Sample Projects
In this page, I describe a subset of my recent and ongoing projects. This page is not intended to be all-inclusive and should be considered a work-in-progress. Projects include- Wind farm control for maximizing energy capture
- Wind turbine fault detection and protection
- Lidar-based control of wind turbines
Wind farm control
When turbines are grouped together in farms, areodynamic interaction between turbines
results in energy loss and increased turbulence compared to the same number of turbines in the same wind
conditions without other turbines nearby. In this research, we have used the high-fidelity
modeling tool SOWFA to examine
methods for redirecting turbine wakes to improve performance. Funding provided by DOE.
Figure from P. Fleming, P. Gebraad, S. Lee, J.W. vanWingerden, K. Johnson, M. Churchfield,
J. Michalakes, P. Spalart, and P. Moriarty, "Evaluating techniques for redirecting turbine
wake using SOWFA," to appear in Renewable Energy, http://dx.doi.org/10.1016/j.renene.2014.02.015.
Wind turbine fault detection and protection
Fault detection and protection schemes are necessary to ensure safe turbine operation,
and this project will help to determine what strategies and sensors are necessary.
Pictured are a block diagram showing the hierarchy of fault detection compared to
operational and subsystem control (top) and a "before" and "after" set of data from a
detected fault in the blade pitch system. Before the fault was detected, a pitch gear
box oil problem was causing the pitch actuator to respond slowly even at its current
limit. After the fault was detected and corrected, the blade pitch was better able to
follow its commanded pitch angle.
Lidar-based wind turbine control
Advanced sensor technology such as Lidar (Light Detection and Ranging) enable more
advanced wind turbine control, including feedforward control for load reduction.
Many different strategies are possible, including the FX-RLS feedforward technique
for which these results are shown. The Lidar measures the wind speed at one or more
points in front of the turbine and the FX-RLS algorithm adapts the coefficients in a
FIR filter in the feedforward path. In the results shown, the four feedforward
control cases (with various parameters) mitigate overspeed caused by a wind gust as
compared to the PI feedback only case, with load reduction also apparent in several
components. Funding provided by DOE/NREL.
