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 wake redirection


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.
Fault detection


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.
Feedforward control
Feedforward control