Advertisement

An AC Power Conditioning with Robust Intelligent Controller for Green Energy Applications

  • En-Chih ChangEmail author
  • Rong-Ching Wu
  • Chun-An Cheng
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 834)

Abstract

In this paper, a high-performance AC power conditioning is developed by using a robust intelligent controller. The moving sliding mode control (MSMC) ensures the sliding mode occurrence from an arbitrary initial state. Once a highly uncertain perturbation occurs, the MSMC has chatter problem, thus leading to high voltage distortion and performance deterioration of AC power conditioning. To weaken the chatter, the BPSO is employed to optimally tune the MSMC gains for achieving good steady state and transience. Using the proposed controller, the robustness of the AC power conditioning is effectively enhanced, and low distorted output voltage can be obtained against load disturbances. Experiments are given to demonstrate the efficacy of the proposed controller. Because the presented proposed controller provides better tracking exactness and convergence rate, this paper will be an applicable reference to the researchers of correlative robust control, evolutionary algorithm, and green energy applications.

Keywords

AC power conditioning Moving sliding mode control (MSMC) Chatter Binary particle swarm optimization (BPSO) Green energy applications 

Notes

Acknowledgements

This work was supported by the Ministry of Science and Technology of Taiwan, R.O.C., under contract number MOST107-2221-E-214-006.

References

  1. 1.
    Ibrahim, D., Adnan, M., Haydar, K.: Progress in Sustainable Energy Technologies: Generating Renewable Energy. Springer International Publishing, Switzerland (2014)Google Scholar
  2. 2.
    Itkis, U.: Control Systems of Variable Structure. Wiley, New York (1976)Google Scholar
  3. 3.
    Malesani, L., Rossetto, L., Spiazzi, G., Zuccato, A.: An AC power supply with sliding-mode control. In: Proceedings of IEEE International Conference on Industry Applications Society Annual Meeting, pp. 623–629 (1993)Google Scholar
  4. 4.
    Sarinana, A.: A novel sliding mode observer applied to the three-phase voltage source inverter. In: Proceedings of European. Conference on Power Electronics, and Applications, pp. 1–12 (2005)Google Scholar
  5. 5.
    Geng, J., Sheng, Y.Z., Liu, X.D.: Second-order time-varying sliding mode control for reentry vehicle. Int. J. Intell. Comput. Cybern. 6(3), 272–295 (2013)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Li, L., Zhang, Q.Z., Rasol, N.: Time-varying sliding mode adaptive control for rotary drilling system. J. Comput. 6(3), 564–570 (2011)Google Scholar
  7. 7.
    Alireza, S., Mozhgan, A.: Binary PSO-based dynamic multi-objective model for distributed generation planning under uncertainty. IET Renew. Power Gener. 6(2), 67–78 (2012)CrossRefGoogle Scholar
  8. 8.
    Lin, C.J., Chern, M.S., Chih, M.C.: A binary particle swarm optimization based on the surrogate information with proportional acceleration coefficients for the 0-1 multidimensional knapsack problem. J. Ind. Prod. Eng. 33(2), 77–102 (2016)Google Scholar
  9. 9.
    Chen, X.Y., Peng, X.Y., Li, J.B., Peng, Y.: Overview of deep kernel learning based techniques and applications. J. Netw. Intell. 1(3), 83–98 (2016)Google Scholar
  10. 10.
    Fournier-Viger, P., Lin, C.W., Kiran, R.U., Koh, Y.S., Thomas, R.: A survey of sequential pattern mining. Data Sci. Pattern Recogn. 1(1), 54–77 (2017)Google Scholar
  11. 11.
    Wu, C.M., Gong, H.Q., Yang, J.H., Song, Q.H., Wang, Y.J.: An improved FOA to optimize GRNN method for wind turbine fault diagnosis. J. Inf. Hiding Multimed. Signal Process. 9(1), 1–10 (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electrical EngineeringI-Shou UniversityKaohsiung CityTaiwan, ROC

Personalised recommendations