Stanford Advisors

All Publications

  • Wind Farm Modeling with Interpretable Physics-Informed Machine Learning ENERGIES Howland, M. F., Dabiri, J. O. 2019; 12 (14)

    View details for DOI 10.3390/en12142716

    View details for Web of Science ID 000478999400076

  • Wind farm power optimization through wake steering. Proceedings of the National Academy of Sciences of the United States of America Howland, M. F., Lele, S. K., Dabiri, J. O. 2019


    Global power production increasingly relies on wind farms to supply low-carbon energy. The recent Intergovernmental Panel on Climate Change (IPCC) Special Report predicted that renewable energy production must leap from [Formula: see text] of the global energy mix in 2018 to [Formula: see text] by 2050 to keep global temperatures from rising 1.5°C above preindustrial levels. This increase requires reliable, low-cost energy production. However, wind turbines are often placed in close proximity within wind farms due to land and transmission line constraints, which results in wind farm efficiency degradation of up to [Formula: see text] for wind directions aligned with columns of turbines. To increase wind farm power production, we developed a wake steering control scheme. This approach maximizes the power of a wind farm through yaw misalignment that deflects wakes away from downstream turbines. Optimization was performed with site-specific analytic gradient ascent relying on historical operational data. The protocol was tested in an operational wind farm in Alberta, Canada, resulting in statistically significant ([Formula: see text]) power increases of 7-[Formula: see text] for wind speeds near the site average and wind directions which occur during less than [Formula: see text] of nocturnal operation and 28-[Formula: see text] for low wind speeds in the same wind directions. Wake steering also decreased the variability in the power production of the wind farm by up to [Formula: see text] Although the resulting gains in annual energy production were insignificant at this farm, these statistically significant wake steering results demonstrate the potential to increase the efficiency and predictability of power production through the reduction of wake losses.

    View details for DOI 10.1073/pnas.1903680116

    View details for PubMedID 31262816

  • Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network Cardona, J. L., Howland, M. F., Dabiri, J. O., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019
  • Dependence of small-scale energetics on large scales in turbulent flows JOURNAL OF FLUID MECHANICS Howland, M. F., Yang, X. A. 2018; 852: 641–62
  • Implication of Taylor's hypothesis on measuringflow modulation JOURNAL OF FLUID MECHANICS Yang, X. A., Howland, M. F. 2018; 836: 222–37
  • Influence of the horizontal component of Earth's rotation on wind turbine wakes Howland, M. F., Ghate, A. S., Lele, S. K., IOP IOP PUBLISHING LTD. 2018
  • Measurement of unsteady loading and power output variability in a micro wind farm model in a wind tunnel EXPERIMENTS IN FLUIDS Bossuyt, J., Howland, M. F., Meneveau, C., Meyers, J. 2017; 58 (1)
  • Wake structure in actuator disk models of wind turbines in yaw under uniform inflow conditions JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY Howland, M. F., Bossuyt, J., Martinez-Tossas, L. A., Meyers, J., Meneveau, C. 2016; 8 (4)

    View details for DOI 10.1063/1.4955091

    View details for Web of Science ID 000383874000003