andresrodriguez55/wind-turbines-edge-ai
BACHELOR THESIS. Detect wind turbine errors using a hybrid model. Utilize ML, DL, edge computing. Dataset, neural network optimized with genetic algorithms. Low computational power ML models. Data is stored in the cloud. Turbine status via secure APIs. Password encryption. GRASP, SOLID
This project helps wind farm operators and maintenance teams proactively detect potential errors in wind turbines. It takes real-time sensor data from turbines and analyzes it to predict internal (like electrical or mechanical issues) and external (like bird strikes or lightning) faults. The output is real-time status updates and early warnings, enabling timely interventions to prevent costly damage.
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Use this if you are responsible for monitoring the health and performance of wind turbines and want to detect errors early using a robust, efficient system.
Not ideal if you need a complex, cloud-heavy AI solution, as this focuses on lightweight models for edge devices and local processing.
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Java
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Jun 02, 2023
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