To address the inherent challenges of intermittent renewable energy generation, this paper proposes a comprehensive energy optimization strategy that integrates coordinated wind–solar power dispatch with strategic battery storage capacity allocation. . Among such solutions, hybrid renewable energy systems - comprising a mix of wind, solar, and battery storage - have emerged as a notably robust and efficient approach to meet today's global energy demands. These systems offer numerous benefits, ranging from increased reliability to reduced. . With the progressive advancement of the energy transition strategy, wind–solar energy complementary power generation has emerged as a pivotal component in the global transition towards a sustainable, low-carbon energy future. In order to evaluate the functionality of the hybrid microgrid, power electronic converters, controllers, control algorithms, and battery storage systems have. .
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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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