A proposed hybrid machine learning model for fault-type classification and detection in a 10 MW wind farm based on synchrophasor and weather data

Incorporating renewable energy sources, such as wind and solar farms, have posed a challenge to electrical engineers due to their intermittent nature as the time and amount of energy generated depends on the weather. For this reason, grid stability and predicting the behaviour of renewables has been increasingly more important. In this study, we propose to train a machine learning algorithm on weather data, synchrophasor readings (voltage, current, and phase), and turbine monitoring data from a 10 MW wind farm located in PEI to detect and classify faults in their wind turbines. We will use the algorithm to develop a protection plan for their wind turbines based on these results.

Faculty Supervisor:

Hamed Aly

Student:

Partner:

Wind Energy Institute of Canada

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Utilities

University:

Dalhousie University

Program: