Open AccessA robust fault detection and isolation filter for the pitch system of a variable speed wind turbine
Author(s) -
Noshirvani Gholamreza,
Askari Javad,
Fekih Afef
Publication year - 2018
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/etep.2625
Subject(s) - fault detection and isolation , kalman filter , turbine , control theory (sociology) , matlab , fault (geology) , wind power , engineering , reliability (semiconductor) , isolation (microbiology) , process (computing) , filter (signal processing) , computer science , actuator , power (physics) , artificial intelligence , physics , control (management) , electrical engineering , microbiology and biotechnology , seismology , biology , geology , mechanical engineering , quantum mechanics , operating system
Summary Prompt detection and isolation of faults is crucial in improving the reliability and safety of Wind Turbines (WT) and extending their lifespan. This paper proposes a model‐based robust Fault Detection and Isolation (FDI) scheme for the blade pitch positions of a WT. The approach is able to detect both sensor and actuator faults in a noisy environment. An Unscented Kalman Filter (UKF), with a newly proposed de‐correlation approach for process and measurement noises, is developed to simultaneously estimate the states and parameters of the WT's pitch position. The effectiveness of the proposed approach was assessed using actual data derived from a 2.5 MW wind turbine and simulated using Matlab/Simulink environment. Besides its accuracy and effectiveness in detecting faults, the proposed FDI algorithm considers admissible parameter uncertainties as well as the correlation between process and measurement noises.