Modeling and maximizing power in wind turbine arrays

Wind farms can increase annual energy production (AEP) with advanced control algorithms by coordinating the set points of individual turbine controllers across the farm. However, it remains a significant challenge to achieve performance improvements in practice because of the difficulty of utilizing models that capture pertinent complex aerodynamic phenomena while remaining amenable to control design. We formulate a multi-stage stochastic optimal control problem for wind farm power maximization and show that it can be solved analytically via dynamic programming. In particular, our model incorporates state-and input-dependent multiplicative noise whose distributions capture stochastic wind fluctuations. The optimal control policies and value functions explicitly incorporate the moments of these distributions, establishing a connection between wind flow data and optimal feedback control. We illustrate the results with numerical experiments that demonstrate the advantages of our approach over existing methods based on deterministic models.

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“…, u * N −1 with Q computed via (4b). We substitute the optimal power (17) into (15) and come to (16), which concludes the proof.…”

Section: Stochastic Optimal Control For Wind Power Maximizationmentioning

confidence: 85%

Stochastic Dynamic Programming for Wind Farm Power Maximization

Guo

Rotea

Summers³

2019

Preprint

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“…Initially, control strategies designed for wind farms were mainly based on aggregate models, which represent those arrays as a large equivalent wind turbine [1][2][3][4]. The drawback of this modeling approach relies on the lack of interactions among turbines caused by the wakes.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Decentralized Algorithm for Wind Farm Control with Population-Games Assistance

Barreiro-Gómez¹,

Ocampo‐Martínez

Bianchi

et al. 2019

Energies

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In wind farms, the interaction between turbines that operate close by experience some problems in terms of their power generation. Wakes caused by upstream turbines are mainly responsible of these interactions, and the phenomena involved in this case is complex especially when the number of turbines is high. In order to deal with these issues, there is a need to develop control strategies that maximize the energy captured from a wind farm. In this work, an algorithm that uses multiple estimated gradients based on measurements that are classified by using a simple distributed population-games-based algorithm is proposed. The update in the decision variables is computed by making a superposition of the estimated gradients together with the classification of the measurements. In order to maximize the energy captured and maintain the individual power generation, several constraints are considered in the proposed algorithm. Basically, the proposed control scheme reduces the communications needed, which increases the reliability of the wind farm operation. The control scheme is validated in simulation in a benchmark corresponding to the Horns Rev wind farm.

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