A Review of Intelligent Control Systems for Grid Tie Doubly Fed Induction Generator Based Wind Farm

This paper presents a novel technique for the optimal detection of faults in doubly fed induction generators (DFIGs), which are widely used in wind turbines. The proposed method leverages a spiking deep residual network (SDRN), a type of spiking neural network (SNN), to accurately detect and classify the operational conditions of the DFIG, distinguishing between healthy and faulty states. The primary goal of the proposed approach is to minimize errors in both fault detection and classification, improving the reliability of the protection system. The present study analyzes the effects of faults on key electrical parameters, including stator phase current, the d-component of stator current, and reactive power. These effects are quantitatively compared using a sensitivity factor, with fault indices assessed under varying fault severities, rotor speeds, and power reference levels. To evaluate the performance of the SDRN-based fault detection method, the results are benchmarked against existing fault detection techniques such as singular spectrum analysis (SSA), artificial neural networks (ANNs), and bee colony optimization (BCO). The proposed method is implemented in MATLAB/Simulink, and its performance is quantitatively evaluated. The results demonstrate that the SDRN approach significantly outperforms the existing techniques in terms of fault detection accuracy, with a reduction in both classification errors and detection errors. The proposed spiking deep residual network (SDRN) achieves an accuracy of 96.2% in fault detection for doubly fed induction generators (DFIGs), outperforming traditional methods like artificial neural networks (ANNs) and singular spectrum analysis (SSA) by 5.5% and 7.2%, respectively, in terms of classification accuracy. The error rate is reduced by 3% compared to existing approaches, demonstrating the effectiveness and robustness of SDRN in fault classification. The results highlight the advantages of using spiking neural networks over traditional methods, particularly in minimizing errors and improving fault classification performance.

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Effect of DC Link Capacitor on the Performance of Wind-Driven Double-Fed Induction Generator

Amer,

Eskander

2024

Wseas Transactions on Power Systems

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In this paper, the starting performance of a double-fed induction generator (DFIG) operating at super-synchronous speed is investigated when varying the values of the DC link capacitor. The starting transients, the settling time, and the harmonics of the stator and rotor currents and voltages are investigated as a function of the DC link capacitor Cd. Curve fitting is applied to derive mathematical equations (polynomial) relating the variation of the stator current THD with the value of the DC capacitor. Similarly, polynomials are deduced to relate the THD of the rotor current and rotor voltage with the value of the DC link capacitor. These polynomials help to design the DC link parameters that lead to minimum current and voltage harmonics of the grid-connected DFIG. The minimum THD values of the stator current, rotor current, and rotor voltage are presented. These results are repeated at different rotor speeds to deduce the optimum Cd value within a wide speed range. Also, the effect of Cd on the DC link current and voltage ripples is demonstrated at different speeds.

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A Review of Intelligent Control Systems for Grid Tie Doubly Fed Induction Generator Based Wind Farm

Cited by 3 publications

References 92 publications

Fuzzy Logic Energy Management Based on Wind Turbine for Standalone Application

Fuzzy Logic Energy Management Based on Wind Turbine for Standalone Application

A novel method for fault detection and diagnosis in DFIG coupled grid connected wind energy systems using spiking deep residual network

Effect of DC Link Capacitor on the Performance of Wind-Driven Double-Fed Induction Generator

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