FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine
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- 作者：Amel Adounia ; amel.enig@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Dhia Chariaga ; dhia.chariag@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Demba Diallob ; Demba.Diallo@lgep.supelec.fr" class="auth_mail" title="E-mail the corresponding author ; Mouna Ben Hameda ; benhamed2209@yahoo.fr" class="auth_mail" title="E-mail the corresponding author ; Lassaâ ; d Sbitaa ; Lassaad.Sbita@enig.rnu.tn" class="auth_mail" title="E-mail the corresponding author
- 关键词：Wind Turbine ; Low Voltage Ride Through ; Fault Detection and Identification ; Electrical grid ; Artificial Neural Network
- 刊名：ISA Transactions
- 出版年：2016
- 出版时间：September 2016
- 年：2016
- 卷：64
- 期：Complete
- 页码：353-364
- 全文大小：3709 K
文摘
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The Faults Detection and Identification using indicators voltages analysis.
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The proposed technique based on Artificial Neural Network.
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The proposed technique is applied on the wind turbine system connected to the electrical grid.
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We improve the rapidity detection, robustness and immunity identification to the disturbances.