Abstract
Increasing demands in decentralized power plants have focused attention on Vertical Axis Wind Turbines (VAWTs). However, accessing high range of power from VAWTs is an impediment due to increased loads on the turbine blades. Here, we derive an optimal pitching action that reduces the periodic disturbance on turbine blades of VAWTs without affecting their power production. A control technique called Subspace Predictive Repetitive Control (SPRC) alongwith a LQ Tracker is used for recursive identification to estimate the parameters of VAWT model and further provide an optimal control law accordingly. Basis functions have been used to reduce the dimensionality of the control problem. Simulation results show a great potential of the data-driven SPRC approach coupled with LQ Tracker in reducing the turbine loads on VAWTs.
Highlights
Over the past decade, the demand for wind energy has progressed significantly
Various research groups have been working towards active control for reducing the blade root loads of a wind turbine
Bossanyi (2003) proposed an Individual Pitch Control (IPC) method to reduce the periodic loading of the wind turbine
Summary
The demand for wind energy has progressed significantly. the capital costs involved still pose a hindrance to its widespread. Various research groups have been working towards active control for reducing the blade root loads of a wind turbine. Bossanyi (2003) proposed an Individual Pitch Control (IPC) method to reduce the periodic loading of the wind turbine.
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