Advanced Monitoring of Wind Turbine DOI: http://dx.doi.org/10.5772/intechopen.84840


#### Table 1.

parameters were assumed to be constant. To improve the extended Kalman filter (EKF), a new nonlinear filtering algorithm named the unscented Kalman filter (UKF) has been developed in [11]. Widely used in some fields, UKF has been found in several studies such as training of neural networks [12], multi-sensor fusion for

This chapter investigates the usage of the unscented Kalman filter UKF, high gain observer (HGO) and the moving horizon estimator (MHE) to estimate the dynamic states and electrical parameters of the wind turbine system. These estimates can be used to enhance the performance of doubly fed induction generator in power systems, for rotor and stator resistances faults in the circumstances where internal states will be involved in a control design [3] and the acquisition of internal states, which are relatively difficult to get can realized from the dynamic state estimation and for monitoring purposes. The chapter is organized as follows: in Section 2, the mathematical model for DFIG is presented, followed by the description of estimation algorithms in Section 3. The results of the parameter estimation

In this section, we deal with the mathematical modeling of the DFIG-based wind energy system, we will only describe the wind turbine (also called drive train), and the asynchronous generator (also called induction generator) because this chapter focuses on estimating of the parameters and dynamic states of the DFIG Figure 1. Two frames of reference are used in this model: stator voltage (d-q) reference frame and mutual flux (d-q) reference frame. In Tables 1 and 2, all parameters and

From the wind, the power extracted can give the mechanical torque. The energy from the wind is extracted from the wind turbine and converted into mechanical power [14]. The wind turbine model is based on the output power characteristics, as

tests are presented in Section 4. Finally Section 5 gives the conclusions.

2. Mathematical model for DFIG

Wind Solar Hybrid Renewable Energy System

2.1 Modeling of the wind turbine

Configuration of DFIG-based wind turbine system [13].

constants are given.

Eqs. (1) and (2), [15].

Figure 1.

24

instance.

Parameters of the DFIG.


#### Table 2.

Parameters of the wind turbine.

$$P\_m = \mathbb{C}\_p(\lambda, \beta) \frac{1}{2} \rho A \nu\_w^3 = \mathbb{C}\_p(\lambda, \beta) E\_w \tag{1}$$

$$
\lambda\_{\rm TS} = \frac{Ro\_{\rm l}}{\nu\_w} \tag{2}
$$

where the aerodynamic extracted power is Pm, which depends on CP, the efficiency coefficient,the air density ρ, the turbine swept area A, and the wind speed νw. The kinetic energy contained in the wind at a particular wind speed is given by Ew. The blade radius and angular frequency of rotational turbine are R and wt, respectively. CP(λ; β) the efficiency coefficient depends on tip speed ratio λTS and blade pitch angle β, determines the amount of wind kinetic energy that can be captured by the wind turbine system [13]. CP(λ; β) can be described as:

$$\mathbf{C}\_{p}(\boldsymbol{\lambda}, \boldsymbol{\beta}) = \mathbf{0}.5 \mathbf{\hat{s}} \left( \frac{\mathbf{116}}{\lambda\_{i}} - \mathbf{0}.4\boldsymbol{\beta} - \mathbf{5} \right) e^{-2\mathbf{1}/\lambda\_{i}} \tag{3}$$

where

$$\frac{1}{\lambda\_i} = \frac{1}{\lambda\_{\rm TS} + 0.08\beta} - \frac{0.035}{\beta^3 + 1} \tag{4}$$
