## 3 Material and Methods
### Visual Tool  
To concisely bring together these indicators, a radar chart was selected, wherein each vertex corresponds to a specific indicator, which will show the evolution per cycle of the signal. As part of the normalization process, indicators are meticulously normalized and biased to mitigate any potential issues arising from their diverse scales. As a result, an ideal sinusoidal signal will form a regular heptagon, as depicted in Figure 1a, establishing an ideal pattern. However, when the signal is under an event, this structured pattern undergoes distortion, adapting to the fluctuations in the waveform, as exemplified in Figure 1b. A radar chart is generated per cycle, illustrating the progression of HOS and CF cycle by cycle, as well as a single SNR value per signal and a THD value per 10 cycles. In order to optimally cover the entire spectrum of PQ's phenomena analyzed, the axes corresponding to skewness, kurtosis, fifth-order statistics, crest factor and SNR follow a linear function, while SOS and THD are governed by a logarithmic function. This distinction is based on the distribution properties of each indicator: SOS and THD typically span a wide range of values with a strong skew toward the upper end, while their nominal values lie closer to the lower bound of their scale. A logarithmic scale enhances resolution in their most informative ranges. In contrast, the remaining indicators vary more symmetrically around their nominal values, for which a linear scale provides clearer visual interpretation. The axles' limits are specified in Table 1.  
Furthermore, the functions governing the visualization of each axis are based on conditions that allow for both the display of all event types and the maintenance of a regular heptagonal silhouette in the absence of disturbances. On the one hand, for all indicators, large outlier values are biased toward the limits of each axis, so the outliers are detected without impairing the visualization of other, less significant events. On the other hand, indicators like SNR, whose ideal value is 80 dB or higher, or THD, where harmonic presence is not considered below 8%, do not have a single optimal or nominal value. Therefore, to ensure an optimal radar display with a regular shape, SNR values greater than 80 dB are shown as 80 dB on the radar chart. Similarly, THD values below 8% will be represented as 8%. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Axis** & **Upper Limit** & **Lower Limit** \\ \hline Sk & 0.31 & \(-\)0.31 \\ K & 1.80 & 1.20 \\ FOS & 0.17 & \(-\)0.17 \\ SOS & 10.63 & 0 \\ CF & 2.51 & 0.32 \\ SNR & 160 & 0 \\ THD & 22 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Axles’ limits. Figure 1: Radar chart from: (**a**) an ideal sinusoidal signal, (**b**) an altered signal. The ideal shape is highlighted in red for clarity. To support the adequacy of these visualization decisions, a sensitivity analysis was conducted using synthetic signals. For selected events, multiple radar charts were simulated with different scaling functions, and the configuration that offered the best performance across the studied events was ultimately selected. Figure 2 illustrates two representative comparisons: in Figure 2a, a radar chart with axis ranges based on the maximum values observed in the dataset is compared to the optimized model under a notch event; in Figure 2b, a chart with more compressed axes is compared to the optimized model under Gaussian noise (40 dB). The results showed that excessively wide axis ranges can visually suppress distortions caused by events such as notch, rendering them indistinguishable. Conversely, overly narrow ranges tend to amplify irrelevant fluctuations due to noise, especially in real measurements, potentially leading to false detections. Moreover, they may obscure the evolution of indicators during an event. Additionally, boxplots are used to visualize the information extracted from each signal.