Abstract
Fine tuning of controllers for pitch-torque regulated wind turbines is an opportunity to improve the wind turbine performances and reduce the cost of energy without applying any changes to the design. For this purpose, a method for automatically tune a classical controller based on numerical optimization is developed and tested. To have a better understanding of the problem a parametric analysis of the wind turbine performances due to changes in the controller parameters is first performed. Thereafter results obtained with the automatic tuning show that is possible to identify a finer controller tuning that improves the wind turbine performances. For the case study selected in this work, a 2% cost function reduction is achieved with seven iterations.
Highlights
Most of the controllers that have been presented for pitch-torque controlled variable speed wind turbines require a set of gains or weights that have to be selected to obtain the desired behavior of wind turbine
In the work by Hansen et al [1] the gains of a classical proportional integral (PI) controller are computed to minimize the standard deviation of the blade root flapwise bending moment
During the optimization most of the parameters are reduced, in accordance with the results shown in the parametric analysis
Summary
Most of the controllers that have been presented for pitch-torque controlled variable speed wind turbines require a set of gains or weights that have to be selected to obtain the desired behavior of wind turbine. The aim is to investigate a possible improvement in the wind turbine performances adjusting the controller parameters according with the loads computed during power production simulations. When the wind turbine is operating at the minimum rotor speed (Ωmin) or rated ΩR, the controller has to keep the rotational speed constant In these regions, the regulation is performed with a PI controller on the generator torque while the blade pitch is kept constant. When the simulations are terminated a post processing procedure extracts the equivalent fatigue loads, the ultimate loads and the power production performances These values are used to compute a scalar cost function and evaluate the fulfillment of the constraints, which goes into the optimization routine that computes new design variables. A gradient based optimization algorithm implemented in the Matlab function
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