| Summary: | This paper presents a fault diagnosis (FDD) approach based on a Takagi-Sugeno Unknown Input Observer (TS-UIO) that allows for the estimation of the states of an active pitch system for a studied wind turbine even in the presence of unknown interference factors. A scheme for FDD is proposed based on the residual evaluation between the non-linear model of the active pitch system and the Takagi-Sugeno unknown input observer proposed for the detection and isolation of faults in sensors with measurable premise variables. The proposed TS-UIO State Observer is resilient to disturbances and measurement noise due to its unique feature of decoupling unknown inputs, interruptions, or undefined factors that affect the behavior of the system under study. This study investigates the effect of load-induced stress on the mechanical blades of a wind turbine, caused by the wind force considered as an unknown disturbance or input to the system given its dependence on weather conditions. The proposed FDD algorithm includes Linear Matrix Inequalities (LMI) ensuring the estimation error dynamics approximates to zero. Successful implementation tests are demonstrated in an active pitch system with reference parameters based on a wind turbine model. The review outlines traditional FDD approaches, including those based on nonlinear models, as well as relatively new methods based on linear sector conditions. Special attention is given to Takagi-Sugeno (TS) methods.
|
|---|