Sliding mode control scheme of variable speed wind energy conversion system based on the PMSG for utility network connection

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Abstract

The study of a Variable Speed Wind Energy Conversion System (VS-WECS) based on Permanent Magnet Synchronous Generator (PMSG) and interconnected to the electric network is presented. The system includes a wind turbine, a PMSG, two converters and an intermediate DC link capacitor. The effectiveness of the WECS can be greatly improved by using an appropriate control. Furthermore, the system has strong nonlinear multivariable with many uncertain factors and disturbances. Accordingly, the proposed control law combines Sliding Mode Variable Structure Control (SM-VSC) and Maximum Power Point Tracking (MPPT) control strategy to maximize the generated power from Wind Turbine Generator (WTG). Considering the variation of wind speed, the grid-side converter injects the generated power into the AC network, regulates DC-link voltage and it is used to achieve unity power factor, whereas the PMSG side converter is used to achieve Maximum Power Point Tracking (MPPT). Both converters used the sliding mode control scheme considering the variation ofwind speed. The employed control strategy can regulate both the reactive and active power independently by quadrature and direct current components, respectively. With fluctuating wind, the controller is capable tomaximize wind energy capturing. This work explores a sliding mode control approach to achieve power efficiency maximization of a WECS and to enhance system robustness to parameter variations. The performance of the system has been demonstrated under varying wind conditions. A comparison of simulation results based on SMC and PI controller is provided. The system is built using Matlab/Simulink environment. Simulation results show the effectiveness of the proposed control scheme.

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Errami, Y., Ouassaid, M., Cherkaoui, M., & Maaroufi, M. (2015). Sliding mode control scheme of variable speed wind energy conversion system based on the PMSG for utility network connection. Studies in Computational Intelligence, 576, 167–200. https://doi.org/10.1007/978-3-319-11173-5_6

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