Current/voltage sensor fault detection and isolation in wind energy conversion systems based on power balance

2016 ◽  
Author(s):  
Haibo Li ◽  
Liyan Qu ◽  
Wei Qiao ◽  
Chun Wei
Keyword(s):  
Fault Detection ◽  
Energy Conversion ◽  
Fault Detection And Isolation ◽  
Power Balance ◽  
Sensor Fault ◽  
Wind Energy Conversion ◽  
Wind Energy Conversion Systems ◽  
Current Voltage ◽  
Energy Conversion Systems ◽  
Sensor Fault Detection

scholarly journals Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Nasser Talebi ◽  
Mohammad Ali Sadrnia ◽  
Ahmad Darabi
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Wind Energy ◽  
Energy Conversion ◽  
Neural Model ◽  
Economic Losses ◽  
Normal System ◽  
Wind Energy Conversion ◽  
Wind Energy Conversion Systems ◽  
Energy Conversion Systems

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.

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