## 2 Methodology
### Methodology Algorithm
Additionally, while this study focused on the voltage response at the point of disturbance injection, it is important to note that power quality events often propagate to adjacent buses, especially in radial networks. These neighboring locations may experience more severe or earlier voltage deviations due to impedance paths and electrical proximity. As such, future work will explore spatial feature integration by capturing the voltage behavior of adjacent buses to enhance detection accuracy and resilience. This approach will support distributed sensing schemes and better reflect real-world deployment scenarios in active distribution networks. The proposed methodology was designed with implementation feasibility in mind. By leveraging a lightweight neural architecture and using HHT-derived statistical features, the framework avoids the high computational overhead typical of more complex models such as deep CNNs or ensemble classifiers. This makes it suitable for deployment on embedded platforms or edge devices for real-time monitoring applications. While the current validation is based on simulation, the approach is modular and can be adapted to process real-time voltage signals from existing smart meters or phasor measurement units (PMUs). While the classifier achieved high accuracy (94.09%) and consistent recall across all event types, future studies could explore real-time adaptation and alternative network architectures to enhance robustness and reduce latency in practical deployment scenarios. Integration efforts will also focus on hardware-in-the-loop validation and compatibility with modern grid monitoring infrastructure, paving the way for scalable, accurate, and responsive power quality monitoring in active distribution networks. Finally, we propose integrating the model into real-time monitoring platforms, such as SCADA systems or embedded environments (such as Raspberry Pi or FPGA), to enable continuous monitoring of electrical service quality. This implementation would not only enable efficient disturbance detection but also facilitate the development of automatic response schemes that respond promptly to anomalous events. Together, these future directions position the proposed approach as a solid foundation for designing intelligent protection and diagnostic systems in modern distribution networks. **Author Contributions**: J.V. : conceptualization, methodology, validation, writing--review and editing, data curation, formal analysis. M.J.: conceptualization, methodology, validation, writing--review and editing, data curation, formal analysis. D.C.: methodology, validation, writing--review and editing. All authors have read and agreed to the published version of the manuscript. **Funding**: Universidad Politecnica Salesiana and GIREI supported this work--Smart Grid Research Group under the project \"Optimization of Energy Dispatch in Block H of the Salesian Polytechnic University, Quito South Campus, through a Predictive Consumption Model and Hybrid Management between Solar Panels and the Electric Grid\" approved and founded by resolution 005-01-2025-02-07. **Institutional Review Board Statement**: Not applicable. **Informed Consent Statement**: Not applicable. **Data Availability Statement**: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. **Conflicts of Interest**: The authors declare no conflicts of interest. ## Abbreviations

The following abbreviations are used in this manuscript:

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