## 4 PD Measurement and Analysis
Recognition relies upon statistical algorithms and points out noise and PD clusters, thus obtaining sub-patterns of the global one, each one pertinent to a specific phenomenon. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Measured Values**} & \multicolumn{2}{c}{**XLEP Flat Specimen**} & \multicolumn{2}{c}{**MV Cable with Surface Defect**} & \multicolumn{2}{c}{**MV Cable with Internal Defect**} \\ \cline{2-6}  & \(\text{PDIV}_{\text{AC}}\) [kV] & \(\text{PDIV}_{\text{AC}}\) [kV] & \(\text{PDIV}_{\text{DC}}\) [kV] & \(\text{PDIV}_{\text{AC}}\) [kV] & \(\text{PDIV}_{\text{DC}}\) [kV] \\ \hline Reading-1 & 1.7 & 3.0 & 10.7 & 3.9 & 7.9 \\ Reading-2 & 1.8 & 3.4 & 11.3 & 3.6 & 7.7 \\ Reading-3 & 1.7 & 3.1 & 10.8 & 3.7 & 7.7 \\ Reading-4 & 1.8 & 3.2 & 11.0 & 3.8 & 7.8 \\ Reading-5 & 1.8 & 3.0 & 10.8 & 3.7 & 8.0 \\ Mean measured value & 1.8 & 3.1 & 10.9 & 3.7 & 7.8 \\ Modelled value & 1.7 & 3.3 & 11.1 & 4.0 & 7.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Measured and modelled AC PDIV values of XLPE specimen with test electrode setup as in Figure 1, along with the PDIV values of cable with surface and internal defects in case of AC and DC. Figure 7: Schematic of the experimental setup used to perform AC and DC PD measurements. Test object is either XLPE specimen with test electrode setup or cable with artificial defects. Figure 8: Typical screenshot from analytics software that illustrates how SRI works for XLPE with test electrode configuration, shown in Figure 1, at 1.1 PDIV\({}_{\text{AC}}\); (**a**) global pattern, (**b**) separation map, (**c**) sub-pattern and cluster corresponding to noise, (**d**) sub-pattern and cluster corresponding to PD, and (**e**) recognition (PD or noise) and identification (type of defect generating PD). Figure 9: Typical screenshot from analytics software that illustrates how SRI works for cable with surface defect at 1.1 PDIV\({}_{\text{AC}}\); (**a**) global pattern, (**b**) separation map, (**c**) sub-pattern and cluster corresponding to noise, (**d**) sub-pattern and cluster corresponding to PD, and (**e**) recognition (PD or noise) and identification (type of defect generating PD). As can be seen from Figures 8-10, the PD and noise pulses were successfully separated and recognized properly. The sub-patterns related to one or more PD sources were then identified by means of artificial intelligence (fuzzy logic), whether belonging to internal, surface, or corona discharge phenomenology (in descending order of harmfulness for electrical insulation reliability). Identification is associated with a likelihood degree. As an example, identification (ID) of 80% internal and 20% surface means that the PD phenomenon can be largely ascribed to internal PD, but these are some inherent features that could be also associated, with low probability, with surface discharges. This mimics the way an expert operator generally explains PD patterns. As regards the experiments of Figure 8, in the case of XLPE with the test electrode configuration as in Figure 1, the PD analytics provided PD identification of 1 (i.e., 100% probability) for surface PD, thus confirming the occurrence of surface discharges. In the case of the cable with the surface defect, the screenshot of the PD analytics from the Figure 9 highlights the typology corresponding to the acquired PD pulses of 100% surface discharge. Therefore, it is certain that the designed and manufactured surface defect actually provided surface discharges. Similarly, in the case of the internal defect, the analytics summarized in Figure 10 were obtained. The automatic acquisition and analysis algorithm provides an identification of 100% internal, near PDIV, for the designed and manufactured defect, thus confirming that both design and manufacturing are appropriate for the specific wanted PD phenomenology. Then, a voltage of 5.8 kV AC (which is 1.1 times the PDIV of surface discharges from triple point near semicon tape) was applied across the same internal defect.