Abstract: A method and apparatus for inspecting a wind turbine blade. The method includes: acquiring a sound signal generated by an impingement of wind on the wind turbine blade using a sound acquisition device; generating a frequency spectrogram corresponding to the sound signal; and obtaining a damage recognition result of the wind turbine blade from the frequency spectrogram by performing image recognition on the frequency spectrogram based on a damage recognition model. With the method, a damage type of the wind turbine blade is accurately recognized based on the frequency spectrogram without manual inspection. Therefore, human resources are saved. In addition, the health state of the wind turbine blade can be monitored in real time.

Abstract: Disclosed are a method and apparatus for detecting a fault, and a method and apparatus for training a model. The method includes: acquiring characteristic data and actual temperature of a first wind turbine among n wind turbines, wherein the characteristic data of the first wind turbine is intended to characterize a working state of the first wind turbine, and n is an integer greater than 1; acquiring a prediction temperature set by inputting the characteristic data of the first wind turbine into a temperature prediction model corresponding to each of the n wind turbines; and detecting, based on the predicted temperature set and the actual temperature of the first wind turbine, whether the first wind turbine encounters a fault.

Abstract: Disclosed are a method and apparatus for detecting a yaw-to-wind abnormality. The method includes: acquiring a wind direction deviation angle within a specified time period; calculating a power performance index, wherein the power performance index is a dimensionless number used to characterize power generation performance of a wind turbine; determining an optimal wind direction deviation angle based on the power performance index; determining a current wind direction deviation angle according to probability distribution of the wind direction deviation angle; and if a difference between the optimal wind direction deviation angle and the current wind direction deviation angle is greater than a preset threshold, determining that a yaw-to-wind abnormality is detected.

Abstract: A method and apparatus for inspecting a wind turbine blade. The method includes: acquiring a sound signal generated by an impingement of wind on the wind turbine blade using a sound acquisition device; generating a frequency spectrogram corresponding to the sound signal; and obtaining a damage recognition result of the wind turbine blade from the frequency spectrogram by performing image recognition on the frequency spectrogram based on a damage recognition model. With the method, a damage type of the wind turbine blade is accurately recognized based on the frequency spectrogram without manual inspection. Therefore, human resources are saved. In addition, the health state of the wind turbine blade can be monitored in real time.

Abstract: Disclosed are a method and apparatus for detecting a yaw-to-wind abnormality. The method includes: acquiring a wind direction deviation angle within a specified time period; calculating a power performance index, wherein the power performance index is a dimensionless number used to characterize power generation performance of a wind turbine; determining an optimal wind direction deviation angle based on the power performance index; determining a current wind direction deviation angle according to probability distribution of the wind direction deviation angle; and if a difference between the optimal wind direction deviation angle and the current wind direction deviation angle is greater than a preset threshold, determining that a yaw-to-wind abnormality is detected.

Abstract: Disclosed are a method and apparatus for detecting a fault, and a method and apparatus for training a model. The method includes: acquiring characteristic data and actual temperature of a first wind turbine among n wind turbines, wherein the characteristic data of the first wind turbine is intended to characterize a working state of the first wind turbine, and n is an integer greater than 1; acquiring a prediction temperature set by inputting the characteristic data of the first wind turbine into a temperature prediction model corresponding to each of the n wind turbines; and detecting, based on the predicted temperature set and the actual temperature of the first wind turbine, whether the first wind turbine encounters a fault.