Fault diagnosis in wind power system based on intelligent techniques
Joint Authors
Abd al-Amir, Lubna A.
Jalal, Kanan Ali
Source
Engineering and Technology Journal
Issue
Vol. 36, Issue 11A (30 Nov. 2018), pp.1201-1207, 7 p.
Publisher
Publication Date
2018-11-30
Country of Publication
Iraq
No. of Pages
7
Main Subjects
Engineering & Technology Sciences (Multidisciplinary)
Abstract EN
Wind energy is one of the most important sources as well as being environmentally friendly and sustainable.
In this paper, different types of faults of Doubly-Fed Induction Generator (DFIG) have been studied based on Artificial Neural Network (ANN), Particle Swarm Optimization (PSO) and Field Programmable Gate Array.
To simulate the wind generators model MATLAB/Simulink program has been used.
Artificial Neural Network (ANN) is trained for detection the faults and (PSO) technique is used to get the best weights.
After the training process, the network was transformed into a Simulink program and then converted into the Very High Speed Description Language (VHDL) for downloading on the (FPGA) card, which in turn is used to detect and diagnosis the presence of faults where it can be re-programmed with high response and accuracy.
American Psychological Association (APA)
Jalal, Kanan Ali& Abd al-Amir, Lubna A.. 2018. Fault diagnosis in wind power system based on intelligent techniques. Engineering and Technology Journal،Vol. 36, no. 11A, pp.1201-1207.
https://search.emarefa.net/detail/BIM-832639
Modern Language Association (MLA)
Jalal, Kanan Ali& Abd al-Amir, Lubna A.. Fault diagnosis in wind power system based on intelligent techniques. Engineering and Technology Journal Vol. 36, no. 11A (2018), pp.1201-1207.
https://search.emarefa.net/detail/BIM-832639
American Medical Association (AMA)
Jalal, Kanan Ali& Abd al-Amir, Lubna A.. Fault diagnosis in wind power system based on intelligent techniques. Engineering and Technology Journal. 2018. Vol. 36, no. 11A, pp.1201-1207.
https://search.emarefa.net/detail/BIM-832639
Data Type
Journal Articles
Language
English
Record ID
BIM-832639