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A wind flow predictor for turbines


European countries have generated electricity from wind turbine for decades and have optimised this technology far better than others. Their wind turbines are designed smartly according to changing wind flow conditions, they can stand crazy winds. As a result, their production of wind energy from wind turbines are greater than others.

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Windmills are major source of energy from wind. But wind is unpredictable sometimes. Whenever wind changes direction or goes crazy, wind turbine gets worn and torn, Its life span decreases and breaks down in worst case scenario. But now It’s possible to predict the next move of the wind with some special software algorithms.

Whenever wind put high pressure on low level area of blades, it creates a huge imbalance of forces acting on blades and as a result the rotation speed decreases and energy production too. But the effect is not only limited to electricity production but it also reduces life span of wind turbine.

A software has been developed with special set of algorithms which can predict future wind behaviour based on present behaviour of wind. The developers have been collecting data about how wind behaves in different conditions like humidity, season, rain, day, night, summer, heat, different temperatures, effect of pollution, etc.. All this data made it possible for software to predict wind flow.

This thing is not limited to theoreticals but this software has been implemented in real life. The software find best possible balance between electricity generation and wind turbine lifespan. Whenever an irregularity is predicted in future wind flow, the software alerts the operator and appropriate actions can be taken to stop the damage to wind turbine.

The software is based on 39 different parameters of wind. The detection uses an extended Kalman filter (EKF), EKF is based on 3D wind model and a turbine with 3 blades. This software simulates wind conditions in 3D world based on real word conditions and measures the torque and pressure on each blades.

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