Dynamic Wind Energy Optimization System using GNN and SEPIC Converter

Abstract

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This paper presents an optimized wind energy conversion system for increased energy output through the use of Graph Neural Networks (GNNs) and machine learning. In order to increase efficiency, the system incorporates a SEPIC converter and keeps track of torque, acceleration, and RPM for the turbine. The GNN model uses historical and current wind speed data to dynamically modify turbine operation in order to reduce losses. Reliability and consistent performance are guaranteed by automated alerts and remote monitoring for problems like dust buildup. This method establishes a standard for sustainable wind energy systems and greatly increases energy utilization. The proposed integration also enhances accuracy in energy forecasting and significantly reduces conversion losses. This is achieved by coupling advanced learning capabilities of GNNs with the stabilization features of SEPIC, resulting in a more reliable and efficient energy conversion system. The integration of GNNs and SEPIC converters enhances wind turbine energy efficiency by dynamically optimizing performance and reducing losses.