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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This study focused on optimizing the performance of energy microgrids, factoring in economic and environmental metrics for day-ahead planning. The proposed microgrid features a combination of hybrid energy resources, which include power, heat, and hydrogen systems. . The interplay between energy, social sustainability, and the economic and environmental dimensions has prompted energy operators to explore various challenges associated with energy operations. In the upper optimization model, the wind–solar–storage capacity optimization model is. . A microgrid is a promising small-scale power generation and distribution system. The selling prices of wind turbine equipment (WT), photovoltaic generation equipment (PV), and battery energy storage equipment (BES) have a significant impact on microgrid profits, which, in turn, affects the planning. .
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