The disclosure provides a method and system to monitor and advise the status of a group of floating platforms, such as offshore floating wind platforms, by using a floating platform's motion in the group and detect one or more anomalies to identify one or more disorders (including irregularities) in the group. Input for this method can include information of wind speed and direction and orientations of the wind turbine nacelles. The orientation of the platform can complement the orientation of a wind turbine nacelle on the platform in case the platform is equipped with a turret. Disorders include, but not limited to, mooring line failure, shifts of a drag anchor, other issues with the mooring system components such as fairleads, issues with the ballasting configuration of the floater, issues with the turret (if any), issues with the swivel of the nacelle, issues with the rotor, and issues with the blades.

A system for maintaining buoyant, energy-capture devices in general relative position in water in the presence of surface waves allows heeling of the energy capture devices while preventing collision. The system includes a grid of structural members that resists compression while permitting limited relative surface displacement between the first and second energy-capture devices. The structural members may be partially compressible and provide a restoring force, and they may allow heeling. Electricity from wave energy capture devices is combined in a way that smoothes variations inherent in wave action. Electricity from wind energy capture devices is combined with energy from wave energy capture devices for transmission to shore.

A method for planning repowering of a wind energy plant is disclosed. The wind energy plant is positioned at a site and comprises a plurality of original wind turbines arranged at distributed positions within the site. Historical data related to the original wind turbines is retrieved. The historical data is collected over a previous time period and during operation of the original wind turbines. Meteorological conditions at the site of the wind energy plant are estimated based on the retrieved historical data. The repowering of the wind energy plant is planned based on the estimated meteorological data, including planning replacement of the original wind turbines by replacement wind turbines to be positioned at the positions of the original wind turbines.

Systems and methods for building predictive and prescriptive analytics of wind turbines generate a historical operational dataset by loading historical operational SCADA data of one or more wind turbines. Each sensor measurement is associated with an engineering tag and at least one component of a wind turbine. The system creates one or more performance indicators corresponding to one or more sensor measurements, and applies at least one data clustering algorithm onto the dataset to identify and label normal operation data clusters. The system builds a normal operation model using normal operational data clusters with Efficiency of Wind-To-Power (EWTP) and defines a statistical confidence range around the normal operation model as criterion for monitoring wind turbine performance. As real-time SCADA data is received by the system, the system can detect an anomalous event, and issue an alert notification and prescriptive early-action recommendations to a user, such as a turbine operator, technician or manager.

A wind turbine offshore installation method of installing a wind turbine using a semi-submersible type floating substructure includes: a step of towing the semi-submersible type floating substructure on which the wind turbine is erected to an installation target site on a sea; and a step of coupling the wind turbine and a spar type floating substructure for supporting the wind turbine on the sea at the installation target site to install the wind turbine on the sea.

A method for computer-implemented identifying an unauthorized access to a wind farm is provided by obtaining environmental and operational data of the wind farm from a repository, which include technical data and organizational data of the wind farm indicating tasks to be dealt with in the wind farm in the future. Based on the environmental and operational data, for a predetermined time interval, a prediction of the operation and/or states of the wind farm is determined by a trained data driven mode. The trained data driven model provides a prediction of the operation and/or states of the wind farm as a digital output. The prediction is compared to operational conditions of the wind farm resulting from current or past user machine interactions of a user. An unauthorized access is identified in case of a predetermined deviation of the obtained operational conditions from the prediction of the operation and/or states of the wind farm.

A method for controlling an inclination of a floating wind turbine platform to optimize power production, or to reduce loads on the turbine, tower, and platform, or both, includes receiving data associated with the inclination of the floating wind turbine platform and wind speed and direction data. An angle of difference between the turbine blade plane and the wind direction is determined, where the angle of difference has a vertical component. A platform ballast system is then caused to distribute ballast to reduce the vertical component to a target angle chosen to optimize power production, or reduce turbine, tower, and platform loads, or both.

The disclosure provides a method and system to monitor and advise the status of a group of floating platforms, such as offshore floating wind platforms, by using a floating platform's motion in the group and detect one or more anomalies to identify one or more disorders (including irregularities) in the group. Input for this method can include information of wind speed and direction and orientations of the wind turbine nacelles. The orientation of the platform can complement the orientation of a wind turbine nacelle on the platform in case the platform is equipped with a turret. Disorders include, but not limited to, mooring line failure, shifts of a drag anchor, other issues with the mooring system components such as fairleads, issues with the ballasting configuration of the floater, issues with the turret (if any), issues with the swivel of the nacelle, issues with the rotor, and issues with the blades.

The present disclosure describes methods, apparatuses, and systems to protect wind turbines, wind farms, and power infrastructure. For instance, wind turbines produce several streams of data varying over time, including sensor readings from components in wind turbines, network traffic from SCADA systems, data from wind farm internal networks, data from the internet, etc. According to the techniques described herein, wind farms may be protected by identifying patterns that may not be apparent from individual time series or network data. Embodiments of the present disclosure include integration and fusion of information from various time series data sources and network data sources for detecting patterns in data (e.g., patterns in data that may indicate an abnormal event, such as wind farm component failure, a control system cyber-attack, etc.). For instance, in some cases, such patterns may be used to detect an abnormal event of interest (e.g., such as an attack).

The objective of the present invention is to diagnose an abnormality in the amount of electricity generated by a distributed power source while restricting an increase in the number of sensors. A packet generating unit 200 generates electricity generation amount packet data D2a from electricity generation amount data D1 and identification data D6; an electricity generation feature quantity calculating unit 201 calculates electricity generation feature quantity data D3a, D3b indicating a feature quantity of the amount of electricity generated by each distributed power source, from each item of and a result output unit 203 consolidates abnormalities in the amounts of electricity generated by the distributed power sources, indicated by the determination result data D5, as abnormality diagnosis result data D7, and outputs the same to a display unit 206.