Wind power is a clean, endless basis of energy that leaves no trace behind. In this study, recurrent neural network (RNN) control for a wind energy conversion system (WECS) founded on a doubly fed
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A method is provided for generating power through artificially induced wind flow. Naturally available energy such as sunlight is used to create high and low air pressure areas artificially in...
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It explores the use of cascaded doubly fed induction generator (CDFIG) benefiting their advantage and proposes a control strategy combining neural networks (NN) with traditional PI
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This article presents novel artificial intelligence (AI)-based techniques for controlling wind energy conversion systems, specifically fuzzy logic control and neural networks, known as fuzzy
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This research is focused on the study and development of an advanced Direct Power Control (DPC) system, using Artificial Neural Networks algorithm, for wind energy conversion
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In recent years, data-driven approaches and machine learning-based methods have helped to enhance the operation and maintenance (O&M) of wind farms. These techniques can
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Artificial neural networks (ANN) have emerged as a powerful tool in wind turbine applications, offering sophisticated control capabilities for addressing the intricate challenges of
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According to recent studies, artificial intelligence accurately predicts wind-power generation, energy production, and power and usage demand, enabling smart grids to store and
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Wind energy, a critical component of the global renewable energy mix, faces challenges related to system reliability and maintenance. Traditional methods for detecting faults and scheduling
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To quantify the potential electrical energy that can be generated from this man-made wind resource. By doing so, the paper aims to establish a new perspective on sustainable energy
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