EEMD-based wind turbine bearing failure detection using the generator stator current homopolar component
By Yassine Amirat, Vincent Choqueuse, Mohamed El Hachemi Benbouzid. 2013
80 citations recorded by the source corpus.
Failure detection has always been a demanding task in the electrical machines community; it has become more challenging in wind energy conversion systems because sustainability and viability of wind farms are highly dependent on the reduction of the operational and maintenance costs. Indeed the most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early detection of the generator health degeneration, facilitating a proactive response, minimizing downtime, and maximizing productivity. This paper provides then an assessment of a failure detection techniques based on the homopolar component of the generator stator current and attempts to highlight the use of the ensemble empirical mode decomposition as a tool for failure detection in wind turbine generators for stationary and non stationary cases.
Subfields: Civil and Structural Engineering, Control and Systems Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/2dc2f07c5a25b0efa37fd214a04cd693787aa994
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In Control and Systems Engineering
In Mechanical Engineering
Citations in the corpus
- Machine learning methods for wind turbine condition monitoring: A review (2019, background): “Applications of EMD-based techniques in fault diagnosis of rotating machines include [82,85 e 88].”