Systems, methods, and computer program products for monitoring occurring rotor imbalances. A dynamic characteristic sensor determines the value of a dynamic characteristic of a wind turbine, e.g., of a nacelle thereof, such that the dynamic characteristic includes a component aligned with a rotor plane of the rotor. The dynamic characteristic is sampled when the rotor of the wind turbine is at each of a plurality of azimuth angles (?.sub.n) to produce a sequence of dynamic values (a(?)). An azimuth-domain transform is applied to the sequence of dynamic values (a(?)) to generate at least one inverse-angle component (A(?.sub.k)). Rotor imbalances are then detected based on the inverse-angle component (A(?.sub.k)), such as by comparing a value of the inverse-angle component (A(?.sub.k)) to a threshold, and the rotation of the rotor is stopped.

The invention provides a method of identifying recurrent free-flow wind disturbances associated with a wind turbine. The method comprises monitoring a signal indicative of a parameter associated with operation of the wind turbine, determining an expected signal of the parameter based on the monitored signal, determining a difference between values of the monitored signal and the determined expected signal, and correlating the determined differences with yaw position of a nacelle of the wind turbine. The method includes determining, based on the correlated differences, unexpected values of the parameter for different yaw positions, and identifying, based on a frequency of occurrence of the determined unexpected values, a recurrent free-flow wind disturbance associated with a yaw position of the nacelle.

The invention provides a method of identifying recurrent free-flow wind disturbances associated with a wind turbine. The method comprises monitoring a signal indicative of a parameter associated with operation of the wind turbine, determining an expected signal of the parameter based on the monitored signal, determining a difference between values of the monitored signal and the determined expected signal, and correlating the determined differences with yaw position of a nacelle of the wind turbine. The method includes determining, based on the correlated differences, unexpected values of the parameter for different yaw positions, and identifying, based on a frequency of occurrence of the determined unexpected values, a recurrent free-flow wind disturbance associated with a yaw position of the nacelle.

The disclosure relates to a wind turbine blade inspection system and method based on an unmanned aerial vehicle. The system includes: a collection module, used for collecting blade data and surrounding environment data of blades to be inspected, determining feature inspection points of the unmanned aerial vehicle according to the blade data and the surrounding environment data, and generating inspection paths; an inspection module, used for shooting corresponding blade at the feature inspection points according to the inspection paths to obtain a first inspection image and a second inspection image; an analysis module, used for receiving the first inspection image and the second inspection image, analyzing the first inspection image and the second inspection image to obtain a health state of the corresponding blade, and making a maintenance plan according to the health state of each of the blades.

The disclosure relates to a wind turbine blade inspection system and method based on an unmanned aerial vehicle. The system includes: a collection module, used for collecting blade data and surrounding environment data of blades to be inspected, determining feature inspection points of the unmanned aerial vehicle according to the blade data and the surrounding environment data, and generating inspection paths; an inspection module, used for shooting corresponding blade at the feature inspection points according to the inspection paths to obtain a first inspection image and a second inspection image; an analysis module, used for receiving the first inspection image and the second inspection image, analyzing the first inspection image and the second inspection image to obtain a health state of the corresponding blade, and making a maintenance plan according to the health state of each of the blades.

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 method and a device for fault diagnosis of wind turbine pitch bearing based on neural network, the method includes: measuring the signal strength at different points of a sensor and different rolling angles, determining an optimal measurement rolling angle of a blade and sensor point arrangement, blocking the blade at the optimal rolling angle to collect the pitch vibration data, further processing the collected vibration data into a dataset, constructing a neural network model, using the collected dataset to train the network, and deploying the trained network to PLC for real-time dynamic monitoring of the wind turbine; the device includes the vibration acceleration sensors, a vibration data acquisition card and a programmable logic controller (PLC). The present disclosure can realize the fast, real-time and accurate monitoring of the health status of the pitch bearing.

A system and method are provided for controlling a wind turbine. Accordingly, a component of the wind turbine is monitored by at least one sensor of a sensor system. An output is received from the sensor system which indicates a fault with the sensor. A fault accommodation response is generated by a fault module. The fault accommodation response includes an accommodation signal which replaces the output signal of the faulty sensor.

A method for estimating future risk of failure of a component of an industrial asset. The method includes receiving a plurality of different types of data associated with the industrial asset or a fleet of industrial assets. The plurality of different types of data includes, at least, reliability data (such as time-to-event data). The method also includes generating a failure prediction model for the component based on the reliability data and available time-series measurements. Further, the method includes applying the failure prediction model to the different types of data based on the types of data available in the received data. The applied failure prediction model includes one of a default model, a conditional survival model, or a joint conditional survival model. Thus, the method includes estimating, via the failure prediction model, the future risk of failure of the industrial asset and implementing a control action as needed.

An online testing and diagnosis method for vibration characteristics of blades of wind turbine is disclosed. Steps of testing and diagnosing blade vibration comprises: S1: installing vibration sensors at key positions of a blade, designing an adaptive data acquisition strategy, and automatically adjusting a sampling rate according to a vibration amplitude and environmental changes monitored in a real time; S2: extracting key features reflecting health status of the blade from massive data, and evaluating an impact of wind speed, temperature, and environmental factors on vibration characteristics; S3: designing a customized deep learning model for damages of the blade of a wind turbine, extracting a time sequence data and a vibration signal, identifying a damage among different types of damages and evaluating a damage degree; and S4: automatically adjusting a warning threshold based on a real-time data stream and a historical trend, and drafting a preventive maintenance plan.