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Neuron‐Adaptive PID Based Speed Control of SCSG Wind Turbine System

Author

Listed:
  • Shan Zuo
  • Yongduan Song
  • Lei Wang
  • Zheng Zhou

Abstract

In searching for methods to increase the power capacity of wind power generation system, superconducting synchronous generator (SCSG) has appeared to be an attractive candidate to develop large‐scale wind turbine due to its high energy density and unprecedented advantages in weight and size. In this paper, a high‐temperature superconducting technology based large‐scale wind turbine is considered and its physical structure and characteristics are analyzed. A simple yet effective single neuron‐adaptive PID control scheme with Delta learning mechanism is proposed for the speed control of SCSG based wind power system, in which the RBF neural network (NN) is employed to estimate the uncertain but continuous functions. Compared with the conventional PID control method, the simulation results of the proposed approach show a better performance in tracking the wind speed and maintaining a stable tip‐speed ratio, therefore, achieving the maximum wind energy utilization.

Suggested Citation

  • Shan Zuo & Yongduan Song & Lei Wang & Zheng Zhou, 2014. "Neuron‐Adaptive PID Based Speed Control of SCSG Wind Turbine System," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:376259
    DOI: 10.1155/2014/376259
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    References listed on IDEAS

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    1. Shen Yin & Steven Ding & Adel Abandan Sari & Haiyang Hao, 2013. "Data-driven monitoring for stochastic systems and its application on batch process," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(7), pages 1366-1376.
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    3. Lei Wang & Shan Zuo & Y. D. Song & Zheng Zhou, 2014. "Variable Torque Control of Offshore Wind Turbine on Spar Floating Platform Using Advanced RBF Neural Network," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-7, March.
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