## 1 Introduction  
Wind energy is essential to the satisfaction of electrical power demands in an environmentally sustainable manner. With the increase in installed wind power capacity, the operation and maintenance (O&M) costs of wind turbines are gradually increasing. Research has shown that the use of a supervisory control and data acquisition (SCADA) system is an economical and effective method for identifying early signs of failure and performance issues; thus, such systems have been widely installed in large-scale wind turbines. Using the SCADA system, large quantities of environmental data and equipment status data are stored. By mining these SCADA data, several investigations can be carried out, such as for condition monitoring [1; 2; 3], fault diagnosis [4; 5], ageing [6; 7] and reliability assessment of wind turbines [8]. A wind turbine is a piece of equipment that generates electricity through the conversion of the kinetic energy of the wind, which automatically adjusts the operating state according to the wind speed and direction. The research and application of the real-time monitoring and fault diagnosis are important to ensure the wind turbine safety and save O&M costs [9; 10]. The electricity generation performance of wind turbines is an important index that manufacturers, wind farm operators, investors and grid operators consider. The power curve is commonly used to monitor wind turbine power performance, as it is an important indicator that reflects the electricity generation performance. By monitoring the variation in the power curve, the operating power performance of wind turbines can be assessed, and problems can be identified [11]. Therefore, it is necessary to define a baseline power curve that represents the optimal power performance of the wind turbine. This reference curve is either constructed by using the measured wind-power data or provided by the wind turbine manufacturers [11; 12]. The actual power curve generated by SCADA data can be compared with this baseline curve, and the deviation between the two curves can reflect variations in power performance. This deviation is often represented as a health value (HV) or confidence value [13; 14]. Further trend analysis on the gradual change of the health value will help to identify signs of performance degradation, performance optimization opportunities, incipient fault detection, and so on. Due to the highly dynamic and stochastic operating conditions, the wind-power data from the SCADA system contain a large quantity of abnormal data because of underperformance, changes in wind resources or equipment failure/anomalies such as pitch system faults and ice on the blade [15; 16]. Therefore, the efficient and objective calculation of the health value is key to power performance assessment. At present, a considerable number of methods are used to calculate the health value. Some of these approaches first model the actual power curves and then analyze the deviations between the baseline power curve and the actual power curve. Such approaches are called parametric evaluation methods. [PERSON] et al. [17] constructed parametric models of the power curve with a logistic function to monitor wind turbine performance. Other parametric modelling methods have been presented, such as the bin method [18], the evaluation method [19], the Gaussian process [20; 21], fuzzy clustering [22], the copulas function [23], the Gaussian mixture model and the neural network [24]. Based on the parametric model, performance assessment methods that use the baseline power curve for comparison were presented. These comparison methods included minimum quantization error, Euclidean distance and residue analysis. A comparative study of wind turbine performance assessment based on three typical parametric evaluation methods was carried out in reference [25]. The results indicated that the Gaussian mixture model-\(L_{2}\) distance (GMM-L2) method is suitable for degradation modelling and performance prediction.