A method for monitoring a wind turbine including: i) obtaining, from a data storage, a plurality of sets of measurement data of at least two measurement variables, the measurement variables being measurement variables of the wind turbine, acquired by first sensors, and/or the environment of the wind turbine, acquired by second sensors, and the measurement data of a respective set of measurement data being acquired at a same time point in the past; ii) processing the measurement data of the at least two measurement variables by creating an image suitable for visualization; iii) determining a deviation type from a predetermined operation of the wind turbine by processing the image by a trained data-driven model configured as a convolutional neural network, where the image is fed as a digital input to the trained data-driven model and the trained data-driven model provides the deviation type as a digital output.

The present invention provides a hydraulic turbine cavitation acoustic signal identification method based on big data machine learning. According to the method, time sequence clustering based on multiple operating conditions under the multi-output condition of the hydraulic turbine set is performed by utilizing an neural network, characteristic quantities of the hydraulic turbine set under a steady condition in a healthy state is screened; a random forest algorithm is introduced to perform feature screening of multiple measuring points under steady-state operation of the hydraulic turbine set, optimal feature measuring points and optimal feature subsets are extracted, finally a health state prediction model is constructed by using gated recurrent units; whether incipient cavitation is present in the equipment is judged. The present invention can effectively identify the occurrence of incipient cavitation in the hydraulic turbine set, reducing unnecessary shutdown of the equipment and prolonging the service life.

A wave energy capture system deployed in water converts mechanical motion induced by waves in the water to electrical energy. A controller of the wave energy capture system receives input regarding real-time wave conditions in a vicinity of the wave energy capture system. The controller applies a control model to the received input to select a value of a control parameter for the wave energy capture system, where the control model includes a model that has been trained using machine learning to take wave condition data as input and to output control parameter values selected based on the wave condition data in order to increase an amount of energy captured by the wave energy capture system. The controller implements the selected value of the control parameter on the wave energy capture system.

The present invention relates to non-thermal renewable energy turbines (20,24,34, 38,40), in particular to the monitoring of turbine performance to identify a loss of performance indicative of faults or component degradation. The method involves comparison of measured power from a target turbine (20) with a predicted value for same turbine. The predicted value is calculated using the output from a plurality of other turbines (24,34,38,40) from an array and a predictive model including weightings for the other turbines (24, 34,38,40) based on the strength of correlation of their historical with historical data from the target turbine (20).

A wind turbine control apparatus, method and non-transitory computer-readable medium are disclosed. The wind turbine control apparatus comprises a generator connected to a wind turbine with a drive train. The drive train comprises a rotor, a low speed shaft, a gear box, a high speed shaft, and a controller module. The controller module is configured to obtain a maximum power within a large range of varying wind velocities by operating the rotor at a neural network determined optimal angular speed for the current wind velocity.

A method for analyzing of a road transport route for transport of a heavy load from an origin to a destination includes i) obtaining images of the transport route, the images being images taken by a drone or satellite camera system, where each of the images includes a different road section of the complete transport route and an peripheral area adjacent to the respective road section; ii) determining objects and their location in the peripheral area of the road section by processing each of the images by a first trained data driven model, where the images are as a digital input to the first trained data driven model and where the first trained data driven model provides the objects, if any, and their location as a digital output; and iii) determining critical objects from the number of determined objects along the road transport route

The present disclosure provides a method and an apparatus for self-adaption of a cut-out strategy. The method may include: predicting, using a wind speed prediction model, a wind resource parameter of a wind turbine at each machine location; predicting, using a load prediction model, a fatigue load and a limit load of the wind turbine based on the predicted wind resource parameter and an air density; comparing the predicted fatigue load and limit load with a reference load; and determining the cut-out strategy based on a result of the comparison, wherein determining the cut-out strategy includes determining a cut-out wind speed and an output power.

A method for controlling wind turbines. Incident signal data is obtained from wind turbines and fed to an artificial intelligence (AI) model in order to identify patterns in the incident signals generated by the wind turbines. One or more actions are associated to the identified patterns, based on identified actions performed by the wind turbines in response to the generated incident signals. During operation of the wind turbines, one or more incident signals from one or more wind turbines are detected and compared to patterns identified by the AI model. In the case that the detected incident signal(s) match(es) at least one of the identified patterns, the wind turbine(s) are controlled by performing the action(s) associated with the matching pattern(s).

A method for operating a generator of a wind turbine includes generating, via a controller, a time series of a plurality of operating signals of the generator. The method also includes applying at least one algorithm to the time series of the plurality of operating signals of the generator to generate a processed time series of the of the plurality of operating signals of the generator. Moreover, the method includes identifying, via the controller, patterns in the processed time series of the plurality of operating signals of the generator to identify one or more of at least one slip event occurring in the slip coupling or a surface health of the slip coupling. Further, the method includes implementing, via the controller, a control action when the at least one slip event occurring in the slip coupling is identified or the surface health of the slip coupling is indicative of degradation in the slip coupling.

An automated method to determine modal characteristics of a wind turbine tower at an offshore location in a continuous manner includes reading one or more sensor data signals, prefiltering the one or more sensor data signals to divide the signals into a plurality of time segments, obtaining a frequency domain representation of each of the plurality of time segments by computing a Power Spectral Density (PSD) of each of the time segments to identify one or more frequency peaks in each of the time segments, assigning a probability to each of the frequency peaks in the PSD of each of the time segments, combining all assigned probabilities and determining the likelihood of the one or more frequency peaks. Also disclosed is an offshore wind turbine tower having a turbine control system utilizing the automated method to determine modal characteristics of the wind turbine.