Comparative study between two artificial intelligence techniques (NN and SVM) applied in fault diagnosis of Wind Turbine


  • Farid Tafinine



Bearing Fault, Wind Turbine, Artificial Intelligence, Classification, NN, SVM


This paper addresses the application of artificial intelligence (AI) techniques for the classification of faults in wind turbine. Wind Energy Conversion Systems have become a focal point in the research of renewable energy sources. Reliability of wind turbine is critical to extract this maximum amount of energy from the wind. Many condition monitoring techniques that are based on steady-state analysis are being applied to wind generators. Bearing faults in this generator causes mechanical vibrations and the variations in the air gap density. The air gap flux density is modulated and currents are generated at different frequencies related to the mechanical vibrations. Characteristic bearing frequencies are associated with the physical construction of bearing and its failure mode. Classical spectral analysis using the Fourier transform was used to detect different bearing failure modes. The magnitudes of the frequencies components formed by the bearing defect are small compared to the rest of the current spectrum. This large difference in magnitude makes difficult the detection and classification of bearing faults. In this paper, we show that the utilization of different approaches of artificial intelligence such as neuronal network (NN) and support vector machines (SVM) can be discriminate different failure modes and gives a good basis for an automatic and non-invasive condition monitoring for wind turbine.


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How to Cite

Tafinine, F. (2024). Comparative study between two artificial intelligence techniques (NN and SVM) applied in fault diagnosis of Wind Turbine. South Florida Journal of Development, 5(5), e3896.