Title: A Review on the State of the Art for Failure Diagnosis and Prognosis Techniques for Wind Conversion System.

Issue Number: Vol. 9, No. 2
Year of Publication: Jun - 2019
Page Numbers: 90-112
Authors: Anissia Beainy , Chantal Maatouk, Nazih Moubayed, Fouad Kaddah
Journal Name: International Journal of Digital Information and Wireless Communications (IJDIWC)
- Hong Kong
DOI:  http://dx.doi.org/10.17781/P002606


Due to gradual depletion of fossil energy resources and the increasingly serious issue of environmental pollution, the scale of renewable energy has been increasing. The demand for wind energy has made it a top competitor as a renewable energy resource. But with all this said, Wind Turbines (WT) manifest many faults along the drive train from the blades to the converter. Some are more prominent than others and more frequent. Three types of major failures are common; failure in electrical subsystems, failure in mechanical subsystems and failures in structural subsystems. This paper is a technical state of the art review as part of a research for WT fault classification. The first highlight of this review covers the WTs critical failures as well as detection algorithms. The second highlight will go into the details of the state of the art and future development of prognosis methods and data analysis techniques that are based on well-built algorithms for remaining useful life calculation and predictions. As a conclusion, the optimal choice is a data collection and diagnosis using stator current signatures, analysed by a discrete wavelet transform with feature extraction optimized by a genetic algorithm, like an Artificial Bee Colony.