## 1 Introduction  
Electrified transportation (including aerospace, aviation, and ships), renewable energy generation, and medium voltage (MV) electrical grids have necessitated advanced electrical insulation designs that can improve the reliability and lifespan of these systems [1; 2; 3]. Partial discharges (PD) are unarguably one of the most common reasons for premature breakdown of organic electrical insulation systems [4; 5; 6; 7]. To face the difficulty of reaching insulation system designs that can assure specified life and reliability irrespective of the type of waveform, i.e., AC or DC, and in the presence of PD, one must first understand and model PD phenomena, especially the PD-inception mechanism. Then, the theoretically obtained partial discharge inception voltage (PDIV) needs to be validated by PD measurements performed on the electrical component under investigation. The inclusion of power electronics has modified the type of supply voltage waveform from sinusoidal AC to modulated AC and DC [8; 9]. Since the electric field distribution along/across the insulation defects is significantly different in the case of AC and DC [10; 11; 12; 13], it is essential to validate the PD-inception model by artificial defects created on the tested object. Dealing with diagnostics and condition maintenance based on PD, it is of fundamental importance not only to measure signals that can be associated to PD but even more so to understand (identify) which type of defect generated the detected PD [4; 14; 15]. Diagnostic studies that comprise of a series of assessments ranging from residual life estimation [16] to health condition or health index [17] can be effective following the proper identification of the PD-source typology. It is reasonable to assume that the primary motivation for establishing PD monitoring and condition-based maintenance (CBM) systems is the cost and the complex analysis of PD measurement results, which generally require the assistance of experts. As long as interpretation of PD in terms of understanding their typology and therefore harmfulness is still dependent on experts, long wait periods and often significant costs can be expected in relation to condition maintenance action related to PD measurement or monitoring procedures. Furthermore, PD analysis performed by expert systems such as those utilizing artificial intelligence (AI) can be ineffective if the PD is not separated properly from noise and other sources of PD [18]. Therefore, the focus of any automatic PD-based diagnostic system method must be on effective separation and recognition of PD and noise, followed by proper PD identification. As regards identification, which is directly associated with maintenance action, PD phenomena can consist of surface/interface discharges (e.g., at cable splices) or internal discharges occurring in insulation bulk. In terms of harmfulness, internal discharges cause a faster degradation rate than surface discharges; thus, the former can be related to higher risk in insulation health condition evaluation [19]. In order to check the validity of any approach trying to add the above-mentioned information to PD measurement/monitoring, insulation system objects endowed with replicable artificial defects that are able to provide the desired type of PD, i.e., internal and surface discharges, must be designed and manufactured for the purpose of developing extensive lab experiments. Acquisition and analysis, the latter focused on identification, are all managed automatically by the PD detection system. Defects generating PD are artificially created on a cable, and the way to model and manufacture them is the main contribution of this paper. As known by lab researchers, generating artificial defects in test objects is always a challenge: We propose here an approach based on PD-inception modelling and electric field simulation, which is validated in the paper by PD measurements.