As the global energy demand is increasing, the share of renewable energy and specifically wind energy in the supply is growing. While vast literature exists on the design and operation of wind turbines, there exists a gap in the literature with regards to the investigation and analysis of wind turbine accidents. This paper describes the application of text mining and machine learning techniques for discovering actionable insights and knowledge from news articles on wind turbine accidents. The applied analysis methods are text processing, clustering, and multidimensional scaling (MDS). These methods have been combined under a single analysis framework, and new insights have been discovered for the domain. The results of our research can be used by wind turbine manufacturers, engineering companies, insurance companies, and government institutions to address problem areas and enhance systems and processes throughout the wind energy value chain.
Ertek, G., Chi, X., Zhang, A. N., & Asian, S. (2017, December). Text mining analysis of wind turbine accidents: An ontology-based framework. In Big Data (Big Data), 2017 IEEE International Conference on (pp. 3233-3241). IEEE.
The published paper can be accessed from the following url:
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