# Anomaly Identification of Wind Turbine Yaw System Based on Two-Stage Attention-Informer Algorithm
## 1 Introduction  
According to the latest statistics from the National Energy Administration of China, as of the end of October 2022, the installed capacity of wind power in China reached approximately 350 million kilowatts, representing a year-on-year growth of 16.6% compared to 2021. [PERSON] stated in \"2022 in review for offshore wind\" in January 2023 that the global offshore wind industry is expected to achieve a five-fold growth by 2030 [1]. With the steady development of renewable energy, offshore wind power will play a crucial role in the field of clean energy and demonstrate vast development prospects. The main components of a wind turbine unit include the blades, gearbox, generator, pitch system, and yaw system [2]. Among these, the yaw system is one of the key executive mechanisms for quickly and accurately aligning the wind turbine unit with the wind direction to capture the maximum wind energy [3]. The operational status of the yaw system directly affects the performance and power generation of the wind turbine unit. If the yaw system malfunctions, it may lead to loss of control or unnecessary resource wastage, and even jeopardizethe stable operation of the unit. Additionally, an unstable yaw system can reduce the safety and reliability of the wind turbine unit, while increasing operation and maintenance costs. Wind turbine units often operate in harsh natural environments, enduring prolonged exposure to extreme and complex conditions such as temperature variations, snow accumulation, salt spray, and sand and dust particles. Moreover, the constantly changing wind conditions require frequent adjustments of the yaw system between different operational states. Consequently, the turbine units unavoidably experience multiple loads, leading to a gradual decline in insulation strength, fatigue resistance, and operational performance over time, ultimately resulting in system failures [4]. According to incomplete statistics, the operating and maintenance costs account for 25% to 50% of the total generation cost of wind turbine units [5,6]. Among different component failure rates, the yaw system failure rate is about 6.7%, and the downtime caused by it accounts for 13.3% [7,8]. Therefore, accurately identifying the abnormal status of the yaw system of wind turbine units and issuing early warnings to ensure safe and stable operation of the units is of great practical significance [9, 10, 11]. Some methods have been proposed for fault diagnosis and optimization control of the yaw system in the current stage. Reference [12] extracts the wind direction characteristics as elite factors and combines them with an optimized ant colony intelligence algorithm. This approach overcomes the limitations of the backpropagation (BP) neural network becoming stuck in local optima and improves the accuracy of processing valid data, significantly reducing the diagnosis time for yaw gearbox faults. Reference [13] conducted aerodynamic analysis of the blades and combined it with simulation methods to analyze the loads on wind turbine units under different yaw angles. The study concluded that a positive yaw deviation angle would result in larger hub loads for wind turbine units. This provides an effective reference for yaw control and protection of large-scale wind turbine units. Reference [14] utilized the ReliefF algorithm and kernel density-mean method to extract seven SCADA parameters that can reflect the operational conditions of the yaw gearbox. By combining these parameters with a neural network, the study diagnosed three operating states of the yaw gearbox: normal state, wear state, and broken tooth fault. Experimental results demonstrated that this method can effectively differentiate between different types of faults. Reference [15] analyzed the yaw data of wind turbine units using the SCADA system and proposed an economic model-based predictive control (EMPC) yaw strategy based on laser ranging. This strategy aims to evaluate the power generation and cumulative damage-equivalent load of the yaw bearing during the yaw process.