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

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.