Early warning study on offshore wind power failure based on vibration monitoring

With the continuous reduction of traditional energy sources, coupled with our country's goal of achieving carbon peak and carbon neutrality in recent years, wind power has been increasingly emphasized in recent years, especially offshore wind power, due to the sufficient and stable wind energy, the installed capacity has risen sharply. However, the working environment of offshore wind turbines is more severe, the structure is more complex, the failure rate is higher, and their operation and maintenance costs are also higher. Therefore, this paper is aimed at the method of offshore wind power fault early warning based on vibration monitoring, the research content is as follows: <br/> <br/> First of all, data processing is carried out to convert the collected vibration monitoring data into RMS Using the average method of adding the window to denoise the generated raw data, and then adding the window Fourier transform of the noise reduction data, the time domain signal is converted into a frequency domain signal to obtain the spectrogram. Finally, Spectrum analysis is carried out to determine the fault location according to the spectral characteristics of the spectrum and combined with the actual verification. According to the experimental results and then combined with the example analysis, a certain early warning strategy, used for the future parts of the turbine may fail to early warning, reducing the cost of operation and maintenance. <br/> <br/> The offshore wind turbine fault diagnosis method proposed in this paper can effectively diagnose a variety of faults in various parts of offshore wind turbines, and make full use of big data to improve diagnostic accuracy.