Abstract

Wake steering is an emerging wind power plant control strategy where upstream turbines are intentionally yawed out of perpendicular alignment with the incoming wind, thereby “steering” wakes away from downstream turbines. However, trade-offs between the gains in power production and fatigue loads induced by this control strategy are the subject of continuing investigation. In this study, we present a multifidelity multiobjective optimization approach for exploring the Pareto front of trade-offs between power and loading during wake steering. An unsteady large-eddy simulation is used as the high-fidelity model, where an actuator line representation is used to model wind turbine blades, and a rainflow-counting algorithm is used to compute damage equivalent loads. A coarser simulation with a simpler loads model is employed as a supplementary low-fidelity model. A multifidelity Bayesian optimization is performed to iteratively learn both a surrogate of the low-fidelity model and an additive discrepancy function, which maps the low-fidelity model to the high-fidelity model. Each optimization uses the expected hypervolume improvement acquisition function, weighted by the total cost of a proposed model evaluation in the multifidelity case. The multifidelity approach is able to capture the logit function shape of the Pareto frontier at a computational cost that is only 30 % of the single fidelity approach. Additionally, we provide physical insights into the vortical structures in the wake that contribute to the Pareto front shape.

Highlights

  • 15 As wind energy systems have matured, plant-level control has emerged as a new paradigm, where groups of turbines are controlled in coordination to maximize collective power production

  • This allows the performance of wind power plants to be improved by diverting wakes away from downstream turbines It is speculated that wake steering may produce more power while inducing less total fatigue on all turbines when compared to the baseline strategy of aligning each turbine with the incoming wind (Howland et al, 2019; Hulsman et al, 2020)

  • The dashed lines show the hypervolume, best-sampled load, and best-sampled power found from 100 sampled points using the heuristic sampling 265 approach, which the single-fidelity and multifidelity approaches both outperformed

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Summary

Introduction

15 As wind energy systems have matured, plant-level control has emerged as a new paradigm, where groups of turbines are controlled in coordination to maximize collective power production. 20 blades (Fleming et al, 2018; Martínez-Tossas and Branlard, 2020), and the direction of rotation of these vortices is determined by the direction of thrust of the wind turbine rotor, which is determined by the yaw offset direction This allows the performance of wind power plants to be improved by diverting wakes away from downstream turbines It is speculated that wake steering may produce more power while inducing less total fatigue on all turbines when compared to the baseline strategy of aligning each turbine with the incoming wind (Howland et al, 2019; Hulsman et al, 2020). We propose a multifidelity multiobjective optimization framework to address this challenge and explore trade-offs between power and loading in wake 40 steering strategies. Imsland (2020), the exact framework outlined here is new—and this is the first demonstration of any such approach in the 60 context of wind energy systems

Optimization Framework
Single-Fidelity Approach
Multifidelity Approach
Objective Functions
Optimization Implementation
Initial Sampling
Low-Fidelity Loading Model
Pareto Set Computation
Flow Physics Insights
Conclusions
370 References
Full Text
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