This project introduces a fault detection and isolation (FDI) system for wind farms based on SCADA (Supervisory Control and Data
Acquisition) data. Instead of the traditional approach of monitoring each turbine individually, this project proposes a model-based
FDI system that exploits correlations between measurements associated to neighboring turbines in order to detect and localize
possible faults. This feature could enhance the performance of the FDI system, in terms of its capability for early detection of
faults and the reduction of false detection and missed detection occurrences.
 The main challenge is to account for the difference in
wind conditions that each turbine is subject to, which can significantly modify the relative turbine behaviors. To this end,
linear parameter-varying 2(LPV) models will be used to represent the relative dynamics between the turbines as a function of wind
speed and wind direction, notably. These LPV models will be identified in the frequency domain since the modeling can be done in a
user defined frequency band. Also, the continuous-time framework will be chosen because models related with the physics of the system
can facilitate the design of the FDI system.
 The project will be carried out as follows. First, a LPV model for monitoring a
single wind turbine will be developed with focus on the pitch system. Second, the results obtained are extended for the LPV
modelling of wind farms with focus on the data associated to turbine efficiency and overheating. Third, the obtained models are analyzed
and a FDI system for wind farms is designed. This brings additional challenges to be addressed: how to best construct the LPV
models for enhancing fault detection while managing complexity? How to automatically extract relevant data for the modeling? How to
ensure a systematic design and tuning of the FDI system?
 Finally, the validation on a wind farm simulator and with SCADA data from
an on-shore wind farm made of ten 2.5 MW turbines will be performed.