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.

Provided is a method of monitoring the structural integrity of a supporting structure of a wind turbine, which method includes the steps of determining a fore-aft tower oscillation frequency; determining a side-to-side tower oscillation frequency; computing a working structural indicator value from the fore-aft tower oscillation frequency and the side-to-side tower oscillation frequency; comparing the working structural indicator value to a reference working structural indicator value; and issuing an alarm if the difference between the working structural indicator value and the reference structural indicator value exceeds a predefined threshold. Also provided is a system for monitoring the structural integrity of a supporting structure of a wind turbine, a wind turbine, and a computer program product for carrying out the steps of the inventive method.

The invention relates to a method for monitoring a status of at least one component of a mechanical plant, in particular of a wind turbine, that comprises the following steps: (i) receiving an evaluation signal that is formed based on values of a measurement quantity recorded in the plant, (ii) determining at least one parameter data set on the basis of the evaluation signal as a function of a signal that is representative for a stress of the plant and/or based on a spectral analysis of the evaluation signal, (iii) evaluation of an indicator signal as a function of at least one parameter data set and determining the status of the component of the plant as a function of the indicator signal.

Provided is a method of monitoring the structural integrity of a supporting structure of a wind turbine, which method includes the steps of determining a fore-aft tower oscillation frequency; determining a side-to-side tower oscillation frequency; computing a working structural indicator value from the fore-aft tower oscillation frequency and the side-to-side tower oscillation frequency; comparing the working structural indicator value to a reference working structural indicator value; and issuing an alarm if the difference between the working structural indicator value and the reference structural indicator value exceeds a predefined threshold. Also provided is a system for monitoring the structural integrity of a supporting structure of a wind turbine, a wind turbine, and a computer program product for carrying out the steps of the inventive method.

A method is described for the early identification of material fatigue in drive train components of a wind turbine installation. In particular, a signal, representing revolutions per minute of a wind turbine shaft, is obtained and modulated by the azimuth angle measurement of the turbine blade. This signal is band passed at twice the frequency of rotation and Fourier transformed to extract amplitude values. An alert response can then be triggered when it is determined that there has been a change in a characteristic of the amplitude values such as the amplitude values increasing beyond a multiple of a determined baseline amplitude value.

A benchmark state space model construction method for an offshore wind turbine is provided. The benchmark state space model is constructed by the modal information of the first several orders of the high-order finite element model of the offshore wind turbine. Since the benchmark state space model is only established by the first several orders of the high-order finite element model, the time domain analysis of the offshore wind turbine using the benchmark state space model instead of the high-order finite element model can improve the calculation efficiency and reduce the calculation cost. The benchmark state space model construction method solves the problem of low computational efficiency and high computational cost of time domain analysis of offshore wind turbines using high-order finite element models due to the excessive number of high-order finite element units in existing technologies.

The application relates to a method, in particular a computer-implemented method, for training a machine learning model usable for determining a remaining useful life of a wind turbine, including providing a plurality of operation data sets of a reference wind turbine, providing a plurality of load data sets of the reference wind turbine, wherein a load data set is based on at least one load parameter measured at the reference wind turbine, and generating a plurality of wind turbine training data sets for training a machine learning model by synchronously assigning a respective operation data set with a respective load data set.

A state monitoring apparatus for a mechanical apparatus including a rolling bearing includes: a first acquisition portion configured to acquire vibration information or sound information of the rolling bearing during rotation; a second acquisition portion configured to acquire a rotation speed of the rolling bearing during rotation; a derivation portion configured to derive, according to the rotation speed, a timing at which data is sampled from the vibration information or the sound information such that the number of times of sampling per rotation of the rolling bearing is a predetermined value; and a generation portion configured to generate, based on the timing derived by the derivation portion, monitoring data by sampling data from the vibration information or the sound information.

The invention relates to a method for monitoring a status of at least one component of a mechanical plant, in particular of a wind turbine, that comprises the following steps: (i) receiving an evaluation signal that is formed based on values of a measurement quantity recorded in the plant, (ii) determining at least one parameter data set on the basis of the evaluation signal as a function of a signal that is representative for a stress of the plant and/or based on a spectral analysis of the evaluation signal, (iii) evaluation of an indicator signal as a function of at least one parameter data set and determining the status of the component of the plant as a function of the indicator signal.

The present invention relates to method for estimating energy production (107) from a wind turbine (101) with plurality of blades (102). The method comprises obtaining one or more infrared images (103) of each blade (102) of the wind turbine (101). Further, identifying one or more cross-sectional regions (302) of each of the blade (102) using the one or more infrared images (103) based on a boundary region (301), wherein the boundary region (301) is indicating a transition from a laminar air flow to a turbulent air flow. Furthermore, determining plurality of polar values indicative of an aerodynamic profile for each of the one or more cross-sectional regions (302) based on one or more panel method based techniques and the boundary region (301). Finally, estimating the energy production (107) for the wind turbine (101) based on one or more blade (102)-element momentum (BEM) based techniques using the plurality of polar values.