Abstract
Despite the significance and growth of wind energy as a major source of renewable energy, research on the risks of wind turbines in the form of accidents and failures has attracted limited attention. Research that applies data analytics methodologically in this context is scarce. The research presented here, upon construction of a text corpus of 721 selected wind turbine accident and failure news reports, develops and applies a custom-developed data analytics framework that integrates tabular analysis, visualization, text mining, and machine learning. Topic modeling was applied for the first time to identify and classify recurring themes in wind turbine accident news, and association mining was applied to identify contextual terms associated with death and injury. The tabular and visual analyses relate accidents to location (offshore vs. onshore), wind turbine life cycle phases (transportation, construction, operation, and maintenance), and the incidence of death and injury. As one of the insights, more incidents were found to occur during operation and transportation. Through topic modeling, topics associated most with deaths and injuries were revealed. The results could benefit wind turbine manufacturers, service providers, energy companies, insurance companies, government bodies, non-profit organizations, researchers, and other stakeholders in the wind energy sector.
Highlights
The inevitable demand for energy across the globe is projected to expand over the years and decades ahead
We developed a text analytics framework for topic modeling of wind turbine accidents (Figure 2), which is novel for the literature on wind energy research
The applicability of the developed framework is demonstrated through the analysis of an extensive text collection of wind turbine accident news between the years 2010 and 2019, using the text dataset collected during the research process
Summary
The inevitable demand for energy across the globe is projected to expand over the years and decades ahead. The World Energy Outlook 2020 report by the International. Energy Agency (IEA) predicts a growth of 4% to 9% in global energy demand from 2019 to. 2030, despite the global COVID-19 pandemic that began in late 2019 [1]. Renewable energy sources, including wind, solar, biomass, geothermal, and hydropower, can help reduce the consumption of fossil fuels and the release of greenhouse gases [2]. According to the 2019 outlook by the IEA, at least 66% to 80% of the newly added global energy capacity is forecasted to come from renewables. Along with solar power, is anticipated to constitute the majority of additional power generation by 2040 [3]. In the USA, wind power has grown more than three times from 2009 (35,130 MW) to 2019 (105,591 MW)
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