# Deep Learning Method for Fault Detection of Wind Turbine Converter
## 1 Introduction
### Methods Based on Signal Processing  
This kind of method extracts fault features by analyzing the mean value, frequency and harmonics of sensor signals to realize fault detection and diagnosis. In this category, the methods are mainly divided into current signal-based and voltage signal-based methods.  
Fault diagnosis by analysis of current signal  
The average current Park's vector method is proposed in [36], where the average value of the three-phase current is transformed to the average current Park's vector by Clark transform. Under normal and fault conditions of the system, the faulty power device is located by analyzing the replication and phase difference of the average current Park's vector. In [37], a method is introduced for the diagnosis of DFIG back-to-back converter open circuit fault, which can realize fault detection and fault location. For fault detection, it gives an absolutely normalized Park's vector method. When the wind turbine generator is running at a synchronous speed, this method can detect multiple open-circuit switch faults and ensure that they are free from false alarms. For fault location, this method uses the normalized current average value to identify single-circuit and double-circuit open-circuit faults. In [38], converter fault occurrence is identified by detecting the current Park's vector phase angle change. The fault is located based on the Park's vector phase angle interval of the average current on the machine side converter and the positive and negative of each phase current on the grid side converter. In view of the fluctuation and high noise of wind speed, in [39], a diagnosis algorithm is proposed based on the Park current amplitude normalization method by reference to wind speed. Combined with the current trajectory, the fault diagnosis of a permanent magnet synchronous wind turbine power converter can be achieved under variable wind speed.  
Fault diagnosis by analysis of voltage signal  
In [40], a method is presented for the diagnosis of open circuit fault of an inverter by measuring and comparing the three-phase output voltage of the inverter and the midpoint voltage at the DC side. For the three-level topology, however, this method can only judge the faulty phase but cannot locate the faulty element. In [41], an inverter phase voltage observer is established. It is used to diagnose open-circuit faults of a converter by comparing the observed voltage with a reference voltage, but the accuracy of the observer is greatly affected by generator parameters. In [42], the operating characteristics of various voltages when the permanent magnet synchronous wind turbine power converter has an open circuit fault are revealed through detailed theoretical derivation. In [43], the deviation of the line voltage before and after the converter fault of the permanent magnet synchronous wind turbine is used to diagnose and locate the fault. The fault diagnosis reliability is improved by satisfying both the voltage amplitude and time width criteria. In [44] a new switch fault diagnosis method which treats output inductor voltages as diagnostic criteria is developed. Based on the features of diagnostic signals in such a system, a low-cost diagnostic circuit is designed. The failed module caused by open-circuit or short-circuit fault could be detected within one switching period, allowing for an immediate fault-tolerant action.  
The fault diagnosis method based only on current or voltage signal has certain limitations in application. First of all, the electronic control system is relatively independent and compact; it is not easy to add additional sensors or data acquisition units unless it is redesigned. It is also required that the monitoring and diagnosis system cannot interfere with the normal operation of the electronic control system. Second, relying only on a single signal for diagnosis will increase the missed fault detection and false detection rate due to signal loss and signal interference.