# Fault Detection for Pitch System of Wind Turbine-Driven Doubly Fed Based on IHHO-LightGBM
## 1 Introduction
A multi-sensory system based on SVM is proposed by [PERSON], [PERSON] et al. [20], which achieved good results in wind turbine misalignment and imbalance faults. [PERSON], [PERSON] [21] optimized SVM by using the cuckoo algorithm, which can address the problem of the poor detection performance of SVM, when multiple faults occur. [PERSON], [PERSON] et al. [22] applied SVM to wind turbine power curve modeling, to evaluate the performance of wind turbines. In this study, the advantages and disadvantages of the proposed nonparametric technology are emphasized, to construct a fault detection algorithm for a wind power generation system based on the power curve. However, when faced with multidimensional data, the performance of SVM begins to decline. Random forest [23] is easy to implement, fast to run, and shows strong performance in real-world tasks. [PERSON], [PERSON] [24] combined random forest with XGboost, on the basis of using the most advanced wind turbine simulator FAST, which avoids the over-fitting issue of a single algorithm in multidimensional data. [PERSON], [PERSON] et al. [25] put forward a multi-domain characteristic fault detection device based on random forest and complex empirical mode decomposition. The fault features of signals can be effectively extracted by this method and the fault diagnosis of wind turbines can be realized with higher fault detection accuracy than traditional classification methods. [PERSON], [PERSON] et al. [26] put forward an RF-based fault detecting method, which combines the short-term memory network of the residual generator with RF algorithm decision-making. The early malfunction of a machine in arduous conditions can be significantly detected by this method. The gradient boosting decision tree is proposed by [PERSON] [27], which has good robustness against nonlinear data. [PERSON], [PERSON] et al. [28] used XGBoost as the basis for modeling, which improves the accuracy of the wind speed forecast and the generalization ability of the model. [PERSON], [PERSON] et al. implemented a stacking model based on RF, XGBoost, and GBDN, to distinguish the hitch of the gearbox, and solves the problems of traditional lifting algorithms, containing inefficiency, low accuracy, and bad timely function when processing huge amounts of engineering data of wind turbine operations. However, the role of parameter selection in the data-driven fault detection model is not able to be ignored, and a parameter optimization algorithm is needed to reach the global optimal function model in time. In view of the significance of the class-imbalanced data, [PERSON], [PERSON] et al. [29] put forward a cost-sensitive large margin distribution machine. [PERSON], [PERSON] et al. [30] proposed an improved grey wolf optimizer for high-dimensional data processing. [PERSON], [PERSON] et al. [31] used improved practice swarm optimization to mend the performance of multiple classifiers. [PERSON], [PERSON] et al. [32] optimized the LDM model by using the state transition algorithm, and achieved good classification results. The Harris Hawks optimizer is a nature-inspired optimization algorithm. The algorithm was inspired by the team collaboration and hunting manner of the Harris eagle population in America. The algorithm not only has strong global search ability, but also has the advantage of having less parameters to be adjusted. Compared with other established meta-heuristic technologies, the HHO algorithm is also quite competitive, and will bring good performance in industrial fields. In order to address the problem of difficult optimization for the fault detection parameters of the wind turbine-based pitch control system, a novel element-based optimization algorithm, combined with the LightGBM algorithm, is applied in this article. An improved HHO is used to select the optimal parameters of LightGBM, and a wind turbine fault detection based on LightGBM, optimized by improved HHO, is proposed to improve the fault detecting ability.