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DOE OSTI · 3014941

Stochastic Model Predictive Control With Gaussian Wind Direction Preview for Wake Steering

Henry, Aoife [National Laboratory of the Rockies, Golden, CO (United States)]·Sinner, Michael [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:0000000187660711)·Pao, Lucy [University of Colorado Boulder]
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Abstract

This article addresses the problem of wake steering control for wind farms that explicitly consider the tradeoff between farm-level power generation and yaw duty cycle under variable and uncertain wind conditions. A novel stochastic model predictive control (MPC) algorithm is presented, which utilizes a stochastic model of the freestream wind field components in a receding horizon framework to compute optimal yaw set points that maximize the expected value of the farm power while constraining the yaw actuation. Different configurations of the algorithm are evaluated using a steady-state wind farm simulator. The proposed stochastic MPC algorithm can plan control actions over a future prediction horizon based on probabilistic estimates of the incoming wind magnitude and direction.

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17 WIND ENERGYstochastic model predictive control (MPC)wake steeringwind energy

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BibTeXRIS

Henry, Aoife [National Laboratory of the Rockies, Golden, CO (United States)], Sinner, Michael [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:0000000187660711), Pao, Lucy [University of Colorado Boulder]. 2025-12-24. Stochastic Model Predictive Control With Gaussian Wind Direction Preview for Wake Steering. https://doi.org/10.1109/tcst.2025.3639652

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