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Investigators at Lancaster University Report Findings in Algorithms (Wind Turbine Fault Detection and Identification Through PCA-Based Optimal Variable Selection)

  • Oct 11, 2018
  • 651 views
Source: 
Journal of Mathematics

By a News Reporter-Staff News Editor at Journal of Mathematics -- A new study on Algorithms is now available. According to news reporting from Lancaster, United Kingdom, by VerticalNews journalists, research stated, "An effective condition monitoring system of wind turbines generally requires installation of a high number of sensors and use of a high sampling frequency in particular for monitoring of the electrical components within a turbine, resulting in a large amount of data. This can become a burden for condition monitoring and fault detection systems."

Financial supporters for this research include UK Engineering and Physical Sciences Research Council, Engineering Department at Lancaster University.

The news correspondents obtained a quote from the research from Lancaster University, "This paper aims to develop algorithms that will allow a reduced dataset to be used in wind turbine fault detection. This paper first proposes a variable selection algorithm based on principal component analysis with multiple selection criteria in order to select a set of variables to target fault signals while still preserving the variation of data in the original dataset. With the selected variables, this paper then describes fault detection and identification algorithms, which can identify faults, determine the corresponding time and location where the fault occurs, and estimate its severity. The proposed algorithms are evaluated with simulation data from PSCAD/EMTDC, Supervisory control and data acquisition data from an operational wind farm, and experimental data from a wind turbine test rig."

According to the news reporters, the research concluded: "Results show that the proposed methods can select a reduced set of variables with minimal information last whilst detecting faults efficiently and effectively."

For more information on this research see: Wind Turbine Fault Detection and Identification Through PCA-Based Optimal Variable Selection. IEEE Transactions on Sustainable Energy, 2018;9(4):1627-1635. IEEE Transactions on Sustainable Energy can be contacted at: Ieee-Inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA. (Institute of Electrical and Electronics Engineers - www.ieee.org/; IEEE Transactions on Sustainable Energy - ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165391)

Our news journalists report that additional information may be obtained by contacting Y.F. Wang, University of Lancaster, Dept. of Engn, Lancaster LA1 4YW, United Kingdom. Additional authors for this research include X.D. Ma and P. Qian.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1109/TSTE.2018.2801625. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

 

(Our reports deliver fact-based news of research and discoveries from around the world.)

 

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