Monitoring the Wind Turbine Condition Using Big Data TechniqueAuthor : N. V. Poorima, B. Srinivasan and S. Karthikeyan
Volume 8 No.1 Special Issue:February 2019 pp 98-102
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.
Big Data Techniques, Wind Turbine Condition Monitoring
 J. Hurwitz, A.N. F. Halper, M. Kaufman, “Big Data For Dummies”, John Wiley & Sons, Inc, 2013.
 Intel and IBM, “Combat Credit Card Fraud with Big Data”, Intel Corporation, 2013.
 T.H. Davenport and J. Dyche, “Big Data in Big Companies”, SAS Institute Inc., 2013
 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
 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
 Software, I., “Managing big data for smart grids and smart meters”, May 2012.
 N. Leavitt, “Will NoSQL Database Live Up to Their Promise?” Computer, Vol.43, No. 2, pp. 12,14, Feb 2010
 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
 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.
 Zaher, et al., Database Management for High Resolution Condition Monitoring of Wind Turbines, UPEC: 2009 44th International Universities Power Engineering Conference, 2009.
 D. Ferguson, et al., Designing Wind Turbine Condition Monitoring Systems Suitable for Harsh Environments, in IET Renewable Power Generation, IET: Beijing, 2013.
 P., Tavner, et al., Condition Monitoring of Rotating Electrical Machines, Institution of Engineering and Technology, 2008.
 V. Chandola, A. Banerjee, and V. Kumar, Anomaly detection: A survey, ACM Computing Surveys, pp. 1, September 2009.