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Home»Articles»Monitoring the Wind Turbine Condition Using Big Data Technique

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Asian Journal of Computer Science and Technology (AJCST)

Editor Dr. K. Ganesh
Print ISSN : 2249-0701
Frequency : Quarterly

Monitoring the Wind Turbine Condition Using Big Data Technique

Author : N. V. Poorima, B. Srinivasan and S. Karthikeyan
Volume 8 No.1 Special Issue:February 2019 pp 98-102

Abstract

The desire to cut back the price of energy from turbine generation has seen a rise within the analysis applied to the sphere of turbine condition observation. Wind turbine condition observation has the potential to cut back operation and maintenance prices through optimized maintenance programming and also the rejection of major breakdowns. To aid this analysis, increasing volumes of knowledge are being captured and keep. These massive volumes of knowledge could also be deemed ‘Big Data’, and need improved handling techniques so as to figure with the information with efficiency. It introduces a turbine condition observation system that has been put in in AN operational Vestas V47 turbine for the aim of developing algorithms to sight machine deterioration. The system’s ability to capture massive volumes of knowledge (approx.2TB per month) has LED to the need of victimization increased knowledge handling techniques. This paper can discuss these ‘Big Data’ techniques and recommend however they will ultimately be used for condition observation of multiple wind turbines or wind farms.

Keywords

Big Data Techniques, Wind Turbine Condition Monitoring

Full Text:

References

[1] J. Hurwitz, A.N. F. Halper, M. Kaufman, “Big Data For Dummies”, John Wiley & Sons, Inc, 2013.
[2] Intel and IBM, “Combat Credit Card Fraud with Big Data”, Intel Corporation, 2013.
[3] T.H. Davenport and J. Dyche, “Big Data in Big Companies”, SAS Institute Inc., 2013
[4] Beyer, M. Gartner Says “Solving ‘Big Data’ Challenge Involves More Than Just Managing Volumes of Data”. [cited 2013 7/10/2013] [Online] Available at: http://www.gartner.com/newsroom/i d/1731916., 2011
[5] M. Kezunovic, L. Xie, and S. Grijalva, “The Role of Big Data in Improving Power System Operation and Protection”, in IREP Symposium – Bulk Power System Dynamics and Control – IX (IREP), Rethymnon, Greece., 2013
[6] Software, I., “Managing big data for smart grids and smart meters”, May 2012.
[7] N. Leavitt, “Will NoSQL Database Live Up to Their Promise?” Computer, Vol.43, No. 2, pp. 12,14, Feb 2010
[8] J. Dean,. and S. Ghemawat, “Map Reduce: a flexible data processing tool”. Communications of the ACM,. Vol. 53, No.1, pp. 72-77, Jan 2010
[9] Z.J. Viharos, et al., “Big Data Initiative as an IT Solution for Improved Operation and Maintenance of Wind Turbines”, in EWEA2013, EWEA: Vienna. pp. 184-188.
[10] Zaher, et al., Database Management for High Resolution Condition Monitoring of Wind Turbines, UPEC: 2009 44th International Universities Power Engineering Conference, 2009.
[11] D. Ferguson, et al., Designing Wind Turbine Condition Monitoring Systems Suitable for Harsh Environments, in IET Renewable Power Generation, IET: Beijing, 2013.
[12] P., Tavner, et al., Condition Monitoring of Rotating Electrical Machines, Institution of Engineering and Technology, 2008.
[13] V. Chandola, A. Banerjee, and V. Kumar, Anomaly detection: A survey, ACM Computing Surveys, pp. 1, September 2009.

Asian Journal of Computer Science and Technology is a peer-reviewed international journal that publishes high-quality scientific articles (both theory and practice) and research papers covering all aspects of future computer and Information Technology areas. Topics include, but are not limited to:

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The desire to cut back the price of energy from turbine generation has seen a rise within the analysis applied to the sphere of turbine condition observation. Wind turbine condition observation has the potential to cut back operation and maintenance prices through optimized maintenance programming and also the rejection of major breakdowns. To aid this analysis, increasing volumes of knowledge are being captured and keep. These massive volumes of knowledge could also be deemed ‘Big Data’, and need improved handling techniques so as to figure with the information with efficiency. It introduces a turbine condition observation system that has been put in in AN operational Vestas V47 turbine for the aim of developing algorithms to sight machine deterioration. The system’s ability to capture massive volumes of knowledge (approx.2TB per month) has LED to the need of victimization increased knowledge handling techniques. This paper can discuss these ‘Big Data’ techniques and recommend however they will ultimately be used for condition observation of multiple wind turbines or wind farms.

Editor-in-Chief
Dr. K. Ganesh
Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
Expert at McKinsey and Company, India
[email protected]
Editorial Advisory Board
Dr. Eng. Hamid Ali Abed AL-Asadi
Department of Computer Science, Basra University, Iraq
[email protected]
Dr. Norjihan Binti Abdul Ghani
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Christos Bouras
Department of Computer Engineering & Informatics, University of Patras, Greece
[email protected]
Dr. Maizatul Akmar Binti Ismail
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Harold Castro
Department of Systems Engineering and Computing, University of the Andes, Colombia
[email protected]
Dr. Busyairah Binti Syd Ali
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. Sri Devi Ravana
Department of Information system, University of Malaya, Malaysia
[email protected]
Dr. Karpaga Selvi Subramanian
Department of Computer Engineering, Mekelle University, Ethiopia
[email protected]
Dr. Mazliza Binti Othman
Department of Computer System & Technology, University of Malaya, Malaysia
[email protected]
Dr. Chiam Yin Kia
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. OUH Eng Lieh
Department of Information Systems, Singapore Management University, Singapore
[email protected]

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    Editorial Note

    Editorial Dr. K. Ganesh

    Editor-in-Chief
    Dr. K. Ganesh
    Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
    Expert at McKinsey and Company, India
    [email protected]
    Editorial Advisory Board
    Dr. Eng. Hamid Ali Abed AL-Asadi
    Department of Computer Science, Basra University, Iraq
    [email protected]
    Dr. Norjihan Binti Abdul Ghani
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Christos Bouras
    Department of Computer Engineering & Informatics, University of Patras, Greece
    [email protected]
    Dr. Maizatul Akmar Binti Ismail
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Harold Castro
    Department of Systems Engineering and Computing, University of the Andes, Colombia
    [email protected]
    Dr. Busyairah Binti Syd Ali
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Sri Devi Ravana
    Department of Information system, University of Malaya, Malaysia
    [email protected]
    Dr. Karpaga Selvi Subramanian
    Department of Computer Engineering, Mekelle University, Ethiopia
    [email protected]
    Dr. Mazliza Binti Othman
    Department of Computer System & Technology, University of Malaya, Malaysia
    [email protected]
    Dr. Chiam Yin Kia
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. OUH Eng Lieh
    Department of Information Systems, Singapore Management University, Singapore
    [email protected]

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