What is it about?

This paper analyzes a decade of wind turbine accident news using topic modeling. By applying data analytics techniques, the study identifies recurring themes and contextual terms associated with accidents. The analysis provides insights into the causes, locations, and phases of accidents, as well as the incidence of death and injury. The research aims to benefit stakeholders in the wind energy sector by improving risk analysis and accident prevention.

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Why is it important?

This research is important because it addresses the limited attention given to wind turbine accidents and failures. The use of data analytics methodologically in this context is unique and timely. The study's integration of tabular analysis, visualization, text mining, and machine learning provides a comprehensive approach to understanding accidents. The findings can help researchers and practitioners in wind turbine manufacturing, service provision, energy companies, insurance companies, government bodies, and non-profit organizations enhance safety measures and mitigate risks.

Perspectives

This research opens up new doors for further studies in wind turbine accident analysis. It motivates researchers to explore advanced techniques in topic modeling, association mining, and data analytics to gain deeper insights into accident patterns and causes. It encourages investigations into the effectiveness of preventive measures, the impact of location and environmental factors, and the development of predictive models for accident prevention. The research publication stimulates interdisciplinary collaborations and knowledge sharing among researchers, practitioners, and industry professionals in the wind energy sector.

Gurdal Ertek

Read the Original

This page is a summary of: Analyzing a Decade of Wind Turbine Accident News with Topic Modeling, Sustainability, November 2021, MDPI AG,
DOI: 10.3390/su132212757.
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