonlinear systems using self-structuring fuzzy system," Trans. KIEE, vol. 61, no. 9, pp. 1319-1329, 2012. https://doi.org/10.5370/KIEE.2012.61.9.1319
  15. C.-T. Lin, and C. S. G. Lee, Neural fuzzy systems, New Jersey : Prentice Hall, 1996.
  16. S. Behatsh, "Robust output tracking for nonlinear systems," Int. J . Control , vol. 51, no. 6, pp. 1381-1407, 1990. https://doi.org/10.1080/00207179008934141
  17. A. L. Elshafei, and M. A. Azzouz, "Adaptive fuzzy regulation of the DC-bus capacitor voltage in a wind energy conversion system (WECS)," Experts Systems with Applications, vol. 38, pp. 5500-5506, 2011. https://doi.org/10.1016/j.eswa.2010.10.087
  18. D. Chen, S. Liu, and X. Ma, "Modeling, nonlinear dynamical analysis of a novel power system with random wind power and it's control," Energy, 2013, in press.