## 2 Related Work  
The enhancement of monitoring, management and control of WTs has become an essential key to guarantee better quality of the grid injected power. In recent years, many studies have been carried out to improve the control of the wind energy conversion system (WECS) and develop power extraction techniques. These techniques could be evaluated depending on the requirements of each method, such as wind speed information, turbine parameters, use of sensors, simplicity of implementation and robustness. The majority of studies found in the literature [14; 15; 19] mainly deal with MPPT strategies. Each method has its specific advantages and drawbacks. For example, the TSR algorithm is a simple control method that requires keeping the TSR at the optimum value by regulating the rotational speed of the generator. It provides efficient results and a quick response. Nevertheless, its robustness decreases when the wind speed suddenly changes. In addition, this method requires previous knowledge of power coefficient (TSR characteristic curve) and also the use of an accurate sensor for wind and turbine speeds, which increases the cost of the system, especially for SSWTs. Concerning the MPPT based on OT, the PMSG torque is controlled and set to its optimal value [14; 15; 16]. This optimal reference is obtained according to the WT's maximum power at a given wind speed. This strategy provides a smooth output power with slow transient responses. Despite its robustness and effectiveness, it cannot not be generalized to all turbines because it requires specific characteristics of each WT. Contrary to the previous techniques, some enhanced methods are developed with no need to know the turbine characteristics, wind measurement or mechanical speed sensors [14; 15; 17]. The fuzzy logic controller (FLC) is one of those methods that can be used independently or along with other methods. According to the results reported in [20; 21], the FLC can provide a fast response and a quick change in parameters when climatic conditions change. Despite the robustness and effectiveness of this method, it requires a large memory for resolution during fuzzification and defuzzification stages and prior knowledge of the results in order to choose the rule base, the levels of membership function and the appropriate error. With the development of soft computing technologies, MPPTs based on ANNs are becoming more and more popular. ANN techniques offer three types of layers: input, hidden and output layers. The ANN's architecture (number of neurons in each layer, number of layers, etc.) is chosen depending on the task to learn. For a WT system, the input variables can be wind speed, rotor speed, output torque, etc. The output can be a reference signal such as reference power, reference torque, etc. This output is used to operate the converter at the maximum power point (MPP). The authors of [22; 23] show sensorless MPPT algorithms based on ANN techniques. The inputs are the rotor speed and the output power of the turbine while the output is the optimal rotor speed. The latter is used to control the generator and then to obtain the optimum power. This ANN-based controller decreased the response time with the ability to provide a smooth power transition during wind speed variations. Furthermore, the most used MPPT methods to target the MPP for SSWTs are PSF and P&O. Regarding MPPT-PSF, this method is based on an optimum relationship. It requires parameters of a specific WT such as mechanical power equation or maximum power curve [2]. In these work [24; 25],an example of controllers based on optimum relationships was reported, such as the output power with the rotational speed or the torque with the rotor speed. These relationships make the system more expensive (by using mechanical speed sensors) and complex (by using estimators for rotor speed). However, some studies highlight that the use of the DC bus's parameter in SWECS, equipped with a PMSG and a diode rectifier, makes the system more reliable with no need to use a mechanical sensor.