1 research outputs found
Offshore Wind Data Integration
Doktorgradsavhandling i informasjons- og kommunikasjonsteknologi, Universitetet i Agder, Grimstad, 2014Using renewable energy to meet the future electricity consumption and to reduce
environmental impact is a significant target of many countries around the world.
Wind power is one of the most promising renewable energy technologies. In particular,
the development of offshore wind power is increasing rapidly due to large
areas of wind resources. However, offshore wind is encountering big challenges
such as effective use of wind power plants, reduced cost of installation as well as
operation and maintenance (O&M).
Improved O&M is likely to reduce the hazard exposure of the employees, increase
income, and support offshore activities more efficiently. In order to optimize the
O&M, the importance of data exchange and knowledge sharing within the offshore
wind industry must be realized. With more data available and accessible, it is possible
to make better decisions, and thereby improve the recovery rates and reduce
the operational costs.
This dissertation proposes a holistic way of improving remote operations for offshore
wind farms by using data integration. Particularly, semantics and integration
aspects of data integration are investigated. The research looks at both theoretical
foundations and practical implementations.
As the outcome of the research, a framework for data integration of offshore wind
farms has been developed. The framework consists of three main components: the
semantic model, the data source handling, and the information provisioning. In
particular, an offshore wind ontology has been proposed to explore the semantics
of wind data and enable knowledge sharing and data exchange. The ontology is
aligned with semantic sensor network ontology to support management of metadata
in smart grids. That is to say, the ontology-based approach has been proven to be
useful in managing data and metadata in the offshore wind and in smart grids. A
quality-based approach is proposed to manage, select, and provide the most suitable
data source for users based upon their quality requirements and an approach to
formally describing derived data in ontologies is investigated