title: Robust fault detection of wind energy conversion systems based on dynamic neural networks.
abstract: Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.
authors.affiliation: School of Electrical and Robotic Engineering, University of Shahrood, P.O. Box 3619995161, Shahrood, Iran.
authors.forename: Nasser
authors.initials: N
authors.lastname: Talebi
authors.affiliation: School of Electrical and Robotic Engineering, University of Shahrood, P.O. Box 3619995161, Shahrood, Iran.
authors.forename: Mohammad Ali
authors.initials: MA
authors.lastname: Sadrnia
authors.affiliation: School of Electrical and Robotic Engineering, University of Shahrood, P.O. Box 3619995161, Shahrood, Iran.
authors.forename: Ahmad
authors.initials: A
authors.lastname: Darabi
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004735:Energy Transfer; D004736:Energy-Generating Resources; D006801:Humans; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted; D014919:Wind