## 1 Introduction
However, parametric evaluation methods involve model selection, parameter estimation, error modelling and algorithm convergence; thus, these methods require human intervention to acquire the best parametric model, which is mainly used for offline data analysis. To achieve online wind turbine assessment, there are many problems to overcome. To avoid modelling problems, nonparametric assessment methods that directly assess the actual wind-power data and the baseline wind-power data were proposed [26; 27; 28], the derivation of secondary principal component (PCA2-Dev) method is a representative method [27]. PCA2-Dev reduced the dimensionality of wind-power data via principal analysis and selected power data as the main evaluation data. The deviations were calculated by using the ratio of the standard deviations of the baseline power data and the actual power data. However, PCA2-Dev could assess only quasilinear wind-power data and could not be used with rated power data. However, the wind turbine maximized its power output in the rated power region; thus, performance fluctuation this region would affect the economic benefit of wind farms. Therefore, SCADA data in the rated power region need to be assessed. In this paper, a novel nonparametric assessment method is developed based on the probabilistic area metric that can compute the health value of the actual wind-power data by locating either the quasilinear region or the rated power region and achieve online assessment in real time. The presented method is compared with the PCA2-Dev method and traditional statistical methods. The results show that the proposed method is simple and effective. In addition, because the probabilistic area metric is used in the proposed method for direct derivation, an iterative process is not required, and convergence does not need to be considered. The assessment result shows that the power performance variation is stable; thus, this result appropriate for prediction analysis. In summary, the key scientific contributions of this paper are as follows:  
* The probabilistic area metric is used to evaluate the deviations of the probabilistic models of two datasets. In other words, the datasets are transformed into probabilistic models instead of means or variances, and the deviations of the probabilistic models are calculated. The area metric has an obvious advantage in dynamic and stochastic SCADA data mining analysis. * The assessment method combines physical model analysis and data mining techniques. The assessment result is statistically significant, and it has physical significance and is easy for engineers to understand. The rest of this paper is organized as follows. Section 2 introduces the basic theory of power curves and establishes the objective of the current study. Section 3 explains the data preprocessing method, the principle of selecting the baseline data, the health value based on the probabilistic area metric and the calculation flow. In Section 4, the proposed method is compared with the PCA2-dev method and the traditional statistical approach. Section 5 discusses the application of the present method in performance optimization, degradation analysis and condition pre-warnings. Section 6 summarizes the study.