Date of Award


Degree Name

MS in Mechanical Engineering


Mechanical Engineering


College of Engineering


Patrick Lemieux

Advisor Department

Mechanical Engineering

Advisor College

College of Engineering


Predicting the performance of wind turbines is a key part of the turbine design process and operation, as predictive models play a large role in determining potential power output and efficiency at different operating conditions to help maximize production. On small-scale wind turbines performance models become more complex, as the rotor aerodynamic performance depends not only on the tip speed ratio, but also on the flow Reynolds number. An accurate predictive model that includes this behavior on small-scale wind turbines can be used to find the optimal operating conditions for power output, and is also a critical component of the design of a control system. This project aims to develop such a model for the small-scale wind turbine operated by the Cal Poly Wind Power Research Center, and to use the developed model to redesign the current control system.

The full turbine system model developed in this project for the Cal Poly Wind Turbine includes detailed models of the aerodynamic, mechanical, and electrical subsystems on the turbine based on first- principles physics. Model parameters were determined through a combination of experimental testing and theoretical analysis. The full turbine model was compared against experimental data, showing that estimations from the predictive model matched closely with the true performance of the turbine. Through the model, the turbine was estimated to have a maximum efficiency of 83.63% and to produce a maximum total 𝐢𝑃 of 35.93% at a tip speed ratio around 5. Using the performance information from the model, a new non-linear controller was designed for constant speed, constant tip speed ratio, and maximum power output. The new maximum power controller is predicted to increase the overall power production of the turbine by 17.1% over the course of a year.