## 1 Introduction
Turbine manufacturers and operators widely adopt supervisory control and data acquisition (SCADA) data-based CM as it improves performance and minimises O&M costs [13,14]. [PERSON] et al. [15] proposed a thermophysics-based approach (a synthesised thermal model) that uses SCADA data for WT drivetrain fault diagnosis by deriving relationships between various SCADA signals and changes in the thermophysics of WT operation. The results suggest that the SCADA-based thermophysics technique is useful in identifying non-linearity of the gearbox oil temperature rise with wind speed/output power, which can effectively suggest gearbox efficiency degradation that may be attributed to gear transmission problems such as gear teeth wear. [PERSON] et al. [16] proposed a co-integration methodology based on SCADA data for improving CM and fault diagnosis that was found to be effective. Most of these technologies have concentrated on using SCADA signals rather than the SCADA alarms that are recorded in the SCADA system. Nevertheless, SCADA alarms are triggered and recorded when vital component signals exceed threshold limits. Hence, the inclusion of alarm signals can assist in identifying anomalies and improving WT performance. Recently, probability-based Venn diagrams and artificial neural networks [17,18,19], as well as classification methods [20], have been used for the analysis of SCADA alarm signals for WT condition/performance monitoring. The WT power curve is widely used as a benchmark for use in purchase contracts and to correctly correlate the non-linear relationship between hub height wind speed (measured immediately before the rotor) and electrical power from the turbine [21]. Power curves can be used to optimise operational costs, enhance reliability and for condition/performance monitoring activities, and therefore are considered as critical performance indicators. However, power curves are adversely affected by changing environmental and topographical conditions, and thus may be site-specific [22; 23]. Depending upon turbine rating and design, the commercial power curve shape deviates from the theoretical power curve [24; 25]. Nevertheless, there has been extensive research on techniques for appraising power curves based on SCADA data over the last decades; these can be categorised into parametric and non-parametric methods as follows. Parametric techniques include specified mathematical equations, perhaps from a family of functions, and with several parameters that are selected to provide the best fit to a particular WT power curve. Segmented linear models [26], polynomial regression [27; 28], and models based on probabilistic distributions such as four- or five-parameter logistic distributions [29; 30] have been used. Unlike parametric techniques, non-parametric approaches are adaptive. They can exhibit a high degree of flexibility because they do not enforce any pre-specified equation, and such methods can tightly fit the measured data subject to some specified smoothness of the fit criterion. Commonly used non-parametric techniques include ANN [31], SVM [32], GP [33] and Copula function [34]; they have proved to be useful in SCADA-based power curve modelling for improving WTs' forecasting and prediction as well as for O&M activities. In contrast to classical neural network techniques, SVM is useful in solving problems related to classification and prediction for non-linear issues. [PERSON] proposed the SVM initially in 1992 [35], and it has been upgraded to provide better computational ability as well as higher prediction accuracy [36]. [PERSON] et al. [37] proposed the SVM classification-based technique to identify failures related both to rotor blade imbalance and imbalance using simulated data points. The proposed algorithm compared different SVM kernels to neural networks with the conclusion that the linear kernel SVM outperforms other kernels and ANNs, in terms of validated accuracy, training and tuning times. [PERSON] et al.