An abnormality determination method for a wind power generation device includes: a measurement step (step S1) of measuring sound emitted by the wind power generation device and recording acoustic data; an analysis step (step S2) of performing a spectrogram analysis on the acoustic data recorded in the measurement step, on a frequency axis and in a temporal axis space as a temporal change in a frequency characteristic by using the short-time Fourier transform or the wavelet transform; a detection step (step S3) of detecting, from the analysis result in the analysis step, a signal component emitted from an abnormal portion of the wind power generation device in a time corresponding to rotation of the wind power generation device; and a determination step (step S5) of determining that the wind power generation device is abnormal when the signal component detected in the detection step is greater than or equal to a certain threshold value.

The present application discloses a blade fault diagnosis method, apparatus and system, and a storage medium. the method includes: acquiring a blade rotation audio collected by an audio collection device during operation of a wind turbine generator system; preprocessing the blade rotation audio based on a wind noise filtering algorithm to obtain a blade rotation audio filtered out of wind noise; dividing the blade rotation audio filtered out of wind noise to obtain audio segments corresponding to blades of the wind turbine generator system respectively; diagnosing, based on the audio segments, whether the blades each corresponding to one of the audio segments are faulty. The present application can diagnose whether a corresponding blade is faulty respectively according to the audio segments of different blades, which improves the accuracy of the diagnosis results.

A method for monitoring a machine state of a machine system, in particular a wind power plant, may include the steps of providing a time series of measured natural vibration spectra of the machine system, detecting a deformation parameter in at least one monitoring time interval, wherein the deformation parameter is characteristic of a deviation of the measured natural vibration spectra from a reference natural vibration spectrum of at least one reference machine system, detecting a noise parameter at the at least one monitoring time interval, wherein the noise parameter is characteristic of a noise of the measured natural vibration spectra, and determining the machine state from the deformation parameter and the noise parameter. A monitoring apparatus for monitoring a machine state of a machine system, in particular a wind power plant, is also described.

An abnormality determination method for a wind power generation device includes: a measurement step (step S1) of measuring sound emitted by the wind power generation device and recording acoustic data; an analysis step (step S2) of performing a spectrogram analysis on the acoustic data recorded in the measurement step, on a frequency axis and in a temporal axis space as a temporal change in a frequency characteristic by using the short-time Fourier transform or the wavelet transform; a detection step (step S3) of detecting, from the analysis result in the analysis step, a signal component emitted from an abnormal portion of the wind power generation device in a time corresponding to rotation of the wind power generation device; and a determination step (step S5) of determining that the wind power generation device is abnormal when the signal component detected in the detection step is greater than or equal to a certain threshold value.

A wind turbine blade, comprising a sensor device for detecting properties of flow-induced noise produced by the blade and an actuator device for emitting an anti-noise signal for at least partially cancelling out the flow-induced noise, wherein the actuator device comprises an aerodynamically shaped housing attached to an outer surface of the blade. The aerodynamically shaped housing of the actuator device reduces a deterioration the aerodynamic efficiency of the blade. Further, the generation of turbulences at sharp edges of the housing is avoided.

The present application discloses a blade fault diagnosis method, apparatus and system, and a storage medium, the method includes: acquiring a blade rotation audio collected by an audio collection device during operation of a wind turbine generator system; preprocessing the blade rotation audio based on a wind noise filtering algorithm to obtain a blade rotation audio filtered out of wind noise; dividing the blade rotation audio filtered out of wind noise to obtain audio segments corresponding to blades of the wind turbine generator system respectively; diagnosing, based on the audio segments, whether the blades each corresponding to one of the audio segments are faulty. The present application can diagnose whether a corresponding blade is faulty respectively according to the audio segments of different blades, which improves the accuracy of the diagnosis results.