Title :
Dip fault detection and identification for wind conversion energy system
Author :
Adouni, Amel ; Diallo, Demba ; Sbita, Lassaad
Author_Institution :
Lab. Syst. photovoltaique, eolien et geothermique, Gabes, Tunisia
Abstract :
This paper addresses the problem of detecting voltage dips in Wind Turbine Generator connected to electrical grid. A procedure based on analysis of voltage indicators is proposed. It used the artificial neural network in order to extract the features (magnitudes and angle of each phase). The method is tested in simulation and the results approved its efficiency and rapidity. It could not only detect the dip fault but also identify the type of fault.
Keywords :
fault diagnosis; feature extraction; neural nets; power engineering computing; power grids; turbogenerators; wind turbines; artificial neural network; electrical grid; feature extraction; voltage dip fault detection; voltage dip fault identification; voltage indicator; wind conversion energy system; wind turbine generator; Artificial neural networks; Fault diagnosis; Generators; Stators; Voltage fluctuations; Wind energy; Wind turbines; Dip voltage; Wind turbine generator; detection; identification; neural network;
Conference_Titel :
Industrial Technology (ICIT), 2015 IEEE International Conference on
Conference_Location :
Seville
DOI :
10.1109/ICIT.2015.7125576
