Abstract
The framework of adaptive control applied to a wind turbine is presented. The wind turbine is adaptively controlled to achieve a balance between two objectives, power maximization and minimization of the generator torque ramp rate. An optimization model is developed and solved with a linear weighted objective. The objective weights are autonomously adjusted based on the demand data and the predicted power production. Two simulation models are established to generate demand information. The wind power is predicted by a data-driven time-series model utilizing historical wind speed and generated power data. The power generated from the wind turbine is estimated by another model. Due to the intrinsic properties of the data-driven model and changing weights of the objective function, a particle swarm fuzzy algorithm is used to solve it. © 2010 IEEE.
| Original language | English |
|---|---|
| Article number | 5560847 |
| Pages (from-to) | 28-36 |
| Journal | IEEE Transactions on Sustainable Energy |
| Volume | 2 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 2011 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Research Keywords
- Adaptive control
- blade pitch angle
- data mining
- electricity demand simulation
- generator torque
- neural networks
- optimization
- particle swarm fuzzy algorithm
- power prediction
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