K-nearest neighbour and K-fold cross-validation used in wind turbines for false alarm detection

Abstract

Maintenance management is critical to ensure the efficiency and competitiveness of energy production in the wind energy industry. Current maintenance operations are planned according to the alarm distribution of the wind farm provided by the supervisory control and data acquisition system, and false alarms may lead to unnecessary operation. The detection of false alarms is critical to ensure proper wind turbine maintenance management plans because false alarms increase the complexity and the costs of maintenance operations. This paper proposes a methodology based on K-nearest neighbour algorithms that compare different k-fold cross-validation values for false alarm detection. A real case scenario formed by three real wind turbines is presented to test the reliability of the methodology. The signal and alarm dataset are acquired by the supervisory control and data acquisition system and an alarm log as a response variable. It is demonstrated that the performance of the three wind turbines is analogous and the variations of the k-cross validation value showed that the accuracy does not increase significantly. The proposed methodology presented an accuracy of 98 % and more than 22 % of false alarms were detected in the case study. These outcomes demonstrate the robustness of the proposed approach for detecting false alarms in wind turbines.

Keywords

• K-nearest neighbours
• Machine learning
• Wind turbine
• Cross-validation
• Alarm
• False alarm detection