# Fault Detection for Pitch System of Wind Turbine-Driven Doubly Fed Based on IHHO-LightGBM
## 6 Conclusions  
In view of the difficulty in optimizing parameters, due to the multi-dimensional characteristics of the fault detection model of the wind turbine-based pitch control system, an improved HHO-LightGBM-based fault detection method is proposed, by analyzing and comparing the shortcomings of traditional algorithms. The accuracy of fault detection is improved. The innovations are mainly embodied in the works as below:  
1. The trigonometric function model is introduced into HHO to update the prey escape energy, so as to balance the global exploration ability and local development ability of the algorithm, and to overcome the problem that the original HHO algorithm is easy to fall into local optimization;
2. A fault detection method of the wind turbine-based pitch control system, based on improved HHO-LightGBM, is proposed. In this model, the false alarm rate of fault detection is used as the fitness function, and two parameters of LightGBM are used as the optimization objects of the improved Harris Hawks optimization algorithm, to optimize the parameters, so as to achieve the global optimal parameters to improve the fault detection model performance and apply it to the actual fault detection of the pitch control system.  
Different types of pitch control system fault samples are selected from the experimental data, and the evaluation indexes are the FNR, FAR, and F1-Score. It is shown, in the comparison results, that the improved HHO-LightGBM has significantly improved the detection performance of the multi-type pitch control system faults, and has lower FAR and FNR than the other three methods.  
In the face of various unbalanced datasets, the improved HHO-LightGBM has good performance. In terms of the fault detection of wind turbines, this method can effectively reduce the fault incidence and improve the operation stability of wind turbines. At present, a single algorithm can no longer meet our demand for improving fault detecting ability, and there will be more excellent combined algorithms in the future.  
Figure 8: F1-score of four algorithms for fault detection.  
**Author Contributions:** Writing--review and editing, conceptualization and methodology, nomenclature, M.T.; software, validation and original draft, data curation and visualization, Z.P.; writing--review and editing, H.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 62173050 and 61403046), the Natural Science Foundation of Hunan Province, China (Grant No. 2019J40304), Changsha University of Science and Technology \"The Double First Class University Plan\" International Cooperation and Development Project in Scientific Research in 2018 (Grant No. 2018 IC14), the Research Foundation of the Education Bureau of Hunan Province (Grant No.19K007), Hunan Provincial Department of Transportation 2018 Science and Technology Progress and Innovation Plan Project (Grant No. 201843), Energy Conservation and Emission Reduction Hunan University Student Innovation and Entrepreneurship Education Center, Innovative Team of Key Technologies of Energy Conservation, Emission Reduction and Intelligent Control for Power-Generating Equipment and System, CSUST, Hubei Superior and Distinctive Discipline Group of Mechatronics and Automobiles (XKQ2021003 and XKQ2021010), Major Fund Project of Technical Innovation in Hubei (Grant No. 2017 AAA133), Graduate Scientific Research Innovation Project of Changsha University of Science & Technology (No. 2021-89).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author, upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.