# Fault Detection for Pitch System of Wind Turbine-Driven Doubly Fed Based on IHHO-LightGBM
## 1 Introduction  
In recent years, governments of various countries increased the investment in the wind power industry, when the concepts of carbon neutrality and carbon peaking were put forward one after another, which led to the sharp decline in wind energy cost and the rapid development of wind energy [1]. The GWEC (Global Wind Energy Council) predicted, in its report, that 2021 will become a record year for the wind energy industry, and it is estimated that 78 GW of wind energy will be added in 2021 [2]. Moreover, by the end of 2024, the newly installed capacity on land and at sea is expected to reach 348 GW, and the cumulative installed capacity of wind power will reach nearly 1000 GW. However, the sustainable development of the wind power industry is facing a very serious problem, that is, the cost per unit of wind power energy output is higher than that per unit of fossil fuels. Therefore, in the field of new energy, it is necessary to reduce the high operation and maintenance costs, while ensuring the safe operation of wind turbines. Wind turbines are prone to failure when running in a complex environment. The pitch control system, one of the most important components of the wind turbine, is also the component with the highest failure rate [3]. The pitch control system consists of the hub andblade, which changes the angle of attack of the blade to adjust the power [4]. The changeable external wind conditions and the complex internal system structure of the pitch control system are prone to lead to abnormal output power, blade damage, and even unit collapse, resulting in a high occurrence rate of pitch motor failure and pitch bearing failure. Failure of pitch bearing would cause the machine to stop [5]. Pitch misalignment could cause performance worsening, undesired rotor loads, and severe damage of the turbine [6; 7; 8]. Once the propeller system fails, the downtime will be prolonged and the reliability of the wind turbine will be reduced, which will cause serious production losses [9]. The methods of wind turbine fault detection can be categorized in two ways, the model-based method [10] and the data-driven method [11]. Model-based fault detection technology has been applied in much research [12; 13]. The commonly used model-based fault detection methods in industry mainly contain the equivalent space method [14], parameter estimation method [15], state estimation method [16], etc. The biggest disadvantage of the model-based method is its low robustness, which depends on the current dynamic parameters of wind turbines and does not have universality. With the rise in machine learning, the data-driven method is widely used. This method uses signal processing, feature extraction, and machine learning-related knowledge to detect wind turbine faults. The data-driven method has high accuracy and good robustness. Since it does not require precise modeling, it can be freely applied to other wind turbines. The data-driven method can make use of a huge amount of offline data stored in the SCADA system [17], and the accuracy of the data-driven fault detection model will also improve with the increase in data volume. Therefore, data-driven methods have also received more attention. Through the early fault diagnosis of the pitch system, the operator can take a more active maintenance strategy [18]. At present, many mature data-driven fault detection methods have been put into practice. This topic will also focus on the data-driven wind turbine fault detection method. SVM [19] is an enduring statistical learning theory-based machine learning method. The generalization capability of the SVM is heightened by reducing the structural risk of the model, and the empirical risk and confidence range are minimized, thus achieving the purpose of obtaining good statistical laws under the condition of a small statistical sample size. The algorithm of SVM is more efficient and can fit the data effectively. In regards to the large-scale wind turbine fault diagnosis, there is also much research around SVM-based fault detection methods.