Open AccessMarkovian jump system approach for the estimation and adaptive diagnosis of decreased power generation in wind farms
Author(s) -
SalesSetién Ester,
PeñarrochaAlós Ignacio
Publication year - 2019
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2018.6199
Subject(s) - control theory (sociology) , turbine , wind power , robustness (evolution) , jump , electric power system , benchmark (surveying) , fault detection and isolation , context (archaeology) , observer (physics) , computer science , fault (geology) , sensitivity (control systems) , binary decision diagram , power (physics) , engineering , actuator , algorithm , artificial intelligence , electronic engineering , geography , chemistry , biology , paleontology , biochemistry , control (management) , geodesy , quantum mechanics , mechanical engineering , physics , seismology , electrical engineering , gene , geology
In this study, a Markovian jump model of the power generation system of a wind turbine is proposed and the authors present a closed‐loop model‐based observer to estimate the faults related to energy losses. The observer is designed through anH ∞ ‐based optimisation problem that optimally fixes the trade‐off between the observer fault sensitivity and robustness. The fault estimates are then used in data‐based decision mechanisms for achieving fault detection and isolation. The performance of the strategy is then ameliorated in a wind farm (WF) level scheme that uses a bank of the aforementioned observers and decision mechanisms. Finally, the proposed approach is tested using a well‐known benchmark in the context of WF fault diagnosis.