# A Compound Approach for Monitoring the Variation in Wind Turbine Power Performance with SCADA Data
## 1 Introduction  
Compared with traditional energy sources, renewable energy, such as wind power, is becoming increasingly popular owing to its cleaner and more efficient nature [1, 2]. As an efficient energy source with no carbon emissions and wide distribution around the world, wind energy has gradually developed into one of the most important renewable energy sources in the form of clean energy. In the process of transitioning to low-carbon electricity generation, expanding the scale of wind farms is inevitable. Wind turbines are the core equipment, capturing wind energy and converting it to electricity. The accurate assessment of wind turbines' power generation performance is crucial for the operation of wind farms. The assessment and real-time monitoring of the power generation performance of wind turbines are further complicated by the multifarious and dynamic external environment and self-imposed defects. It is a matter of formality for operators and manufacturers in the wind power industrial chain to constantly think about how to identify defects and deeply excavate the potential power generation capacity of wind turbines in a timely manner. Supervisory control and data acquisition (SCADA) systems have been widely employed for the operational monitoring of modern wind turbines [3]. As frontier approaches to Industry 4.0, big data, big-data mining, and artificial intelligence techniques bring breakthrough development opportunities to the wind power industry [4]. The continuous monitoring of wind turbine conditions to detect potential failures at an early stage draws attention worldwide. Early failure detection enables prompt responses prior to catastrophic accidents and reduces economic losses and maintenance costs [5]. Benefiting from the boom in cloud computing and data-mining technologies, dataanalysis methods have been extensively investigated in fault detection and the condition-based monitoring of wind turbine components, such as blades, gearboxes, and bearings, drawing increasing attention to the reliability improvements in wind turbines [6; 7]. [PERSON] et al. [8] primarily discussed the design of convolutional and recurrent neural networks for extracting spatial and temporal features from SCADA systems. A coarse learner and multiple fine learners were established to enhance the reliability of fault diagnosis results through collective decision making. [PERSON] et al. [9] proposed an improved maximum correlated kurtosis inverse convolution adjustment method for wind turbine gear fault diagnosis based on encoder signals. Convolutional adjustment definitions were introduced to overcome the limitations of traditional deconvolution methods. Finally, real experimental cases validated that encoder signals can serve as alternative tools for wind turbine gear fault diagnosis. [PERSON] et al. [10] developed a wind turbine gearbox fault detection method based on a deep, fully connected neural network. [PERSON] et al. [11] developed an adaptive method to identify blade damage based on a microphone array and compressive beamforming. A generalized minimax concave penalty function was employed to enhance sparse recovery capacities, and the potential damage locations were extracted in coarse acoustic maps to improve the convergence rates. The experimental results demonstrated that several instances of damage to operating blades can be precisely recognized with high efficiencies, and the deterioration of acoustic maps induced by improper step sizes can be avoided. [PERSON] et al. [12] presented an adaptive proportional integral algorithm with an automatic signal correction system for the detection and diagnosis of pitch controller and pitch actuator faults. [PERSON] et al. [13] combined Kalman filtering and an artificial neural network for diagnosing blade pitch system faults and achieved the goal of high diagnostic accuracy in fault instances of wind turbines. [PERSON] et al.