## 2 Methodology
### Methodology Algorithm
Given the satisfactory results and robust generalization observed through cross-validation, extending the study to alternative classifiers is considered future work. Real-Time Feasibility and Computational Cost

Although real-time deployment was not directly implemented, the methodology was designed with computational efficiency in mind. The average processing time per event, including both Hilbert-Huang decomposition and ANN classification, was approximately 0.13 s using MATLAB on a standard Intel i7-11800H CPU with 16 GB RAM. This runtime supports near real-time monitoring scenarios (e.g., 5 Hz sampling) with minimal optimization. This result aligns with reported empirical runtimes for Hilbert-Huang transform (HHT)-based feature extraction in non-parallelized settings, where typical values range between 0.1 and 0.2 s per signal for similar data lengths [50; 51]. Furthermore, the proposed ANN architecture remains lightweight, with fewer than 100 trainable parameters, enabling fast inference and compatibility with embedded or edge-device platforms in smart grid environments. Comparative Analysis with Existing Methods

To evaluate the results from the implementation of the HHT-derived ANN model, in Table 5, this paper reviews other approaches to power quality disturbance (PQD) classification, focusing on more recent published works, which naturally enables understanding where the gaps in the research lie. The table features the most impactful works published \begin{table}
\begin{tabular}{c c c c} \hline
**Class** & **Precision** & **Recall** & **F1-Score** \\ \hline Normal & 0.95 & 0.93 & 0.94 \\ Sag & 0.94 & 0.96 & 0.95 \\ Swell & 0.92 & 0.89 & 0.90 \\ Interruption & 0.97 & 0.98 & 0.98 \\ \hline \end{tabular}
\end{table} 
Table 4: Per-class performance metrics. Figure 21: Confusion matrix illustrating the classification performance of the proposed ANN model across four event types: normal, sag, swell, and interruption. Diagonal values indicate correct classifications, while off-diagonal values represent misclassifications. Color intensity reflects the normalized frequency of predictions. The model demonstrates strong accuracy, particularly for interruption and normal events. after 2020 that use other signal processing and machine learning techniques like Discrete Wavelet Transform (DWT), Variational Mode Decomposition (VMD), and S-Transform, alongside Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Extreme Learning Machine (ELM) classifiers. While some methods like DWT combined with FPGA-accelerated ELMs reach classification accuracies over 99%, these methods often rely on ideal signal conditions or need specialized equipment. In comparison, the approach presented in this paper attained 94.09% classification accuracy while processing each event in 0.13 s on a standard CPU. This trade-off between accuracy and efficiency highlights the method's potential for real-time application in smart distribution systems. In addition, the application of Hilbert-Huang transform allows for multiscale analysis of changing voltage signals and provides a powerful complement to standard time-frequency decomposition methods. ## 4 Conclusions

The neural network model trained using features obtained with the Hilbert-Huang transform reached a classification accuracy of 94.09% for electrical system disturbance classification. The level of accuracy attained confirms the model is well-suited for the automatic detection of transient events, underscoring its value for proactive power quality monitoring. The architectural model described herein displayed a high level of generalization, performing voltage dip detection and interruption detection, as well as recording overrides of the voltage at 0.2, 0.4, and 0.8 s. In all scenarios studied, the neural network was successful in detecting the beginning, active period, and termination of the event without any false detections.