The integration of renewable energy sources into microgrids introduces significant challenges in protection, stability, and reliability. Reliable protection of renewable integrated microgrids remains a major challenge due to the presence of harmonics, switching transients, and noise contamination that compromise conventional fault detection schemes. The work in this paper suggests the integration of zero sequence current (ZSC) detection, differential faulty energy (DFE), variational mode decomposition (VMD), and support vector machine (SVM) classification into a novel network fault current signal protection algorithm. The values of the ZSC and DFE indices offer fault signatures and noise, while VMD efficiently decomposes the signals under noisy conditions to extract robust features. These features are further analysed using an SVM supported supported classifier for fault analysis under diverse conditions. Simulation research is performed on a renewable integrated microgrid model with diverse fault scenarios and noise levels. The outcome indicates that a novel scheme provides excellent detection performance, robustness to noise, and better reliability, accuracy, and classification efficiency than typical approaches. The simulation results of the proposed framework show that the proposed scheme achieves 97.2 % fault classification accuracy, detects ground faults down to 0.7 A zero sequence current, and remains robustness at noise levels as high as 39 dB, providing a 20 % improvement in noise-resilient detection performance compared to conventional approaches. Read More