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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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Preprints
https://doi.org/10.5194/wes-2020-63
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2020-63
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  26 Mar 2020

26 Mar 2020

Review status
A revised version of this preprint was accepted for the journal WES and is expected to appear here in due course.

Automatic controller tuning using a zeroth-order optimization algorithm

Daniel S. Zalkind, Emilano Dall'Anese, and Lucy Y. Pao Daniel S. Zalkind et al.
  • Department of Electrical, Computer & Energy Engineering, University of Colorado Boulder, Boulder, CO 80309, USA

Abstract. We develop an automated controller tuning procedure for wind turbines that uses the results of nonlinear, aeroelastic simulations to arrive at an optimal solution. Using a zeroth-order optimization algorithm, simulations using controllers with randomly generated parameters are used to estimate the gradient and converge to an optimal set of those parameters. We use kriging to visualize the design space and estimate the uncertainty, providing a level of confidence in the result.

The procedure is applied to three problems in wind turbine control. First, the below-rated torque control is optimized for power capture. Next, the parameters of a proportional-integral blade pitch controller are optimized to minimize structural loads with a constraint on the maximum generator speed; the procedure is tested on rotors from 40 to 400 m in diameter and compared with the results of a grid search optimization. Finally, we present an algorithm that uses a series of parameter optimizations to tune the lookup table for the minimum pitch setting of the above-rated pitch controller, considering peak loads and power capture. Using experience gained from the applications, we present a generalized design procedure and guidelines for implementing similar automated controller tuning tasks.

Daniel S. Zalkind et al.

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Daniel S. Zalkind et al.

Daniel S. Zalkind et al.

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Latest update: 29 Oct 2020
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Short summary
New wind turbine designs require updated control parameters, which should be optimal in terms of the performance measures that drive hardware design. We show how a zeroth-order optimization algorithm can randomly generate control parameters, use simulation results to estimate the gradient of the parameter space, and find an optimal set of those parameters. We then apply this automatic controller tuning procedure to three problems in wind turbine control.
New wind turbine designs require updated control parameters, which should be optimal in terms of...
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