Engineering topics

Johnson, Kathryn E

Publications and source records attributed to Johnson, Kathryn E.

3 records

DOE OSTI2019

A Framework for Autonomous Wind Farms: Distributed Optimization for Wind

A presentation from NREL Researcher Jennifer King on Distributed Optimization for Wind as part of the Innovative Optimization and Control Methods for Highly Distributed Autonomous Systems workshop at NREL. The workshop brought together experts in the field of distributed optimization and control to exchange ideas on state-of-the-art control and optimization strategies as well as get feedback on work being done at NREL.

17 WIND ENERGY↗

DOE OSTI2019

Short-Term Forecasting Across a Network for the Autonomous Wind Farm

In an autonomous wind farm, turbines will use information from nearby turbines to achieve wind farm-level objectives such as optimizing the overall performance of a wind farm, ensuring resiliency when other sensors fail, and adapting to changing local conditions. In this paper, the wind farm can be modeled as a network within which turbines (nodes) share information across designated communication channels, with a focus on turbines at the outside of the wind farm capturing local effects and sharing that information with downstream turbines. Understanding of varied inflow conditions can be especially important in complex terrain. This information can be used to monitor turbines, self-organize turbines into groups, and predict the power performance of a wind farm. In particular, this paper describes an autonomous wind farm that incorporates information from local sensors in real time to predict wind speed and wind direction at each turbine over a short-term horizon. Results indicate that the estimate of wind direction can be used to improve the knowledge of the wind speed and direction over the persistence method on a 10-15-minute time horizon. These short-term forecasts can also be used to facilitate advanced control methods such as feedforward control within a wind farm.

17 WIND ENERGY↗

DOE OSTI2019

Flow Control Leveraging Downwind Rotors for Improved Wind Power Plant Operation

Controlling the air flow within wind power plants has the potential to improve plant performance and is an active area of research in the wind energy control community. In order to develop, test, and tune wind power plant controllers efficiently, an accurate engineering model of the turbine wake dynamics is required. Two elements of flow control are wake steering via yaw and tilt of a turbine. When a turbine is yawed or tilted away from the incoming wind field, the wake shape is changed. This is largely due to shed vortices that produce a curled wake. In this work, the well-known wake engineering model FLOw Redirection and Induction in Steady State (FLORIS) wake engineering model is enhanced to include these curled wake effects due to tilt. Since decay of these vortices has not been previously captured in an engineering model, the authors describe how vortices with decay have been added to FLORIS and how the updated model has been used to study the effects due to tilt in the wake. Results are demonstrated and compared to high-fidelity large-eddy simulations. Potential wind power plant performance gains due to flow control using tilt are investigated across different wind conditions and sites. Preliminary results show power gains by using tilt to implement flow control in a variety of wind distributions and tilt values.

17 WIND ENERGY↗