# Deep Learning Method for Fault Detection of Wind Turbine Converter
## 1 Introduction  
In recent years, the penetration of wind energy into the whole energy market is constantly growing. The installed capacity worldwide reached 650.8 GW by the end of 2019 with a yearly average increase rate of more than 10% in the past 10 years [1]. Wind energy is captured using wind turbines. However, most wind turbines are operating in a very harsh environment. As a result, wind turbine health condition assessment has become increasingly important for the purpose of realization of condition-based maintenance and operation. In a wind turbine, the power drive-train system usually has a higher failure rate than others. As a critical component in the power drive-train system, the converter plays a roleto transmit the generated power to grid. The power converter realizes the conversion and control of the generated wind power. It converts the electrical energy that changes with fluctuation by following the wind speed into energy with stable frequency complying with the requirement of the grid. Grid-side converter voltage failure is the most influential converter failure in a wind farm. When it occurs, the grid voltage, current, and grid-side active and reactive power will produce secondary harmonics. Wave pulsation causes the voltage of the capacitor terminal on the DC side to be unstable, the operation performance of the grid-connected converter is reduced and the power loss increases. In severe cases, the main power device may be burnt, and the asymmetry of the grid voltage will be further aggravated [2]. The research work reported in this paper is focused on the faulty status detection of the Double-fed Induction Generator (DFIG) wind turbine converter in order to avoid unnecessary shutdowns of the wind turbines caused by converter failure. Regarding fault detection and diagnosis for wind turbines, there are different models and methods developed which can be generally categorized as model-based [3; 4; 5; 6] and data-based [6; 7; 8; 9; 10; 11; 12; 13] approaches. The model-based approach is mainly used for fault mechanism analysis, and the data-based approach is for condition assessment and prediction. The data-based approach is adopted in this paper using the Supervisory Control and Data Acquisition (SCADA) system data of wind turbines. In [14], a review of research on fault detection and condition monitoring using SCADA data is provided, with some discussions on the potential research trend in the future. Using SCADA data for wind turbine fault detection and diagnosis, different modeling techniques have been developed and utilized. They are generally classified as artificial neural networks (such as back-propagation, radial basis function, auto-associative, multilayer perceptron and concurrent and convolutional neural networks), Support Vector Regression (SVR), Support Vector Machine (SVM) [15; 16], Bayesian inference (such as Bayesian network, Naive Bayes), fuzzy logic and fuzzy inference (such as fuzzy inference system, fuzzy logic system and adaptive neuro-fuzzy inference system), Principal Component Analysis (PCA), Self-Organizing Feature Map (SOFM), expert systems, k-means clustering, decision trees and wavelet-based techniques [6; 10; 17; 18; 19; 20; 21; 22; 23]. In [23], a brief summary of the application of these techniques is given. In [12], an in detail review of these techniques from the perspective of deep learning was conducted. One review of machine learning methods for wind turbine condition monitoring is proposed by [PERSON] et al. [26], where various techniques applied to regression and classification are discussed. From these reviews, it is perceived that the application of deep learning techniques to wind turbine fault detection and diagnosis will represent a strong research and application trend for the operation of wind turbines; see, for example, refs. [27; 28; 29; 30; 31; 32] published in the last two years.