Sidra Shahid

 


 

Research Work

WECS(wind energy conversion systems) are under extensive research now a days. To detect and predict faults in wind turbines by observing stator current signal can make it more efficient and reliable. A high performance signal processing technique is required to fulfill this task and this describes my current area of research.



 

Project Description


"Fault detection and identification in wind turbine farms"


As an alternate source of energy, wind energy is getting more contribution in world power production. To make the wind energy conversion systems compatible with other conventional power sources, it should be reliable and cost effective. Fault detection and isolation (FDI) techniques are used at a wind farm control level to cut down its maintenance costs. In this project the main objective is to develop algorithm for fault detection and isolation at a wind farm level.
The basic concept I am using is, to estimate the next value of the generated output power, speed and pitch angle of wind turbines farm using previous values at a current time cycle and then compare it with the actual output in the next time cycle. On the basis of this comparison and certain threshold level we can detect fault. For this purpose we first write the state space equations for the system model.
I am also developing wind speed estimator because of the high noise factor present in the measured wind speed at hub. The wind speed is used as an input to the FDI system. The estimator is using Kalman Filter approach.

 

Main components of FDI System

Contact Information :


Sidra Shahid
Graduate Researcher
Analog Mixed Signal Group
NUST-SEECS