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

Andersson, Leif Erik; Imsland, Lars

doi:10.5194/wes-5-885-2020

Cited by 17 publications

(16 citation statements)

References 46 publications

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“…The present approach uses the low-fidelity model to first explore the full parameter space, then iteratively builds the low-and high-fidelity model surrogates to gain the most improvement in the Pareto front per model evaluation costs. While this framework is similar to those presented by Ariyarit and Kanazaki (2017) and Andersson and Imsland (2020), the exact framework outlined here is new-and this is the first demonstration of any such approach in the context of wind energy systems.…”

Section: Introductionmentioning

confidence: 79%

Multifidelity Multiobjective Optimization for Wake Steering Strategies

Quick¹,

King²,

Barter³

et al. 2022

Preprint

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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.

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Section: Introductionmentioning

confidence: 79%

Multifidelity Multiobjective Optimization for Wake Steering Strategies

Quick¹,

King²,

Barter³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In this article the MA-GP approach proposed by [20] is applied to a nine-turbine wind farm simulated with SOWFA. Alternatively, to the MA-GP approach the BO approach is tested, which uses no a priori plant model.…”

Section: Discussionmentioning

confidence: 99%

“…Consequently, MA can correct the inaccurate surrogate model using plant measurements and provides a viable closed-loop model-based wind farm control solution. Recently, a method was proposed that combines MA with Gaussian process (GP) regression for wind farm optimization [20]. The GP regression is used to correct for the plant-model mismatch.…”

Section: Introductionmentioning

confidence: 99%

Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data

Andersson,

Doekemeijer,

van der Hoek

et al. 2020

Preprint

Self Cite

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This article investigates the optimization of yaw control inputs of a nine-turbine wind farm. The wind farm is simulated using the high-fidelity simulator SOWFA. The optimization is performed with a modifier adaptation scheme based on Gaussian processes. Modifier adaptation corrects for the mismatch between plant and model and helps to converge to the actual plan optimum. In the case study the modifier adaptation approach is compared with the Bayesian optimization approach. Moreover, the use of two different covariance functions in the Gaussian process regression is discussed. Practical recommendations concerning the data preparation and application of the approach are given. It is shown that both the modifier adaptation and the Bayesian optimization approach can improve the power production with overall smaller yaw misalignments in comparison to the Gaussian wake model.

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“…The mitigation of loads on the drivetrain of the wind turbine and an increase in power capture at the turbine level are addressed in the literature on turbine control by optimizing the generator torque, blade pitch and yaw steering controls (as shown in, for example, van Binsbergen et al, 2020, andFleming et al, 2013). Optimized wind power plant management by considering the influence of wakes was recently studied by Andersson and Imsland (2020).…”

Section: Drivetrain and Plant Considerationmentioning

confidence: 99%

Wind turbine drivetrains: state-of-the-art technologies and future development trends

et al. 2022

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Abstract. This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the system that converts kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning and recycling. Offshore development and digitalization are also a focal point in this study. Drivetrain in this context includes the whole power conversion system: main bearing, shafts, gearbox, generator and power converter. The main aim of this article is to review the drivetrain technology development as well as to identify future challenges and research gaps. The main challenges in drivetrain research identified in this paper include drivetrain dynamic responses in large or floating turbines, aerodynamic and farm control effects, use of rare-earth material in generators, improving reliability through prognostics, and use of advances in digitalization. These challenges illustrate the multidisciplinary aspect of wind turbine drivetrains, which emphasizes the need for more interdisciplinary research and collaboration.

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

Cited by 17 publications

References 46 publications

Multifidelity Multiobjective Optimization for Wake Steering Strategies

Multifidelity Multiobjective Optimization for Wake Steering Strategies

Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data

Wind turbine drivetrains: state-of-the-art technologies and future development trends

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