Open AccessData‐driven subspace‐based adaptive fault detection for solar power generation systems
Author(s) -
Chen Jianmin,
Yang Fuwen
Publication year - 2013
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.2012.0932
Subject(s) - fault detection and isolation , residual , subspace topology , fault (geology) , computer science , control theory (sociology) , filter (signal processing) , reliability (semiconductor) , signal (programming language) , electric power system , power (physics) , real time computing , algorithm , artificial intelligence , computer vision , physics , quantum mechanics , seismology , actuator , geology , programming language , control (management)
Data‐driven fault detection has emerged as one of the most prevalent topics in the fault diagnosis. In this study, a novel data‐driven subspace‐based fault‐detection scheme is proposed to handle the problem of fault detection with system uncertainties in solar power generation systems. A data‐driven subspace‐based predictor is developed by using the input–output measurements. The residual signal is generated from the predictive error of the predictor and a fault‐detection filter that is designed to diminish the influence of system uncertainties. An adaptive algorithm is developed for updating the fault‐detection filter. Faults can be detected by comparing the evaluated residual signal with a threshold. The reliability of the designed fault‐detection scheme is verified in three cases in a solar power generation system.