Distributed compression for condition monitoring of wind farms

Vladimir Stankovic, Lina Stankovic, Shuang Wang, Samuel Cheng

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


A good understanding of individual and collective wind farm operation is necessary for improving the overall performance of the wind farm “grid,” as well as estimating in real time the amount of energy that can be generated for effectively managing demand and supply over the smart grid. This paper proposes a scheme for compressing wind speed measurements exploiting both temporal and spatial correlation between the readings via distributed source coding. The proposed scheme relies on a correlation model based on true measurements. Two compression schemes are proposed, both of low encoding complexity, as well as a particle-filtering-based belief propagation decoder that adaptively estimates the nonstationary noise of the correlation model. Simulation results using realistic models show significant performance improvements compared to the scheme that does not dynamically refine correlation.
Original languageEnglish
Pages (from-to)174-181
Number of pages8
JournalIEEE Transactions on Sustainable Energy
Issue number1
Early online date17 Sep 2012
Publication statusPublished - Sep 2012


  • wind farms
  • distributed compression
  • condition monitoring
  • adaptive decoding
  • distributed source coding


Dive into the research topics of 'Distributed compression for condition monitoring of wind farms'. Together they form a unique fingerprint.

Cite this