# A Compound Approach for Monitoring the Variation in Wind Turbine Power Performance with SCADA Data
## 1 Introduction
[14] devised a vibration-based fault detection method for offshore wind turbine diveraterians based on the optimal selection of the acceleration measurements. [PERSON] et al. [15] utilized a new method based on a Bayesian and an adapted Kalman-augmented Lagrangian for filtering signals under time-varying conditions for the fault detection of wind turbine blade bearings. [PERSON] et al. [16] proposed a novel method and embedded a multivariable query pattern in a self-attention mechanism for quantifying the influences of different features on the anomalies of wind turbines. The experimental results confirmed the effectiveness of the proposed method for the early detection of wind turbine faults and identification of anomaly causes. [PERSON] et al. [17] presented a SCADA data-driven method for the fault diagnosis of wind turbines based on incremental learning and the multivariate state estimation technique (MSET). The experimental results demonstrated that incremental MSET can obtain higher estimation accuracies and lower false-alarm rates in long-term operation and detect potential gearbox faults from hours to weeks in advance. Meanwhile, the performance assessment of wind turbines mainly relies on power curve modeling, which is a conventionally recognized standard in the field of wind energy [18]. Many scholars have conducted extensive research on power curve modeling, which can be classified into two categories: parametric methods and non-parametric methods [19]. [PERSON] et al. [20] organized a comprehensive review comparing different wind turbine power curve modeling approaches. Several real wind turbines with their experimental data were used in that study. The obtained results showed a good correlation between the estimated energy output and the measured energy output. [PERSON] et al. [21] emphasized the importance of detecting and removing outliers from a dataset before modeling, which improved the accuracy of their power curve assessments. The outliers were directly related to the abnormal operation states of wind turbines, such as power limitations, shutdowns caused by failures and maintenance, and measurement errors. [PERSON] et al. [22] proposed an efficient combinational algorithm based on an Isolation Forest (I-Forest) and a mean-shift model for data cleaning in the power curve modeling of wind turbines. Local abnormal data were detected by I-Forest, and the remaining stacked data were eliminated by the mean-shift model. The numerical results of application cases positively confirmed the reliability of the universal framework provided by the proposedalgorithm. [PERSON] et al. [23] constructed a model-data hybrid-driven outlier detection method for power curve modeling and designed an adaptive update rule for major parameters in that method to enhance the detection accuracy. [PERSON] et al. [24] employed a methodology for wind turbine performance monitoring using high-frequency SCADA data by multivariate-featured and non-parametric methods, and detected the performance degradation through analysis of the power-predicted residuals. [PERSON] et al. [25] invoked a Dirichlet Process Gaussian Mixture model for data clustering to identify the contour of the main power band used as a baseline performance region. The Mahalanobis distance was used to determine whether new monitoring data lay outside the contour of the main power band. In the industrial performance degradation case caused by the high temperature of the gearbox oil, the proposed method quickly issued an alarm only 2 hours later than the first degraded operational data appeared. [PERSON] et al. [26] proposed a three-stage multiple data pre-processing algorithm for power curve modeling and introduced a novel evaluation method based on Energy Characteristic Consistency to assess the reliability of these algorithms. Four wind turbines were selected to verify the effectiveness of the nine data processing methods.