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.