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In this paper, an improvement of the particle swarm optimization algorithm is proposed. The aim of this algorithm is to determine the optimal parameters of a robust fractional order controller which guarantees stability and robustness of required nominal performances. Controllers with fractional order are first obtained by a previous transformation of the multi-objective optimization problem into an equivalent single-optimization problem, and then solved by the proposed improved particle swarm optimization algorithm. In order to examine the stability and performance robustness, this controller is applied on an ill-conditioned wind turbine equipped with a doubly fed asynchronous machine, where the system dynamics is modeled by an unstructured output multiplicative uncertainty model. The simulation results show the effectiveness of the proposed synthesis method, where the control is compared (for the same design frequency-domain specifications) for both the robust fractional order controller such as that designed through the standard particle swarm optimization algorithm and that obtained by resolving the multi-objective optimization problem.

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The most important issue in the use of wind energy conversion systems is to ensure maximum power extraction in terms of efficiency. Therefore, maximum power point tracking algorithms are as important as the maximum power point tracking controller. In this study, maximum power extraction frameworks operating the state-of-the-art optimization methods are presented for permanent magnet synchronous generator–based wind energy conversion system. These frameworks consist of a Gauss map–based chaotic particle swarm optimization and a hybrid maximum power point tracking approach that combines feedback linearization technique with fractional-order calculus. The feedback linearization control strategy can fully decouple and linearize the original state variables of the nonlinear system and thus provide an optimal controller crossing wide-range operating conditions. The objective is to maintain the tip speed ratio at its optimal value, which implies the use of a rotational speed loop. The method is based on the feedback linearization technique and the fractional control theory. Gauss map–based chaotic particle swarm optimization, which is a remarkable and recent optimization technique, is utilized to achieve optimum coefficients to efficiently ensure the maximum power point tracking operation in here. A simulation study is carried out on a 3-kW wind energy conversion system to show the effectiveness of the proposed control scheme.

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A robustification method of primary two degree-of-freedom (2-DOF) controllers is proposed in this paper to control the wind turbine system equipped with a doubly-fed induction generator DFIG. The proposed robustification method should follow the following three step-procedures. First, the primary 2-DOF controller is designed through the initial form of the multivariable generalized predictive control MGPC law to ensure a good tracking dynamic of reference trajectories. Second, the robust [Formula: see text] controller is independently designed for the previous system to ensure good robustness properties of the closed-loop system against model uncertainties, neglecting dynamics and sensor noises. Finally, both above mentioned controllers are combined to design the robustified 2-DOF-MGPC controller using Youla parameterization method. Therefore, the obtained controller conserves the same good tracking dynamic that is provided by the primary 2-DOF-MGPC controller. It ensures the same good robustness properties which are produced by the robust [Formula: see text] controller. A wind turbine system equipped with a DFIG is controlled by the robustified 2-DOF-MGPC controller. Its dynamic behaviour is modelled by an unstructured-output multiplicative uncertainty plant. The controller performances are valid by comparison with those given through both controllers, which are primary 2-DOF-MGPC and robust [Formula: see text] controllers in time and frequency domains.