# Damage Detection in Wind Turbine Blades Based on an Improved Broad Learning System Model
## 1 Introduction  
In recent years, the development and utilization of renewable energy has been highly valued by many countries. Wind energy is a new and renewable energy source with great value for exploitation and has become the fastest growing renewable energy source in the world. It is gradually occupying an important position in the global energy structure and is highly competitive with traditional energy sources. Figure 1 shows the global cumulatively installed wind power capacity and growth rate from 2001 to 2020, as compiled by the Global Wind Energy Council (GWEC) and Weber Consulting [1]. Over the last two decades, global wind power has grown rapidly. Cumulatively installed wind power capacity has increased from 24 GW to 743 GW, with a compound annual growth rate of over 20%. Since 2020, it has remained on track despite the impact of COVID-19. Wind turbines are moving towards being developed on a large scale, as the number of installed wind turbines grows. By 2030, the globally installed capacity of wind power will exceed 2000 GW [2]. Large wind turbines place higher demands on the safe and reliable operation of their components [3]. As size and power ratings increase, the analysis of support structures and blades subjected to wind loads become increasingly important [4]. Wind turbine blades are a crucial component for capturing wind energy. Wind turbine blades need to be manufactured from materials with excellent characteristics due to their working environment. Despite all the effort that has been put into the material of the blades, the surface is inevitably subject to various defects due to the constant exposure to storms,sand, lightning, rain and snow. The cost of wind turbine components accounts for around 20% of the overall installed cost [5], which is high. Wind turbines could suffer from varying degrees of damage during operation. If not repaired in time, this could have a serious impact on the cost control and output efficiency of wind power generation and could even cause serious accidents [6]. The prevention and identification of defects in the blades of wind turbines in advance could reduce the incidence of accidents and economic losses. With the development of neural network technology, deep learning algorithms have received increasing attention from academic and industrial fields in the processing of images [7; 8]. [PERSON] et al. [9] proposed a new convolutional neural network (CNN) model with the classifiers of migration learning and integrated learning to improve the accuracy of defect detection in the blades of wind turbines. [PERSON] et al. [10] selected VGG-11 as a damage recognition model for wind turbine blades and used the algorithm of the alternating direction method of multipliers (ADMM) to compress the model to reduce the requirements on the hardware devices. [PERSON] et al. [11] proposed a new CNN developed on the basis of the LeNet-5 neural network to detect the damage types in rotating machinery. A detection method combining a back propagation (BP) neural network and a support vector machine was proposed by [PERSON] as the machine classifier [12]. The experimental nature of deep learning methods usually requires a large amount of image data to provide the foundational support. The large amount of data and the slow running processes have become the biggest problems for deep learning. The BLS was proposed by [PERSON] in 2017 [13; 14]. It is regarded as the replacement for the deep learning structure. The problems of time consumption and complex structures in deep learning models are solved by the BLS. [PERSON] proposed that convolutional and pooling operations should be used to improve the feature extraction capability of traditional BLSs for images to reduce the training time [15]. The intrinsic graph with the BLS (IGBLS) and the intrinsic penalty graph with the BLS (IPGBLS) have been proposed by [PERSON] to further mine image features [16].