### Literature Review
The article provides detailed models and experimental validations performed in real microgrids, highlighting the fundamental role of power control and the coordination between different protection levels. The analysis is based on experiences gathered in European systems, emphasizing the need to establish specific standards for designing and operating protections in microgrids. For its part, the work by [15] offers a comprehensive review of fault detection techniques and protection schemes applicable to networks with distributed generation, addressing technical challenges such as the bi-directionality of power flow and the high variability of renewable sources. A classification of existing methods is proposed based on accuracy, computational complexity, and scalability criteria. The results reveal that artificial intelligence-based techniques, combined with phasor synchronization systems (PMU), offer superior performance in speed and reliability. Based on the analysis of 20 case studies, a methodological framework is proposed for the comparative evaluation of protection schemes, also integrating considerations for their interoperability with SCADA platforms. The study concludes with suggestions for improving the robustness and reliability of these solutions in real-world environments. Finally, the classic study by [16] addresses protection problems in distribution networks with dispersed generation, highlighting how the presence of distributed sourcessignificantly alters the coordination of protection devices. Fault currents are modified by the effect of DG units, which can reduce the effectiveness of conventional schemes. Proposed solutions include directional protection, dynamic parameter adjustment systems, and adaptive protection strategies. The analysis was based on simulations of real medium-voltage networks, where recurring conflicts in relay coordination were also identified. This work has served as the basis for subsequent investigations focused on protecting microgrids and hybrid systems. Several recent studies have explored advanced artificial intelligence techniques for classifying power quality events. In [17], a review focuses on machine learning and deep learning algorithms applied to electrical signals. Approaches based on convolutional neural networks (CNNs), support vector machines (SVMs), and hybrid methods are highlighted, demonstrating accuracies above 95% in identifying events such as interruptions, transients, and harmonic distortions. The study used real databases and synthetic datasets generated for training and validation. The robustness of the models to noise and data variability was also analyzed, identifying the need for large volumes of labeled data as the main challenge. Strategies such as federated learning and knowledge transfer (transfer learning) are proposed as a future projection to mitigate these limitations. In an innovative line of work, the work in [18] employs topological tools, specifically simplicial complexes and visibility graphs, for fault classification in distribution systems. These techniques were applied to multivariate time series under noisy conditions, achieving an accuracy of 98.7% in classifying symmetric and asymmetric faults. The model proved highly robust to nonlinear distortions and was validated with simulated data and real records. Due to its low computational requirements, its application in real-time automated protection systems is suggested. On the other hand, ref. [19] proposes a method for early prediction of faults in electric motor bearings (CMOR) based on variational modal decomposition (VMD) combined with the autocorrelation function. This technique was optimized to improve extracting relevant signal features from critical systems. The methodology demonstrated high accuracy in predicting imminent faults, surpassing the performance of classical techniques such as the fast Fourier transform (FFT) and wavelet. Its non-invasive nature and compatibility with industrial monitoring platforms make it an effective solution for predictive maintenance schemes.