Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data

• Published in: Energies (Basel) Vol. 14; no. 6; p. 1728
• Main Authors: Velandia-Cardenas, Cristian, Vidal, Yolanda, Pozo, Francesc
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.03.2021
• Subjects: fault detection; machine learning; principal component analysis; SCADA; structural health monitoring; wind turbine
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en14061728

Wind power is cleaner and less expensive compared to other alternative sources, and it has therefore become one of the most important energy sources worldwide. However, challenges related to the operation and maintenance of wind farms significantly contribute to the increase in their overall costs, and, therefore, it is necessary to monitor the condition of each wind turbine on the farm and identify the different states of alarm. Common alarms are raised based on data acquired by a supervisory control and data acquisition (SCADA) system; however, this system generates a large number of false positive alerts, which must be handled to minimize inspection costs and perform preventive maintenance before actual critical or catastrophic failures occur. To this end, a fault detection methodology is proposed in this paper; in the proposed method, different data analysis and data processing techniques are applied to real SCADA data (imbalanced data) for improving the detection of alarms related to the temperature of the main gearbox of a wind turbine. An imbalanced dataset is a classification data set that contains skewed class proportions (more observations from one class than the other) which can cause a potential bias if it is not handled with caution. Furthermore, the dataset is time dependent introducing an additional variable to deal with when processing and splitting the data. These methods are aimed to reduce false positives and false negatives, and to demonstrate the effectiveness of well-applied preprocessing techniques for improving the performance of different machine learning algorithms.