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.

Disclosed is a method, performed by a root cause analysis system. The method comprises obtaining operational data associated with operation of a wind turbine system in response to a fault of the wind turbine system. The method comprises determining, based on the operational data, a set of candidate root causes associated with the fault, by applying a machine learning model to the operational data. The machine learning model is configured to classify and/or locate one or more candidate root causes. The method comprises providing, based on the set of candidate root causes, output data indicative of at least one root cause of the fault of the wind turbine system.

Provided is a method for monitoring one or more wind turbines in a wind farm, each wind turbine having a rotor with rotor blades which are rotatable around a rotor axis, wherein one or several times during the operation of the wind farm a process is performed that includes i) obtaining a digital image of the respective rotor blade, the image being a current image taken by a camera looking at the respective rotor blade; ii) determining one or more operation characteristics of the respective rotor blade by processing the image by a trained data driven model, where the image is fed as a digital input to the trained data driven model and the trained data driven model provides the one or more operation characteristics of the respective rotor blade as a digital output.

Provided is a method of determining a wind parameter value for use in a wind turbine control system, the method including (a) receiving a plurality of wind measurement signals, wherein each wind measurement signal is provided by a respective wind sensor among a plurality of wind sensors, (b) determining a set of statistical values based on the wind measurement signals, (c) calculating a weighting factor for each wind measurement signal based on the set of statistical values, and (d) calculating the wind parameter value as a weighted sum by applying the calculated weighting factors to the corresponding wind measurement signals. Further, a corresponding system and a wind turbine with such a system are provided.

A method and device for monitoring operation of a wind power bearing holder. The method includes: performing multi-cluster head assisted tracking at multi-point locations on an operation state of the wind power bearing holder via a plurality of sensing chips preinstalled in the wind power bearing holder to obtain point location tracking information; perform point location-related motion vector correction and prediction on orthogonally covered point location tracking information to obtain a circumferential motion trajectory of the point locations; performing irregular trajectory filtering processing on a current circumferential motion trajectory to obtain an ideal circumferential motion trajectory; and comparing locations of probability centroids between an ideal circumferential spatial region and a predicted circumferential spatial region, to obtain operation monitoring information of the wind power bearing.

A method is for estimating health status of a pitch energy storage unit of a wind turbine. The method includes: providing an equivalent circuit model, ECM, of the storage unit, the ECM including an equivalent series resistor and an electrical energy storage element connected between two terminals; repeatedly sampling a voltage and a corresponding current at the two terminals; determining a voltage change and a current change for a plurality of voltage samples and corresponding current samples; performing an optimization procedure based at least on the ECM, the plurality of the voltage samples, the determined voltage changes, and the determined current changes to obtain an updated resistance of the equivalent series resistor and an updated model parameter of the storage element; and estimating a status of health parameter for the storage unit by comparing at least one of the updated resistance and the updated parameter with a threshold value.

The present inventive concept provides for a method of apparatus static inertia compensation using external robots. The method includes identifying a region of at least one wind turbine experiencing actual or imminent static inertia. A wind speed at the region is identified. The identified wind speed is compared to a predetermined cut-in speed. An external force necessary to overcome the actual or imminent static inertia based on the compared identified wind speed and the predetermined cut-in speed is calculated. The calculated external force necessary to overcome the actual or imminent static inertia using at least one external robot is generated.

A method for controlling a wind turbine includes receiving, via a control system, at least one speed measurement from at least one sensor on a drivetrain of the wind turbine. The method also includes receiving, via the control system, at least one rate gyroscope speed measurement from at least one rate gyroscope on the drivetrain of the wind turbine. The method also includes calculating, via the control system, an offset for the at least one rate gyroscope speed measurement as a function of the at least one speed measurement. The method also includes adjusting, via the control system, the at least one rate gyroscope speed measurement by the offset. The method also includes implementing, via the control system, a control action for the wind turbine based on the adjusted at least one rate gyroscope speed measurement.

Disclosed is an integrated DFOS system and method for enhanced offshore wind turbine monitoring using physics-informed machine learning algorithms that advantageously utilizes existing optical fiber communication cables, distributed fiber optic sensing (DFOS), Physics-informed machine learning algorithms, monitoring of critical underwater components, integrated data processing (DPU), and comprehensive monitoring.

Provided are an off-grid wind-hydrogen energy supply system for polar regions and a control method thereof, and relate to the field of new energy supply. In the system, wind energy is converted by a wind power generation system into electric energy, and a cryogenic battery energy storage system is charged. Chemical energy of hydrogen and oxygen is converted by a hydrogen fuel cell system into electric energy, and the cryogenic battery energy storage system is charged. Energy is stored by the cryogenic battery energy storage system, a wind power fluctuation is mitigated, and a load is smoothened. Operation parameters of the wind power generation system, the cryogenic battery energy storage system, and the hydrogen fuel cell system are obtained by an intelligent monitoring system.