Condition monitoring of the wind turbine generator slip ring

The huge proliferation of wind farms across the world has arisen as an alternative to the traditional power generation and as a result of economic issues which necessitate monitoring systems in order to optimize availability and profits too. Equipments inside a wind turbine are subject to failures which, most of times lead to long downtime periods. When wind turbine is not running due to a failure, no profits are added and operation and maintenance costs increases. The development of advanced techniques to detect the onset of mechanical and electrical faults in wind turbines at a sufficiently early stage is very important for maintenance actions. Neural networks can be used to detect failures in some equipments of wind turbines, but to use them is necessary to create a model to the equipment under surveillance. The training of the neural network represents the big handicap of the developed method that will be presented here. However, after solving this problem, results are very interesting, and failures can be detected with several months in advance.