Wind turbine bearing fault diagnosis via doubly-fed induction generator electrical signals

Bearing failures are the leading failure factor in gearboxes and induction generators of any wind turbines. It is highly desired to detect the bearing faults at an early stage and repair or replace the faulted bearing to prevent catastrophic damages of the wind turbine generation system. In this project, we are going to develop advanced diagnostic methods and techniques for the implementation of induction generator electrical signals for bearing diagnosis. The diagnosis of bearing faults via generator electrical signals is a very promising method, because it does not require the installation of any additional sensors, and signals can be easily measured remotely. The accomplishment of the objectives outlined in this proposal will directly impact advancement in knowledge, research, and technology in bearing health condition monitoring in application to wind turbines. The realization of proposed approach will increase the reliability of wind turbines, reduce maintenance costs, and unplanned downtime.

Faculty Supervisor:

Qiao Sun

Student:

Partner:

Shanghai Jiao Tong University

Discipline:

Engineering

Sector:

Agriculture; Education

University:

University of Calgary

Program: