Wind Turbine Fault Diagnosis Method Based On Microphone Array Is Studied

As the emergence of energy shortage and environmental pollution problems etc,human beings are in urgent need of a new clean, green energy,wind power gradually become the focus of attention all over the world, therefore, the resulting developing wind power industry.Therefore, our country set up long-term planning in detail, the plan explicitly proposed to vigorously carry out the research work of large wind power equipment, so the wind power industry has been rapid development.In the wind power industry rapid development, the repair and the maintenance of wind turbine huge funds damaged the economic interests of the entire wind farm, and hindered the fast developing of the whole industry.Therefore, how to quickly and efficiently to wind turbine fault detection and diagnosis, and reduce the cost of repair and maintenance, is the whole industry facing a problem.This paper on the study of fault detection and diagnosis methods of wind turbines,analysis of the current research status at home and abroad, and put forward the application of microphone array to realize the remote and non-contact method for wind turbine fault diagnosis.In this paper, in order to reduce the workload, to speed up the paper completed progress, only in gearbox of wind turbine as the research object.The reason chosen the gearbox is also given, it is the high rates of failure parts, and the failure of maintenance downtime is the longest and economic costs is the highest.In this paper, the method of fault diagnosis: First of all,we decorate the microphone array at the bottom of the wind turbine,it real-timely gather sound signal of gearbox,and get sound signal spectrogram.And then by means of wavelet packet analysis method will extract feature of spectrogram of each wind turbine fault,and establish eigenvector.The last eigenvector as the data samples train the SVM model and fault identification.This article also according to the fault diagnosis method to develop basic Labview platform of fault diagnosis system, and carries on the field test in a wind field, the results show that the system is effective.