This paper proposes an analytical dynamic model for series connected wound rotor induction generator (SCWRIG) and investigates the experimental performance of SCWRIG and compares it with an ordinary induction generator (IG) at standalone operating mode. After introducing the structure of series connected wound rotor induction generator and its different modes of performance, a suitable dynamic model is proposed for this machine. The results of experimental investigations indicate that series connected induction generator and ordinary induction generator have some similar behaviors. But, SCWRIG has higher speed range without any need to gearbox and capability of operating at higher voltage levels. So it may be proposed as a new suitable candidate for wind power generation at regions with high-speed wind.

Doubly Fed induction generator (DFIG) is one of the important generators that is used in the wind turbine structures. In the past few years, DFIG-based wind turbine systems that have been installed in the onshore and offshore wind farms are greater in number, than any other generator. Since the number of references that present the DFIG designing field are limited and equations and the design process are incompletely reviewed, in this paper the complete design process of a 250kW DFIG is presented. First, the optimal design equations of DFIGs are reviewed and then a model for the study of generator operation is presented. The detailed design of the DFIG is done next, and finally the Finite Element Analysis (FEA) is used to verify the design. The results show the effectiveness of the presented design process for the DFIG.

Increasing the electrical demand causes the expansion of distributed generation sources and the interconnection of power systems. In the integrated power system, stability is one of the vital issues. The instability of the power system may cause a blackout. Therefore, the analysis of stabilities and sensitivities should be considered in power system studies. In this paper, the sensitivity analysis and eigenvalue of the induction wind generator were investigated by using a proper nonlinear model. First, the dynamic equations of the wind turbine induction generator were presented. For this purpose, the induction generator was connected to an infinite bus through a short transmission line. Sensitivity analysis was performed by changing some important system parameters. In this study, by changing the inductance and resistance of the transmission line, the variations of the induction generator were analyzed separately. Then the ratio r to x of the transmission line was investigated. Finally, the eigenvalues were considered for analysis of the case study's stability. Differential equations of the case study were solved using the Rang-Kutta order-four method with MATLAB software.

In this work, a robust nonlinear control technique of a doubly fed induction generator (DFIG) intended for wind energy systems has been proposed. The principal idea in this article is to decouple the active and reactive power of the DFIG with high robustness using the backstepping strategy. The principle of this control method is based on the Lyapunov function, in order to guarantee the global asymptotic stability of the system. Finally, we present some simulation results in order to verify the efficiency and robustness of the proposed control technique.

Self-excited induction machines seem to be the most suitable generators for wind energy conversion in remote and windy areas. Steady state analysis for such machines is essential to estimate the behavior under actual operating conditions. This paper presents a new technique for the steady-state analysis of a three-phase self-excited induction generator feeding balanced unity power factor load. Iterative technique has been used to find the generated frequency and Artificial Neural Network (ANN) has been applied to capture the nonlinear magnetization characteristics of induction machine in place of piecewise linear approximation as used by other research persons. The results have been compared with experimental results. The comparison confirms the validity and accuracy of the ANN based modeling of induction generator.

This paper presents a hybrid scheme for the control of active and reactive powers using the direct vector control with stator flux orientation (SFO) of the DFIG. The hybrid scheme consists of Fuzzy logic, Reference Signal Tracking (F-RST) controllers. The proposed (F-RST) controller is compared with the classical Proportional-Integral (PI) and the Polynomial (RST) based on the pole placement theory. The various strategies are analyzed and compared in terms of tracking, robustness, and sensitivity to the speed variation. Simulations are done using MATLAB software. The simulation results prove that the proposed approach leads to good performances such as the tracking test, the rejection of disturbances and the robustness concerning the parameter variations. The hybrid controller is much more efficient compared to those of PI and RST controller, it also improves the performance of the powers and ensures some important strength despite the parameter variation of the DFIG.

induction generators are widely used in various applications since they offer distinct advantages over conventional synchronous machines, resulting in a simplified design and installation at lower first cost and substantial savings in operation and maintenance expenses. This paper presents a Particle Swarm Optimization based procedure for optimization of self-excited cage induction generator design. The optimization problem is formulated as a nonlinear multivariable programming problem with nine independent variables and six constraints. Five different objective functions are considered for optimization in order to select a desired design. The procedure is tested on two cage induction generators and the results are compared with those obtained from sequential unconstrained minimization Technique (SUMT) in conjunction with Rosen Brock's method of rotating co-ordinates. The results prove the supremacy of Particle Swarm optimization method in terms of convergence speed and accuracy.