# Anomaly Detection of Wind Turbines Based on Deep Small-World Neural Network
## 1 Introduction  
Changeable and severe environment is the main cause of failures for wind turbines (WT), such frequent malfunctions will inevitably lead to low availability and expensive maintenance costs [1]. In general, various types of condition monitoring sensors are installed in different WT components, and their multi-dimensional state parameters, such as wind speed, pitch angle, hydraulic oil temperature, etc., are recorded and saved by the WT supervisory control and data acquisition (SCADA) system [2]. Once an exception occurs, its fault information will be fed back in the multi-dimensional sensor parameters of the SCADA system [3], where such parameters are referred as \"SCADA data\". Hence, using the SCADA data for early anomaly detection is beneficial to achieve the condition assessment and fault warning of wind turbines. Generally, fault detection and isolation (FDI) approaches have two critical processes: (1) Extract effective features from the complex data, and (2) use prior knowledge or machine learning techniques to achieve failure classifications. It is worth noting that both steps need obvious label information (normal or abnormal labels) as the essential elements of training the intelligent classification algorithms [4; 5]. But in the actual WT operation, it is almost impossible to acquire enough label data. This is because the normal operation time is much longer than fault occurrence time, which causes the data sparseness tobe inevitable. In addition, the monitoring data and fault information of the SCADA system are mostly recorded separately, and the correlation between them only depends on manual completion after the fault occurs. Such non-intelligent recording and classifying work is huge and unrealistic. Therefore, using imperfect labels to complete FDI of the wind turbines is fraught with challenges. Deep learning methods [6] have been widely reported in data mining and intelligent fault diagnosis due to their excellent nonlinear approximation performance [7]. Compared with the shallow neural networks, the deep learning algorithms [8], such as multi-layer perceptron (MLP), deep neural network (DNN), long short-term memory (LSTM) or convolutional neural network (CNN), can mine valuable information from the data more truly and effectively through the real imitation of the human learning process. [PERSON] and [PERSON] [9] proposed a multi-scale convolutional neural network to effectively learn the high-level fault features and obtain rich diagnostic information on different vibration frequencies. [PERSON] et al. used the LSTM-based recurrent neural network (RNN) to mine the spatial or temporal relationships hidden in SCADA signals, so as to realize the fault classification of WT gears [10]. Faced with a large amount of textual data collected in the SCADA system, recent research work has innovatively transformed them into image or map form and then used image recognition algorithms to detect and identify anomalies. For instance, [PERSON] et al. converted the vibration signal into time-domain spectrums, and then applied the CNN to extract the fault features [11]. [PERSON] [12] first arranged the multidimensional SCADA data and then used a window function to slide to intercept a digital matrix of equal length and width. Each value in this digital matrix is equivalent to the pixel value in an image. On this basis, the multilayer CNN is used for deep-level anomaly detection. The methods mentioned above provide more possibilities for the realization of WT fault diagnosis. However, the implementation of the above networks requires a supervised learning environment with sufficient labels, which is a gap and difficult to achieve in practical applications. The techniques of using unsupervised learning in FDI are also relatively mature. The corresponding algorithms are mainly deep learning networks [13] constructed with Restricted Boltzmann Machine (RBM) or Auto-encoder (AE).