Wind turbine (WT) reliability has come to the forefront of research due to the rapid growth of wind energy in recent years. Reliability information can help understand failure causes and focus maintenance and prevention efforts on the most critical components, reducing costs and increasing profits. This paper offers new insights into WT reliability after analysing the data provided by the Supervisory Control And Data Acquisition (SCADA) system collected from seven onshore WTs located in central Spain from January 2014 to September 2021. To this end, we propose a method to link SCADA data to failure and maintenance records based on checking whether each 10-min average time sample was collected when any failure or maintenance action had been reported. These records have been manually mapped to the WT taxonomy based on the standard Reference Designation System for Power Plants (RDS-PP\u00ae) with minor changes. We present three different results: (i) The capacity factor and time-based availability of each WT; (ii) the subsystem failure rate and downtime to identify the most critical ones; and (iii) each WT power curve with the 10-min time samples labelled as healthy, under maintenance, or failure states, along with a ranking of the subsystems causing the most failures in each part of the power curves. It is the first time that time samples are linked to failure and maintenance records to visualise their distribution on the power curves. These results can help research point in the right direction to improve reliability and increase electricity production worldwide.<\/p>\n
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