Remotely monitoring offshore wind turbines via ZigBee networks embedded with an advanced routing strategy

  • Yongduan SONG*
  • , Bikun WANG
  • , Bin LI
  • , Yu ZENG
  • , Lei WANG
  • *Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

12 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number013110
JournalJournal of Renewable and Sustainable Energy
Volume5
Issue number1
DOIs
Publication statusPublished - Jan 2013
Externally publishedYes

Bibliographical note

The constructive comments provided by the two anonymous reviewers and the Editors are also greatly appreciated.

Funding

This work was supported in part by the National High Technology Research and Development Program of China under Grant No. 2012AA052302, the National Natural Science Foundation of China (Grant Nos. 609704052, 61134001, and 52105064), and the Fundamental Research Funds for the Central Universities.

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