## 4 PD Measurement and Analysis  
This section shows the validation of the capability of a specific defect to trigger the wanted type of PD (i.e., validation according to the 3 rd leg of the three-leg approach). For this purpose, it is necessary to have a PD measurement system that is not only capable of detecting PD signals coming from the object under test but also should be able to identify which type of defect is generating the PD. The PD system used for this research was developed recently and is fully automatic for both acquisition and analytics [18,23]. The analysis features are based on SRI technology, that is, with the separation of acquired signal having different features and the creation of a multi-dimensional clustering map, recognition of clusters corresponding to noise and PD and noise rejection from the PRPD global pattern, and identification of the typology of PD based on sub-patterns as separated through the clustering map and considering three categories (in descending harmfulness order), i.e., internal, surface, and corona PD. AC PD measurements were performed on XLPE flat specimens by the electrode setup of Figure 1 as the test object, using a signal generator (RIGOL, Portland, OR, USA; DG952, 50 MHz, 250 MSA/s) plus high-voltage amplifier (Matsusada Precision, Otsu, Japan; AMPS series, 0 to \(\pm\)30 kV) setup as the supply (which is able to generate different types of voltage waveforms). A high-frequency current transformer (HFCT) with a large bandwidth of 100 kHz to 50 MHz was employed to sense PD, and the PD pulses were acquired and processed using an automatic PD acquisition and detection system of 10 kHz to 100 MHz bandwidth. The schematic of the experimental setup used to perform AC PD measurements is displayed in Figure 7. Five measurements were carried out to determine \(\mathrm{PDIV}_{\mathrm{AC}}\), and the corresponding values along with mean value are represented in Table 2. As can be seen in Table 2, the mean value of measured \(\mathrm{PDIV}_{\mathrm{AC}}\) is closer to the estimated \(\mathrm{PDIV}_{\mathrm{AC}}\) value. Thus, the estimated surface \(\mathrm{PDIV}_{\mathrm{AC}}\) value in the case of XLPE with the test electrode setup are validated with the help of experimentally determined \(\mathrm{PDIV}_{\mathrm{AC}}\) value. Figure 6: (**a**) Simulated orthogonal electric field across the edge of the cavity and (**b**) tangential electric field along the cable surface from outer semicon tape to cable termination (as shown with red arrow in Figure 5) and PD-inception field obtained from the PD-inception field model (gas (**a**) and surface discharges (**b**)). Then, AC PD measurements were performed on the MV cable specimen of 5 m long as the test object featuring surface and internal defects, using the same test setup as in Figure 7. The results of the measurements (five) that were performed to determine \(\text{PDIV}_{\text{AC}}\) are summarized in Table 2. The mean measured \(\text{PDIV}_{\text{AC}}\) is in accordance with the modelled values in case of the cable with surface as well as internal defects. Figure 8 depicts the typical screenshot of an XLPE flat specimen with the test electrode setup obtained by the innovative SRI automatic PD software (PDNOVA, Seiktron, Weston, FL, USA; version: 1.2.3.2) at 1.1 \(\text{PDIV}_{\text{AC}}\), and Figures 9 and 10 display the screenshots obtained by the same PD software at 1.1 \(\text{PDIV}_{\text{AC}}\) for both types of defects on MV cable. The innovative, fully automatic, PD analytics algorithm is based on SRI logic, i.e., separation, recognition, and identification [18,24]. Separation is based on quantities extracted from pulse shape and projected on a 2-dimensional space by principal component analysis (PCA). Clusters of homogenous pulse characteristics would separate different types of PD sources and noise from PD, assuming that the acquired signal characteristics are partly or totally different (which happens in most cases).