DOI: 10.5061/dryad.5dv41nsft
title: Data from: The design of an intelligent fault-tolerant control for floating offshore wind turbine with blade faults

description: The great efficiency and deep-sea suitability of floating offshore wind
 turbines (FOWTs) have led to their rapid development. It is also extremely
 difficult to regulate FOWTs. Two major obstacles are the increased
 component failure rate and the difficulty of accurately modelling FOWTs.
 For that reason, this study suggests a model-free adaptive fault-tolerant
 control strategy to deal with problems caused by blade root moment
 sensors. In order to sidestep the need to mathematically describe FOWT, a
 fault compensation and individual pitch controller are developed using a
 model-free adaptive control technique. Instead of requiring fault
 detection and isolation, the suggested fault-tolerant control scheme turns
 fault dynamic compensation into a nonlinear system's control issue
 that has to be solved in real-time. By simulating and testing the
 suggested control method using FAST, we find that it not only keeps the
 wheel's bearing load balanced, but it also greatly reduces the
 FOWT's bearing load and the floating platform's movement. Also,
 the strategy's great fault tolerance capabilities under numerous
 blade root moment sensor failures is confirmed by the fact that the output
 power is closer to the rated power.
description: Offshore wind turbines (FOWTs) have grown significantly due to
 their deep sea suitability and high power generating efficiency. However,
 FOWT control is challenging. FOWT modelling accuracy and component failure
 rates are the key issues. This research suggests a model-free adaptive
 fault-tolerant control strategy for blade root moment sensor failures.
 Using model-free adaptive control, an individual pitch controller and
 fault compensator are created to circumvent mathematical modelling of
 FOWT. Our fault-tolerant control technique eliminates the requirement for
 fault detection and isolation, turning fault dynamic compensation into a
 real-time issue for nonlinear systems. FAST simulations demonstrate that
 the proposed control technique effectively balances wheel bearing load,
 reduces floating platform movement, and greatly reduces FOWT bearing load.
 Additionally, the strategy's output power is close to the rated
 power, demonstrating remarkable fault tolerance with numerous blade root
 moment sensor failures.
description: # Data from: The design of an intelligent fault-tolerant control for
 floating offshore wind turbine with blade faults
 [https://doi.org/10.5061/dryad.5dv41nsft](https://doi.org/10.5061/dryad.5dv41nsft) ## Description of the data and file structure From 2015 to 2019, 6,924 wind turbines from more than 10 countries were located in this dataset, which offers geocoded information on OWTs worldwide. With data accessible at a spatial resolution of 10 m, an explicit dataset is provided for the purpose of maritime space planning, monitoring, and management. The global dataset is referred to as the WGS84 data. Each record consists of seven characteristics: continent, nation, centroid latitude and longitude, sea area, appearance year, and appearance monthcord. ## General Information * **Title**: Offshore Floating Wind Turbine Dataset (2015-2019) * **Accession**: WGS84 Offshore Floating Wind Turbine Data * **Description**: This dataset contains detailed geospatial and temporal information on 6,924 offshore floating wind turbines (OWTs) located across more than 10 countries. The data are specifically designed to support maritime spatial planning, monitoring, and management activities. Data were acquired between 2015 and 2019, providing a comprehensive global view of OWT distributions at a high spatial resolution of 10 meters. The dataset includes the precise geocoded location of each OWT, along with key metadata, including the country, continent, sea area, and the time of appearance (in year and month) for each wind turbine. All geospatial data are recorded using the **WGS84** coordinate system, which ensures consistency in global positioning. * **Study Dates**: 2015-01-01 to 2019-12-31 * **Geographic Scope**: Global (more than 10 countries across multiple continents) ### Data Description Each record in the dataset consists of the following seven attributes: 1. **Continent**: The continent where the offshore floating wind turbine is located. 2. **Nation**: The country to which the wind turbine belongs. 3. **Centroid Latitude**: Latitude of the wind turbine's centroid in WGS84 coordinates. 4. **Centroid Longitude**: Longitude of the wind turbine's centroid in WGS84 coordinates. 5. **Sea Area**: The name of the sea or ocean in which the wind turbine is located. 6. **Appearance Year**: The year when the wind turbine was first recorded. 7. **Appearance Month**: The month when the wind turbine was first recorded. * **Spatial Resolution**: 10 meters * **Coordinate System**: WGS84 (World Geodetic System 1984) ##  Description of the Supplementary Files * The file named "paper_research_unknown_authors.pdf" illustrates the general methodology and describes the software used for processing the prodided data; * The file named "Report_turbines_24-10-2023.pdf" provides a review of the state of the art of the related liteature and a general overview of the methodologies used for processing the data from wind turbines. ## Usage Notes This dataset is especially useful for: * Maritime spatial planning and policy development * Monitoring the expansion and distribution of offshore wind energy infrastructure * Environmental impact assessments * Academic research related to renewable energy and marine environments Researchers and planners should be aware of the spatial resolution and geospatial accuracy, which is designed to meet high-precision mapping needs. ### File Format The dataset is provided in **CSV** format, with one record per offshore wind turbine. The columns follow the structure listed above, and each record represents a unique OWT with corresponding geospatial and temporal attributes. ## Data Acquisition and Processing * **Data Sources**: The data were collected from satellite imagery, maritime records, and other geospatial sources from over 10 different countries. The spatial resolution was achieved by employing high-precision remote sensing technologies. * **Temporal Coverage**: The dataset covers offshore floating wind turbines that were deployed or observed between 2015 and 2019. * **Processing**: All geospatial coordinates were normalized to the WGS84 system for uniformity. The temporal data on wind turbine appearance were cross-verified with national maritime records. ## Acknowledgments We would like to thank the various national maritime authorities, satellite data providers, and renewable energy organizations that contributed data to this dataset. ## Contact Information * **Primary Contact**: Offshore Wind Energy Research Group * **Email**: [offshorewindresearch@domain.com](mailto:offshorewindresearch@domain.com) * **Address**: 123 Research Blvd, Energy City, Country