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dc.contributor.authorSong, Y.
dc.contributor.authorLi, Bin
dc.contributor.authorWang, B.
dc.contributor.authorZeng, Y.
dc.contributor.authorWang, L.
dc.identifier.citationSong, Y. and Li, B. and Wang, B. and Zeng, Y. and Wang, L. 2013. Remotely monitoring offshore wind turbines via ZigBee networks embedded with an advanced routing strategy. Journal of Renewable and Sustainable Energy. 5 (1): Article ID 013110.

As better wind speeds are available offshore compared to on land, offshore wind power contribution in terms of electricity supplied is higher, thus more and more offshore wind turbines have been and will be deployed. However, the severe offshore conditions make it necessary to develop reliable and cost-effective real-time monitoring system when building offshore wind power farms. This paper proposes an innovative method for designing remote monitoring system for offshore wind turbines based on ZigBee wireless sensor networks. ZigBee networks carrying variety of sensors actively collect dynamic data related to the system operation status, including parameters of the mechanical unit and electrical unit as well as the operation environment. Each wind turbine itself represents a single wireless network, which sends information to remote monitoring center by GPRS module to achieve full wireless communication. To enhance the topologic efficiency and reduce the energy consumption of the networks, an optimized routing algorithm is developed. A physical system based on such method is developed. Analysis and experiment tests with real wind farm data indicate that the developed system works fairly well. The fundamental idea as studied in this work is of great value for building reliable and affordable real-time monitoring systems for wind farms (offshore and on land) with enhanced safety and efficiency.

dc.publisherAmerican Institute of Physics
dc.titleRemotely monitoring offshore wind turbines via ZigBee networks embedded with an advanced routing strategy
dc.typeJournal Article
dcterms.source.titleJournal of Renewable and Sustainable Energy
curtin.departmentDepartment of Mathematics and Statistics
curtin.accessStatusFulltext not available

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