What question did this study set out to answer?

To develop and validate a physics-based analytical model for predicting wind turbine wake expansion, wake interactions, and power losses in utility-scale wind farms.

September 15, 2016SHILAP Revista de lepidopterologíaOpen Access

Analytical Modeling of Wind Farms: A New Approach for Power Prediction

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Authors

ANAmin NiayifarÉcole Polytechnique Fédérale de Lausanne FPFernando Porté‐AgelÉcole Polytechnique Fédérale de Lausanne

Discussion

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Overview

Analytical modeling study demonstrates accurate power prediction across operating wind farms, indicating that physics-based Gaussian wake modeling improves wind energy optimization.

Key Points

To develop and validate a physics-based analytical model for predicting wind turbine wake expansion, wake interactions, and power losses in utility-scale wind farms.
Extended a single-turbine analytical wake framework enforcing mass and momentum conservation with an assumed self-similar Gaussian velocity deficit.
Modeled wake growth via local streamwise turbulence intensity and calculated multi-turbine wake interactions through velocity deficit superposition.
Validated performance against turbine power measurements and large-eddy simulation data from the Horns Rev offshore wind farm across diverse wind directions.
Demonstrated good agreement with both empirical power output data and large-eddy simulation benchmarks under full-wake and partial-wake conditions.
Yielded substantial improvements in wake field estimation and power loss prediction compared with conventional industrial wind farm wake models.

Cite This Study

Niayifar et al. (2016) studied this question.

synapsesocial.com/papers/69d7e6e4ec32c73b01ae347f https://doi.org/10.3390/en9090741

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September 15, 2016SHILAP Revista de lepidopterologíaOpen Access

Analytical Modeling of Wind Farms: A New Approach for Power Prediction

View Full Paper

Ask AI

Bookmark

Authors

ANAmin NiayifarÉcole Polytechnique Fédérale de Lausanne FPFernando Porté‐AgelÉcole Polytechnique Fédérale de Lausanne

Discussion

Member takes

Overview

Analytical modeling study demonstrates accurate power prediction across operating wind farms, indicating that physics-based Gaussian wake modeling improves wind energy optimization.

Key Points

To develop and validate a physics-based analytical model for predicting wind turbine wake expansion, wake interactions, and power losses in utility-scale wind farms.
Extended a single-turbine analytical wake framework enforcing mass and momentum conservation with an assumed self-similar Gaussian velocity deficit.
Modeled wake growth via local streamwise turbulence intensity and calculated multi-turbine wake interactions through velocity deficit superposition.
Validated performance against turbine power measurements and large-eddy simulation data from the Horns Rev offshore wind farm across diverse wind directions.
Demonstrated good agreement with both empirical power output data and large-eddy simulation benchmarks under full-wake and partial-wake conditions.
Yielded substantial improvements in wake field estimation and power loss prediction compared with conventional industrial wind farm wake models.

Cite This Study

Niayifar et al. (2016) studied this question.

synapsesocial.com/papers/69d7e6e4ec32c73b01ae347f https://doi.org/10.3390/en9090741

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