Simulation of High Aspect Ratio aeroplanes and wind turbines in Python: a nonlinear aeroelastic code

AMReX-based structured wind solver

My master's dissertation on wind turbine fault prediction using machine learning

A Comprehensive Julia implementation of the Vortex Lattice Method

Dashboard designed to demonstrate the power of Machine Learning to predict failures (Remaining Useful Life (RUL)) in wind turbines. To predict the date when equipment will completely fail (RUL), XGBoost is used and achieved RMSE error is 0.033964 days, which is highly accurate.

Controllers designed to the 5MW NREL wind turbine using Simulink and Fast V8

Provides Home Assistant sensors for multiple wind turbines from the Windcentrale

Wind turbine fault detection using one class SVM

In this project, I have employed various regression techniques to estimate the Power curve of an on-shore Wind turbine. Nonlinear trees based ensemble regression methods perform best as true power curve is nonlinear. I have implemented and optimized XGBoost using GridSearchCV that yields lowest Test RMSE-6.404.

Structural Health Monitoring of Wind Turbines

Wind Energy : A Practical Power Analysis Approach - Open Sourced Code for the Research Paper published in IEEE.

This repository is dedicated to wind turbines power curve modeling, from data cleansing to the actual power curve modeling with various approches.

Basic design routine for domestic wind turbine design