Multivariable Analysis for Advanced Analytics of Wind Turbine Management
Operation and maintenance tasks on the wind turbines have an essential role to ensure the correct condition of the system and to minimize losses and increase the productivity. The condition monitoring systems installed on the main components of the wind turbines provide information about the tasks that should be carried out over the time. A novel statistical methodology for multivariable analysis of big data from wind turbines is presented in this paper. The objective is to analyse the necessary information from the condition monitoring systems installed in wind farms. The novel approach filters the main parameters from the collected signals and uses advanced computational techniques for evaluating the data and giving meaning to them. The main advantage of the approach is the possibility of the big data analysis based on the main information available.
KeywordsCondition monitoring systems Multivariable analysis Wind turbine maintenance Neural networks
The work reported herewith has been financially supported by the Spanish Ministerio de Economíay Competitividad, under Research Grant DPI2015-67264, and the FP7 Research project with reference FP-7-Energy-2012-TREN-1:322430.
- 1.Arbib MA (2003) The handbook of brain theory and neural networks. MIT Press, CambridgeGoogle Scholar
- 2.Asht S, Dass R (2012) Pattern recognition techniques: a review. Int J Comput Sci Telecommun 3(8)Google Scholar
- 8.Gomez Munoz C, la Hermosa De et al (2014) A novel approach to fault detection and diagnosis on wind turbines. Glob Nest J 16(6):1029–1037Google Scholar
- 12.Lloyd G (2007) Guideline for the certification of condition monitoring systems for wind turbines. Hamburg, GermanyGoogle Scholar
- 15.Masters T (1993) Practical neural network recipes in C++. Morgan Kaufmann, San FranciscoGoogle Scholar
- 17.Novaes Pires de G, Alencar E, Kraj A (2010) Remote conditioning monitoring system for a hybrid wind diesel system-application at fernando de naronha island, brasilGoogle Scholar
- 18.OPTIMUS (2014) Demonstration of methods and tools for the optimisation of operational reliability of large-scale industrial wind turbines, optimus projectGoogle Scholar
- 20.Pliego Marugán A, García Márquez F (2014) System management for remote condition monitoring in railway systems. In: 6th IET conference on railway condition monitoring (RCM 2014), IET, pp 1–10Google Scholar
- 22.Pullen A (2015) Global wind report annual market update 2014. Technical report, Global Wind Report Annual Market, pp 3–9Google Scholar