## 1 Introduction  
In recent years, the world wind power industry has developed rapidly. According to the statistics, by the end of 2018, the world's newly installed wind power capacity was 51.3 GW, and the world's cumulative installed wind power capacity reached 591 GW [1]. The single unit capacity of wind turbines has also increased from 30 kW several decades ago to 8 MW at present. With the continuous increase in the installed capacity, wind turbines are also being deployed to more remote land and sea locations. Difficulties in maintenance lead to increased operating costs of wind farms and increased electricity consumption costs for customers, requiring more stringent requirements for the reliable and economical operation of wind turbines. To determine the real-time operation status of wind turbines and to implement reasonable maintenance of wind turbines, modern large wind turbines are equipped with SCADA (SupervisoryControl and Data Acquisition) systems. The application of SCADA data for wind turbine state identification, fault diagnosis, and early warning is currently a hot research topic in academic circles [2; 3; 4] and focuses mainly on statistical methods or data mining methods, such as neural networks and support vector machines [5; 6]. In terms of statistical methods, [PERSON] and others identified faulty wind turbines through comparative analysis of SCADA data of more than three wind turbines [7; 8]. [PERSON] and [PERSON] et al. conducted identification research on gearbox faults by extracting the SCADA data of wind farms [9; 10]. [PERSON] et al. analyzed SCADA long term operation data based on multivariable state estimation technology, thus identifying gearbox faults of wind turbine units [11]. [PERSON] et al. constructed an evaluation index of state discrimination based on the pretreatment of SCADA raw data to evaluate quantitatively the wind turbine blade failure and generator bearing failure [12]. [PERSON] et al. [13] established a probability model of a wind turbine power curve based on the Copula function. Using the SCADA system operation data, the results showed that the early failure symptoms of blade degradation and the yaw and pitch system of wind turbines could be effectively monitored. [PERSON] et al. [14] used the Hotelling T2 statistical method to analyze the active power operation data of a wind turbine to identify its operation state. [PERSON] et al. [15] selected characteristic parameters based on the BP neural network model and used the RSME method to identify generator bearing faults. [PERSON] et al. [16] proposed a fuzzy synthetic method for real-time condition assessment of the wind turbine gearbox to reduce the Operation and Maintenance (O&M) cost with the optimized O&M strategy. In terms of artificial intelligence methods, [PERSON] et al. [17] constructed a BP neural network model of gearbox temperature to identify faults. [PERSON] et al. [18] applied an adaptive neuro-fuzzy inference system to SCADA data processing for a wind farm and investigated wind turbine state identification. [PERSON] et al. [19] established a standard operating state model of a wind generator based on the neural network method and proposed a health index to measure the difference between the operating state of the generator and the standard state and to evaluate the operating state of the generator. [PERSON] et al. [20] proposed a fault diagnosis method based on Variational Mode Decomposition (VMD) Multi-scale Permutation Entropy (MPE) and Feature based Transfer Learning (FTL) to monitor the health condition of the wind turbine gearbox. These studies are of guiding significance to the evaluation and identification of wind turbine operation status, but lack in-depth interpretations of the physical significance of the evaluation of the wind turbine operation status and systematic analysis of the factors affecting the evaluation. Using SCADA data, this paper proposes a new health index and its calculation method to characterize the operating state of wind turbines. The remaining part is organized as follows.