Ethan Chow

Ethan Chow

Biography

Ethan Chow grew up in the California Bay Area and is a rising junior transferring to
UC San Diego to study electrical engineering. He is researching the joint influence of inflow
conditions and pitch angle on power production of wind turbines with the Howland Lab at MIT
as an MSRP 2026 intern. He spent four years at a California community college to discover
his passion, from robotics to 3D modeling until sustainability workshops turned his interest
in supporting the environment into a career direction. In summer 2025, he interned at SLAC
National Accelerator Laboratory, where he developed molecular dynamics simulations to
model the thermal response of tungsten in fusion environments. This sparked his excitement
about nuclear fusion’s potential. He believes investing in others and the environment is key to
a fulfilling life, and he hopes to pursue a career advancing fusion energy. He enjoys practicing
piano, making glass paintings, and cooking.

Characterizing the joint influence of atmospheric conditions and control setpoints
on wind turbine power production using field measurements
Ethan M. Chow1, Ilan M. L. Upfal2, Alex Clerc3, and Michael F. Howland2
1Department of Engineering, Chabot College
2Department of Civil and Environmental Engineering, Massachusetts Institute of Technology
3Renewable Energy Systems (RES) Group, United Kingdom

As demand for low-cost clean energy increases, improving the efficiency of wind farms is
becoming increasingly important. Currently, the control strategy of wind turbines, including the
rotational speed of the rotor and the pitch angle of the blades, is designed to maximize power
production assuming simplified inflow conditions. However, in realistic atmospheric boundary
layers, wind turbines experience complex inflow conditions in which the wind speed and
direction both change with height, known as wind speed shear and direction shear, respectively.
Recent studies have revealed that turbine power production depends significantly on the inflow
wind profile. Furthermore, current control strategies may no longer maximize power generation
for these realistic inflow conditions. In this project, we seek to understand how the blade pitch
angle affects power production under different complex inflow conditions. We analyze a six
month dataset collected at a commercial wind farm in Northern Ireland. The inflow wind profiles
are characterized using LiDAR measurements to determine the degree of wind speed and
direction shear. The objective of the investigation is to determine how the power-maximizing
pitch angle depends on the inflow wind profile. These findings can inform future turbine control
strategies which adapt to complex inflow conditions, increasing power production.