In the wind energy industry, the power curve represents the relationship between the “wind speed” at the hub height and the corresponding “active power” to be generated. It is the most versatile condition indicator and of vital importance in several key applications, such as wind turbine selection, capacity factor estimation, wind energy assessment and forecasting, and condition monitoring, among others. Ensuring an effective implementation of the aforementioned applications mostly requires a modeling technique that best approximates the normal properties of an optimal wind turbines operation in a particular wind farm. This challenge has drawn the attention of wind farm operators and researchers towards the “state of the art” in wind energy technology. This paper provides an exhaustive and updated review on power curve based applications, the most common anomaly and fault types including their root-causes, along with data preprocessing and correction schemes (i.e., filtering, clustering, isolation, and others), and modeling techniques (i.e., parametric and non-parametric) which cover a wide range of algorithms. More than 100 references, for the most part selected from recently published journal articles, were carefully compiled to properly assess the past, present, and future research directions in this active domain.

Wind turbines (WTs) are complex systems composed of multiple components. In order to assess the overall condition of a given WT, one may need to employ multiple models; such a solution, however, is deemed to be unwieldy and expensive to implement, especially for large-scale wind farms. This article proposes a novel heterogeneous stacked regressor (HET-SR) algorithm, which learns from optimal power curve data attained through a delicate signal processing scheme involving density-based spatial clustering of applications with noise (DBSCAN). The proposed method is thus a normal behavior model (NBM) capable of accurately assessing the health status of an entire WT through an overall health indicator such as the power curve. We compare this method with state-of-the-art methods and perform an extensive experimental work based on real operational data from the supervisory control and data acquisition (SCADA) system. The findings of this research work provide evidence on the effectiveness and superiority of the proposed method in terms of high accuracy in fault detection.

Recent advances in the wind energy industry have stimulated the demand for automated condition monitoring mechanisms capable of mitigating the cost of operations and avoiding tremendous economic losses due to unplanned downtime. To this end, a wide range of normal behavior models have been developed to monitor wind turbine performance. However, since most models are tailored to a single target at a time, a separate model is required for each target and are thus deemed unwieldy and expensive to implement, particularly in large-scale wind farms. Therefore, this article advocates for a multitarget normal behavior model which is capable of monitoring multiple targets simultaneously. The proposed model is specifically based on heterogeneous stacked regressions, trained with normal data curated via kernel density estimation. The distinct targets are monitored through a control chart based on an exponentially weighted moving average chart and a change-point detection (CPD) method via binary segmentation for wind turbine suboptimal performance detection. Extensive experiments based on real-world wind farm data are carried out and the results are compared with state-of-the-art methods. The attained results indicate that the proposed model is highly effective in not only reducing the number of models required for monitoring wind turbines, but also in improving model accuracy significantly.