Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data

Cristian Velandia-Cardenas, Yolanda Vidal and Francesc Pozo
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Cristian Velandia-Cardenas: Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Besòs (CDB), Universitat Politècnica de Catalunya (UPC), Eduard Maristany 16, 08019 Barcelona, Spain
Yolanda Vidal: Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Besòs (CDB), Universitat Politècnica de Catalunya (UPC), Eduard Maristany 16, 08019 Barcelona, Spain
Francesc Pozo: Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Besòs (CDB), Universitat Politècnica de Catalunya (UPC), Eduard Maristany 16, 08019 Barcelona, Spain

Energies, 2021, vol. 14, issue 6, 1-26

Abstract: 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.

Keywords: fault detection; machine learning; principal component analysis; SCADA; structural health monitoring; wind turbine; imbalanced data; support vector machines; k nearest neighbors (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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