# Deep Learning Method for Fault Detection of Wind Turbine Converter
## 1 Introduction
### Data-Driven Methods  
The data-driven method does not require the precise mathematical model of the diagnostic object, nor does it need to add extra sensors or hardware circuits. It is widely applied to the fault detection/diagnosis of the converter. The typically applied techniques include neural networks, expert systems, support vector machine (SVM), fuzzy logic and cluster analysis [45; 46; 47; 48; 49]. In [45], wavelet transform is used to obtain three-phase current fault characteristics, where the artificial neural network algorithm or fuzzy expert system is employed to complete classification of failure modes from which the open circuit fault of the converter is diagnosed. In [41], a summary of a variety of diagnostic methods for open circuit, short circuit and drive signal loss for converter IGBT module failure is provided and compared to the effectiveness and anti-interference abilities of the methods. In [46], a data-driven fault diagnostic method using long short-term memory (LSTM) network to detect multiple open-circuit switch faults of the back-to-back converters used in DFIG wind turbines is presented. In [47], an advanced Fault Detection and Diagnosis (FDD) approach aiming to increase the availability, reliability and required safety of wave energy converters (WEC) under different conditions is described. The developed approach exploits the benefits of the Machine Learning (ML)-based Hidden Markov Model (HMM) and the PCA model. To improve the accuracy of fault diagnosis for wind turbine converters, a fault feature extraction method combined with a wavelet transform and compressed sensing theory is proposed in [48], in which an improved AdaBoost-SVM is developed and used for fault diagnosis. The three-phase output current signal is selected as the research object and is processed by the wavelet transform to reduce the signal noise. In [49], fault detection was conducted for three wind turbine subsystems, including a pitch system, generator and converter by developing SVR, SVM and convolutional neural network (CNN) models using SCADA system data. It is verified that the CNN model's performance is superior to SVR and SVM models. In view of the literature review above, the following conclusions can be drawn:  
1. The model-based fault diagnosis method has higher diagnosis speed and lower cost, but it is sensitive to system parameters so that its practical application is limited. 2. The current signal-based fault diagnosis method does not require new hardware to be added, but its diagnosis speed is lower, and it is easily affected by noise disturbance. The voltage signal-based method has fast diagnosis and high accuracy, but it requires additional hardware circuits, such as voltage sensors, which increases the system cost and complexity. Usually, it is not suitable to add new sensors to a wind turbine control system unless it is redesigned. The fault diagnosis performance is not sufficient, and it is not stable if only one signal is used. 3. The data-based method does not require the establishment of an accurate mathematical model and has certain advantages for complex systems, but the algorithm is relatively complicated. Nowadays, with the fast improvement in computational capacities and large volumes of high frequency and multi-dimensional parametric data recorded, new opportunities are created that make it possible to take advantage of deep learning techniques in fault detection and diagnosis for wind turbines. Based on a quick literature review, it was found that the future techniques applied to fault detection and diagnosis of wind turbines will be driven by deep learning approaches and algorithms. Motivated by this finding, this paper focuses on fault detection of wind turbine power converters using convolutional neural network models which are one kind of important model in deep learning.