## 5 Discussion  
In this section, the authors delve into the benefits and limitations of the visual tool developed for power quality analysis, considering both synthetic and real signals.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Database**} & \multirow{2}{*}{**PQ Event**} & \multicolumn{2}{c}{**System Radar + Decision**} & \multirow{2}{*}{**Boxplot**} \\  & & **Tree Detection (\%)** & & **Detection (\%)** \\ \hline \multirow{8}{*}{Synthetic Quality Disturbances} & Flicker & 100 & 99 \\  & Harmonics & 100 & 100 \\  & Impulsive transient & 99 & 46 \\  & Interruption & 91 & 90 \\  & Notch & 90 & 27 \\  & Oscillatory transient & 90 & 58 \\  & Sag & 97 & 92 \\  & Spike & 98 & 68 \\  & Swell & 91 & 90 \\  & Sag + harmonics & 98 & 97 \\  & Swell + harmonics & 100 & 100 \\ \hline Real-life Power Quality Events & Sag & 96.15 & 76.92 \\  & Impulsive transient & 83.33 & 23.81 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Percentage of event satisfactorily detected by the proposed tool.  
Although clear patterns have been established in synthetic signals corresponding to at least 90% of the events, the presence of noise and distortion of real signals causes the correspondence between the expected behavior patterns and reality to be at least 83.33% for real impulsive transient events, and 96.15% for real sags, confirming a margin of error for the tool. However, the event detection through the proposed system surpasses traditional tools, such as boxplots, in all cases. Furthermore, unlike purely visual methods, the decision tree provides a quantitative system for classifying events.  
Despite alterations due to noise and distortion in real signals, the pattern corresponding to each state remains distinguishable. Although the tool does not provide data on the exact moment an event occurs or begins, it does allow for locating the signal cycle in which the event takes place, as long as the signal lasts a minimum of 10 cycles, therefore providing a range of 0.02 s within which the event clearly appears. However, when it comes to continuous real-time visualization, such frequency (one cycle at 50 Hz) is unnecessary and cognitively inefficient. A more practical implementation would involve processing overlapping windows of 100 cycles (2 s) and displaying only the most altered cycle within that interval. This would ensure meaningful human interpretability while retaining a near-real-time response capability.  
From a computational standpoint, prior evaluations of the algorithm have shown execution times of up to 0.36 s per 1000 data points under favorable conditions, suggesting that near-real-time operation could be achievable with an optimized implementation (e.g., using compiled languages or parallel processing). Moreover, since the analysis is inherently cycle by cycle, the method is structurally well suited to real-time monitoring systems, where visualization or alert generation can be decoupled from raw computation and scheduled at lower, perceptually meaningful frequencies.  
An improvement to further optimize the simplicity of the measurement system could involve the incorporation of a color-coded system. This system would assign colors to the radar chart area based on the optimal behavior of the signal. For example, if an event is detected, the area enclosed in the heptagon could be highlighted in red for quick identification.