An example method comprises receiving historical wind turbine failure data and asset data from SCADA systems, receiving first historical sensor data, determining healthy assets of the renewable energy assets by comparing signals to known healthy operating signals, training at least one machine learning model to indicate assets that may potentially fail and to a second set of assets that are operating within a healthy threshold, receiving first current sensor data of a second time period, applying a machine learning model to the current sensor data to generate a first failure prediction a failure and generate a list of assets that are operating within a healthy threshold, comparing the first failure prediction to a trigger criteria, generating and transmitting a first alert if comparing the first failure prediction to the trigger criteria indicates a failure prediction, and updating a list of assets to perform surveillance if within a healthy threshold.

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.

An equipment performance modeling platform is disclosed. In certain embodiments, an adaptive sensing coordinator acquires sensor measurements, configures and processes the sensor measurements for a specific statistical model, and sends the measurements to a server. A server performs data processing, provides storage (e.g., local or in a database), and provides an interface for data extraction. Statistical models are used to interpreting sensor values for a type of equipment, and a labeling mechanism labels performance occurrences.

The present invention belongs to the technical field of information, particularly relates to the theories such as time series interval prediction, extreme learning machine modeling and Gaussian approximation solution, and is a wind power output interval prediction method. First, interval prediction of wind power output influencing factors is realized by time series analysis and normal exponential smoothing so as to consider an input noise factor. Then an extreme learning machine prediction model is established with an interval result as an input, output distribution is calculated based on iterative expectation and a conditional variance law, and thus an interval prediction result of wind power output is obtained. The method has advantages in interval prediction performance and calculation efficiency and can provide guidance for production, scheduling and safe operation of a power system.

Methods (200) for reducing vibrations in one or more rotor blades (120) of a wind turbine (160), when the wind turbine is in standstill conditions are provided. The method comprises measuring (201) one or more deformation parameters indicative of deformation of one or more blades (120), determining (202), at a dedicated controller (190) for an auxiliary drive system (20, 107), a vibration of one or more of the blades (120) based on the deformation parameters, wherein the dedicated controller (190) for the auxiliary drive system is separate from the wind turbine controller (180), and generating (203), at the dedicated controller (190), an output signal to operate the auxiliary drive system to reduce the vibration. Also disclosed are wind turbines (160) which comprise a dedicated controller (190) configured to determine a vibration and generating an output signal to reduce the vibration, when the wind turbine is in standstill conditions.

A system for generating power includes an environmental engine operating on one or more computing devices that determines a wind flowing over a blade of a wind turbine, wherein the wind flowing over the blade of the wind turbine varies based on environmental conditions and operating parameters of the wind turbine. The system also includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a plurality of different models for the wind turbine. Each model characterizes a relationship between at least two of a rotor speed, a blade pitch, the wind flowing over the blade, a wind speed and a turbulence intensity for the wind turbine. The AI ensemble engine selects a model with a highest efficiency metric, and simulates execution of the selected model to determine recommended operating parameters.

A wind turbine control unit includes an upsampling module that receives a first control signal that includes a current control sample value and a predicted control trajectory. The upsampling module also calculates a second control signal in dependence on the current control sample value and the predicted control trajectory. The second control signal has a higher frequency than the first control signal. The upsampling module further outputs the second control signal for controlling an actuator.

A system for servicing watercraft includes one or more waterborne platforms. Each waterborne platform includes an electric power supply, a driving system for moving the waterborne platform in a body of water, a watercraft interfacing system configured to at least supply electric power to an electrically-powered watercraft, and a control interface configured to exchange data with a controller. The controller is configured to: receive input data, determine respective destination locations for the waterborne platforms to supply electric power to the electrically-powered watercraft, and send control data that includes data indicating the destination locations to the waterborne platforms.

A method for providing grid-forming control of an inverter-based resource includes receiving a negative-sequence voltage feedback of the inverter-based resource. The method also includes receiving at least one negative-sequence feedback signal of the inverter-based resource. The method also includes determining, via a negative-sequence regulator, one or more control signals indicative of a desired negative-sequence impedance of the inverter-based resource using the at least one negative-sequence feedback signal. Further, the method includes generating, via the negative-sequence regulator, a control command for the inverter-based resource based on the one or more control signals. Moreover, the method includes controlling the inverter-based resource based on the control command to achieve the desired negative-sequence impedance of the inverter-based resource.

Physical and/or financial instruments may optimally hedge the cash flow of one or more renewable energy generators based on a desired risk and return profile of renewable infrastructure investors. Baseline revenues may be determined based on forward-looking electricity market price scenarios corresponding to qualified market products intended for sale from the renewable energy generators. Risk and return metrics of cash flows of the renewable energy generators may be determined. At least one physical hedge and/or financial hedge may be added. The size and operation of the renewable energy generators along with any physical hedges, or financial hedges, or both physical and financial hedges, may be optimized across multiple market price scenarios of qualified market products to optimize investor-tailored risk and return utility functions.