Correlation Between Wind Turbine Failures and Environmental Conditions: A Machine Learning Approach - Da Silva, Thadeu Carneiro; Da Silva Antunes, Fabio Augusto; Teixeira, Julio Carlos; Contreras, Rodrigo Colnago [UNESP] (Institute of Electrical and Electronics Engineers (IEEE), 2025)
Wind energy has emerged as a vital renewable source, competing with conventional energy due to its clean and inexhaustible nature. However, the global mass production of wind turbines often disregards the unique environmental conditions of installation sites, leading to performance and reliability challenges. This study applies machine learning methodologies to analyze the correlation between wind turbine failures and local environmental conditions. The research leverages Rough Set Theory to transform instances of undesirable turbine shutdowns - especially those influenced by incomplete tropicalization processes - into actionable decision rules. The findings provide practical insights applicable to wind farms worldwide, enabling optimized maintenance strategies and precise adjustments to protection parameters. These improvements contribute to reducing failure rates, enhancing energy conversion efficiency, and promoting the sustainable expansion of wind energy across diverse geographic and climatic contexts.
Keywords: Machine learning; rough sets; wind energy; wind turbine failures; wind turbine projects tropicalization