Design nonlinear feedback strategy using H2/H∞ control and neural network based estimator for variable speed wind turbine

The exploitation of nature to convert energy to electrical power is the most important rule in power generation. Wind energy is one of the most important of those energies that are widely available, and its use does not affect the environment significantly compared to fossil energy. On the other hand, and in recent times, researchers have made great efforts in the field of intelligent control and optimization, which has led to great leaps in the development of these sciences. In this paper, a detailed study is proposed for filling the gaps and conducting an updating state-of-arts of the last pitch control methods in the wind turbine systems. The review is conducted by comparing the key requirements related to control, complexity, stability and speed rangeability. Furthermore, a new classification for the general controller is introduced according to the techniques. Several recommendations for future research related to the control and technical evaluation of wind energy are presented. In sum, the appropriate classification of such important issues and identification of their advantages and drawbacks may greatly contribute to find better solutions.

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Design nonlinear feedback strategy using H2/H∞ control and neural network based estimator for variable speed wind turbine

Cited by 3 publications

References 27 publications

Experimentally robustness improvement of DC motor speed control optimization by H-infinity of mixed-sensitivity synthesis

Experimentally robustness improvement of DC motor speed control optimization by H-infinity of mixed-sensitivity synthesis

Advancing voltage control in DC microgrids: a comparative study with IGWO-based H∞ control

A comprehensive overview of wind turbine controller technology: Emerging trends and challenges

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