An Automated SCADA Alarm Analysis in Wind Turbines for Improving Reliability and Downtime – A Solution for Operators

Supervisory Control and Data Acquisition (SCADA) signals such as wind speed and power output are frequently used for fault detection in wind turbine (WT) condition monitoring. However, there have been limited attention on SCADA alarm analysis, despite its importance in managing turbine reliability. This paper presents an automated methodology to analyze SCADA alarm signals for identifying patterns predictive of turbine faults and minimizing downtime. To minimize downtime and identify turbine fault patterns, this paper proposes an automated methodology for analyzing SCADA alarm signals. First, alarms are classified into normal, abnormal, and questionable events using a structured taxonomy. These categories are linked to instances of downtime in a later analysis. Alarms are ranked according to their operational impact and criticality using a prioritization method that was modified from industry standards. The results show that even low-volume alarm data holds significant diagnostic and prognostic value. The suggested event-and pattern-based alarm analysis improves the ability to identify faults and provides proactive maintenance planning. This work underscores the value of intelligent alarm handling to reduce downtime and improve operational reliability in wind farms.