Browsing by Author "LHR TP 8686"

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Diagnosis of Doubly Fed Induction Generator Inter-Turn Faults (ITF) in Wind Energy Conversion System
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Anisa Sharif; SP20-REE-014; LHR TP 8686
The most often utilized wind turbine generators right now are Doubly Fed Induction Generators (DFIG). When compared to other generators, it offers significant benefits such as low mechanical stress, excellent system efficiency, and a variable speed constant frequency mechanism. Mechanical and electrical faults are the two categories into which DFIG faults exist. The stator inter-turn fault is one of the stator problems that are not detectable by traditional protective devices. This study focuses on the identification of stator inter-turn short circuit (ITSC) Faults in DFIG since it accounts for more than one-third of all DFIG faults. Because the DFIG system is nonlinear, estimating its states without linearizing the system is challenging. Many approaches, including adaptive extended Kalman filtering, Wavelet Transform, Hilbert-Hang Transform, and Neural Networks can be utilized to identify stator inter turn faults and produce reasonable results. These approaches are less precise and efficient because some of these techniques linearize the DFIG nonlinear model for fault identification. The Unscented Kalman Filter (UKF) is really a derivative-free strategy for nonlinear systems that outperform linearization-based Kalman filter algorithms in fault detection approaches. Because the UKF is a nonlinear estimate approach that solves directly without linearization, it produces more accurate results than the Extended Kalman Filter (EKF). In this contribution, the UKF is implemented to detect stator inter-turn faults in the DFIG. By creating and examining the electrical currents residual from the stator, the fault is detected. The outcomes of the simulation demonstrated how well the suggested technique performs in both the diagnosis of a high and a low value of the ITSC fault in the generator stator windings. Firstly, by applying the state space model equations we created both healthy and defective DFIG-based wind turbine models in Matlab/Simulink, and then we used the UKF to identify inter-turn faults and describe the UKF estimation response. Simulation results of the UKF and EKF are compared for performance Analysis. For the implementation of the proposed scheme, the state space model of DFIG in the dq-reference framework is used and the simulations are carried out in the MATLAB/Simulink environment. Although, the trade-off between accuracy and speed of estimation response has played an important role in deciding the most effective technique.