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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To address this challenge, this article pro-poses a coupled electricity-carbon market and wind-solar-storage complementary hybrid power generation system model, aiming to maximize energy complementarity benefits and economic efficiency. . As the development of new hybrid power generation systems (HPGS) integrating wind, solar, and energy storage progresses, a significant challenge arises: how to incorporate the electricity-carbon market mechanism into the planning of power system capacity. power grid in 2025 in our latest Preliminary Monthly Electric Generator Inventory report. This amount represents an almost 30% increase from 2024 when 48. 6 GW of capacity was installed, the largest. .
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