Wind turbine control systems serve as the central intelligence of each turbine, managing functions such as blade pitch, yaw adjustments, energy conversion, and fault detection. . This study develops a robust nonlinear control, using an integral sliding mode control (ISMC) associated to an artificial neural network (ANN) approach for a variable-speed wind turbine (VSWT). At below rated speed of wind, the control aims to extract the maximum energy from the wind by the WT as. . This evolution calls for next-generation wind turbine control systems—a fusion of intelligent automation, digitalization, and adaptive control technologies.
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Distributed secondary control schemes, which leverage local neighboring information, offer a more promising solution for MGs. They minimize resource consumption and enhance efficiency and reliability without significantly increasing design and installation complexity. . This work focuses on enhancing microgrid resilience through a combination of effective frequency regulation and optimized communication strategies within distributed control frameworks using hybrid energy storages. Through the integration of distributed model predictive control (MPC) for frequency. . In this paper, a distributed control is proposed for Distributed Energy Storage Systems (DESSs) and Renewable Energy Sources (RESs) power management in islanded Microgrid (MG). The power management strategy is designed to maintain generation/consumption balance, to ensure State of Charge (SoC). .
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