Abstract This paper introduces a novel hybrid controller designed for a wind turbine power generation system (WTPGS) that utilizes a permanent magnet synchronous generator
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Next-generation wind turbine control systems are evolving with intelligent automation, predictive monitoring, and grid-aware design to drive efficiency, resilience, and sustainability in the
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Due to the complexity of wind turbine systems and the difficulty to predict varying wind speeds, artificial intelligence (AI) and machine learning (ML) algorithms have become key components when
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Optimizing wind turbine control is a major challenge due to wind variability and nonlinearity. This research seeks to improve the performance of wind turbines by designing and
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Due to the complexity of wind turbine systems and the difficulty to predict varying wind speeds, artificial intelligence (AI) and machine learning (ML) algorithms have become key
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Advanced controllers of modern wind turbines can help increase energy capture efficiency and reduce structural loading. However, to fulfill the modern wind turbine control demands with
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The depicted system in the following Fig. 1 is a sophisticated setup for a WECS-enabling DFIG. The model initiates the wind turbine harnessing kinetic energy, translating it into mechanical
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The future of wind turbine control is rapidly evolving through emerging technologies that leverage artificial intelligence, digital simulation, and hybrid system integration to push the
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This paper reviews advancements in intelligent control systems, notably those proposed by Smart Wind technologies. These systems leverage a network of sensors and IoT devices to gather real-time
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Wind farms'' power-generation efficiency is constrained by the high system complexity. A novel deep reinforcement learning (RL)-based wind farm control scheme is proposed to handle this
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