Big Data Analytics in the Maintenance of Off-Shore Wind Turbines: A Study on Data Characteristics

The aim of this study is to discuss the characteristics of the input data to the data analytical algorithms of a predictive maintenance system, from the viewpoint of big data technology. The discussed application is for the maintenance of off-shore wind turbines. The maintenance of off-shore wind turbines is an expensive and sensitive task. Therefore, making decision for planning and scheduling of maintenance in a wind farm (which is made by the operating company of a wind farm) is important and plays a critical role in the cost of maintenance. In this paper, the current state of the art for big data technology in the maintenance of off-shore wind turbines is presented. The dimensions of big data analytics and the technical requirements of data for the use of this technology in the maintenance of off-shore wind turbines are described. A contribution of this paper is to study the technical requirements of suitable data for decision-making. The outcomes of presented study are identifying the characteristics of input data to predictive maintenance in the era of big data and the discussion of these characteristics in the condition monitoring for off-shore wind energy.

[1]  Murtaza Haider,et al.  Beyond the hype: Big data concepts, methods, and analytics , 2015, Int. J. Inf. Manag..

[2]  István Petrás,et al.  ”Big Data” Initiative as an IT Solution for Improved Operation and Maintenance of Wind Turbines , 2013 .

[3]  M. Taisch,et al.  The value of Big Data in servitization , 2015 .

[4]  Rainer Göb.  Discussion of “Reliability Meets Big Data: Opportunities and Challenges” , 2014 .

[5]  Fouad Slaoui-Hasnaoui,et al.  Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges , 2014 .

[6]  Mayorkinos Papaelias,et al.  Condition monitoring of wind turbines: Techniques and methods , 2012 .

[7]  M. White,et al.  Digital workplaces , 2012 .

[8]  Sung-Hoon Ahn,et al.  Condition monitoring and fault detection of wind turbines and related algorithms: A review , 2009 .

[9]  Gerd Balzer,et al.  Reliability model of large offshore wind farms , 2009, 2009 IEEE Bucharest PowerTech.

[10]  Kalyan Veeramachaneni,et al.  Unsupervised learning and fusion for failure detection in wind turbines , 2009, 2009 12th International Conference on Information Fusion.

[11]  Peter Géczy,et al.  BIG DATA CHARACTERISTICS , 2014 .

[12]  Shahriar Akter,et al.  How ‘Big Data’ Can Make Big Impact: Findings from a Systematic Review and a Longitudinal Case Study , 2015 .

[13]  Irina Branovic,et al.  Smart power grid and cloud computing , 2013 .

[14]  Andreas Prinz,et al.  Big Data Metadata Management in Smart Grids , 2014, Big Data and Internet of Things.

[15]  A. Karyotakis,et al.  On the optimisation of operation and maintenance strategies for offshore wind farms , 2011 .

[16]  Yili Hong,et al.  Reliability Meets Big Data: Opportunities and Challenges , 2014 .

[17]  Yih-Huei Wan,et al.  Wind Power Plant Behaviors: Analyses of Long-Term Wind Power Data , 2004 .

[18]  George K. Karagiannidis,et al.  Big Data Analytics for Dynamic Energy Management in Smart Grids , 2015, Big Data Res..