## 2 Methodology
### Methodology Algorithm
As the ANN relies on features derived from the Hilbert-Huang transform--namely, the instantaneous amplitude and frequency extracted from the first two IMFs--the HHT remains an integral part of the inference pipeline. However, due to the localized and adaptive nature of the EMD-Hilbert process, feature extraction can be performed efficiently, supporting the feasibility of deploying the proposed method in real-time disturbance monitoring systems. While simulating simultaneous active disturbances on all three phases of the 33 node IEEE system, the model consistently demonstrated accurate classification devoid of errors. The ability to sustain this level of performance illustrates the capacity of the system to function in highly dynamic scenarios, which is necessary in modern distribution systems that are subject to high-load fluctuations and transients. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Year** & **Methodology** & **Test System** & **Accuracy (\%)** & **Remarks** \\ \hline [PERSON] et al. [50] & 2021 & S-Transform + Convolutional Neural Network (CNN) & Simulated POD dataset & High & Robust classification using S-Transform, tolerant to noise and distortion \\ [PERSON] et al. [51] & 2020 & DWT + Hierarchical Extreme Learning Machine (H-ELM) & Synthetic signals & Not specified & Multi-level structure improves generalization and handles imbalanced data \\ [PERSON] et al. [52] & 2023 & VMD + CNN with noise injection & Simulated signals with noise injection & Not specified & Emphasized complex transient behavior using adaptive decomposition \\ [PERSON] et al. [53] & 2025 & Multiple ML models (SVM, XGBoost, KNN) & Real-world transmission data & Not specified & Secondary algorithms on real SGADA/FQD data for grid-scale analysis \\ [PERSON] [54] & 2022 & DWT + Online Sequential ELM (OS-ELM) & Simulated PQD cases & Not specified & Designed for real-time learning; good for streaming classification \\ Present Work & 2025 & Hilbert–Huang Transform (HHT) + Artificial Neural Network (ANN) & IEEE 33-bus system & 94.09 & Strong performance with low computational cost and multiscale sensitivity \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of recent PQD classification methodologies (2020–2025). The current framework was evaluated using synthetic, noise-free voltage signals under controlled scenarios. Although the results demonstrate strong classification accuracy, practical deployment may face challenges such as measurement noise, data incompleteness, or signal distortions. Future research will aim to validate the model on field-acquired datasets and explore alternative neural architectures, such as convolutional or recurrent networks, to enhance generalization under real-world conditions. ## 5 Future Work and Challenges

Based on the results obtained, one of the central future projections is to apply the proposed model to the analysis of short-circuit conditions in distribution networks. This type of event represents one of the most critical scenarios in the operation of electrical systems, and its early detection is key to avoiding severe damage to the infrastructure. Incorporating short circuits as a new disturbance class within the classification system would enable the evaluation of the model's ability to identify high-severity events, accurately differentiating them from other, less severe, transient phenomena. Another relevant aspect of future work will be the validation of the model with real signals obtained directly from operating electrical networks. This approach will allow for the analysis of its behavior under practical conditions characterized by noise, uncertainty, and multiple sources of interference. Additionally, the exploration of more sophisticated neural architectures, such as convolutional networks (CNNs), recurrent networks (RNNs), or hybrid networks, is proposed, which could improve accuracy, reduce response times, and increase the system's robustness to non-ideal or incomplete data.