Wind Turbine SCADA Data Imbalance: A Review of Its Impact on Health Condition Analyses and Mitigation Strategies

Adaiton Oliveira-Filho (), Monelle Comeau, James Cave, Charbel Nasr, Pavel Côté and Antoine Tahan ()
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Adaiton Oliveira-Filho: Department of Mechanical Engineering, École de Technologie Supérieure, Université du Québec, 1100 Rue Notre Dame O, Montreal, QC H3C 1K3, Canada
Monelle Comeau: Power Factors, 7005 Boulevard Taschereau, Brossard, QC J4Z 1A7, Canada
James Cave: Department of Mechanical Engineering, École de Technologie Supérieure, Université du Québec, 1100 Rue Notre Dame O, Montreal, QC H3C 1K3, Canada
Charbel Nasr: Department of Mechanical Engineering, École de Technologie Supérieure, Université du Québec, 1100 Rue Notre Dame O, Montreal, QC H3C 1K3, Canada
Pavel Côté: Power Factors, 7005 Boulevard Taschereau, Brossard, QC J4Z 1A7, Canada
Antoine Tahan: Department of Mechanical Engineering, École de Technologie Supérieure, Université du Québec, 1100 Rue Notre Dame O, Montreal, QC H3C 1K3, Canada

Energies, 2024, vol. 18, issue 1, 1-23

Abstract: The rapidly increasing installed capacity of Wind Turbines (WTs) worldwide emphasizes the need for Operation and Maintenance (O&M) strategies favoring high availability, reliability, and cost-effective operation. Optimal decision-making and planning are supported by WT health condition analyses based on data from the Supervisory Control and Data Acquisition (SCADA) system. However, SCADA data are highly imbalanced, with a predominance of healthy condition samples. Although this imbalance can negatively impact analyses such as detection, Condition Monitoring (CM), diagnosis, and prognosis, it is often overlooked in the literature. This review specifically addresses the problem of SCADA data imbalance, focusing on strategies to mitigate this condition. Five categories of such strategies were identified: Normal Behavior Models (NBMs), data-level strategies, algorithm-level strategies, cost-sensitive learning, and data augmentation techniques. This review evidenced that the choice among these strategies is mainly dictated by the availability of data and the intended analysis. Moreover, algorithm-level strategies are predominant in analyzing SCADA data because these strategies do not require the costly and time-consuming task of data labeling. An extensive public SCADA database could ease the problem of abnormal data scarcity and help handle the problem of data imbalance. However, long-dated requests to create such a database are still unaddressed.

Keywords: wind turbine; SCADA data; imbalanced data; normal behavior model; data augmentation techniques (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: 2024
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