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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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https://doi.org/10.5194/wes-2020-18
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2020-18
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 04 Feb 2020

Submitted as: research article | 04 Feb 2020

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This preprint is currently under review for the journal WES.

Real-time optimization of wind farms using modifier adaptation and machine learning

Leif Erik Andersson and Lars Imsland Leif Erik Andersson and Lars Imsland
  • Norwegian University of Science and Technology, Department of Engineering Cybernetics, 7491 Trondheim, Norway

Abstract. Real-time optimization (RTO) covers a family of optimization methods that incorporate process measurements in the optimization to drive the real process (plant) to optimal performance while guaranteeing constraint satisfaction. Modifier Adaptation (MA) introduces zeroth and first-order correction terms (bias and gradients) for the cost and constraint functions. Instead of updating the plant model, in MA the optimization problem is updated directly from data guaranteeing to meet the necessary condition of optimality upon convergence.

The main burden of the MA approach is the estimation of the first-order modifiers of the cost and constraint functions at each RTO iteration. Finite-difference approximation is the most common approach that requires at least nu + 1 steady-state operation points to estimate the gradients, where nu is the number of control inputs. Obtaining these can require a long convergence time. For this reason, this work considers the use of Gaussian process (GP) regression to estimate the plant-model mismatch based on plant measurements, and replace the usual modifiers by these high order regression functions. GP is a probabilistic, non-parametric modelling technique well known in the machine learning community. The approach is tested on several numerical test cases simulating wind farms. It is shown that the approach is able to correct the model and converges to the plant optimal point. Several improvements for large inputs spaces, which is a challenging problem for the approach presented in the article, are discussed.

Leif Erik Andersson and Lars Imsland

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Status: final response (author comments only)
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Leif Erik Andersson and Lars Imsland

Leif Erik Andersson and Lars Imsland

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Latest update: 01 Apr 2020
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Short summary
The article describes a hybrid modelling approach to optimize the energy capture of a wind farm. Hybrid modelling combines mechanistic and data-driven models. The data-driven part is used to correct inaccuracies of the mechanistic model. The hybrid approach allows for adjustment of the mechanistic model beyond simple parameter estimation. It is, therefore, an attractive approach in wind farm control. The approach is illustrated on several numerical case studies.
The article describes a hybrid modelling approach to optimize the energy capture of a wind farm....
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