Abstract

This chapter presents a general framework for the doubly fed induction generator (DFIG). We apply and analyze the behavior of three estimation techniques, which are the unscented Kalman filter (UKF), the high gain observer (HGO) and the moving horizon estimation (MHE). These estimations are used for parameters estimation of the doubly fed induction generator (DFIG) driven by wind turbine. A comparison of those techniques has been made under different aspects notably, computation time and estimation accuracy in two modes of operation of the DFIG, the healthy mode and the faulty mode. The performance of the MHE has been clearly superior to other estimators during our experiments. These estimation tools can be used for monitoring purposes.

Keywords: doubly-fed induction generator, high gain observer, unscented Kalman filter, moving horizon estimation, parameters estimation, monitoring

## 1. Introduction

Nowadays most of generated electricity comes from nonrenewable sources of fuel. These products transfer to the atmosphere important quantities of CO2, and inescapably leading to the warming up of the atmosphere [1]. The production of the wind energy spreads through the world, and significantly, it imposed itself during the past decade [2]. Doubly-fed induction generators (DFIGs) are actually the most used wind power generators in many countries [3].

Therefore, many contributions have been made to the inverters and converters usually in DFIG used in the power electronics domain [4]. A doubly fed induction generator model for transient stability analysis has been proposed in [5], in which authors focused their study on the control loops of instantaneous response. In [6], authors have been proposed some robust observers to estimate states and actuator faults for different class of linear and nonlinear systems at the same instant. Though systems are becoming more and more complex, DFIG can be subject by many types of faults [7], diagnosis and faults estimation issues have become primordial to ensure a good supervision of systems and guarantee the safety of materials and operators (humans) [8].

A survey based on current sensor fault detection and isolation and control reconfiguration current for doubly fed induction generator has been proposed by [9]. Studies led by [10], have contributed to an adaptive parameter estimation algorithm used for estimating the rotor resistance of the DFIG, however, the others 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 instance.

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