Medium Voltage (MV) electrical assets are no longer just distribution networks, since also electrified transportation is moving towards kV, if not tens on kV, of target operating voltage and renewable power generation, being largely distributed, is also often delivered through MV links. A characteristic aspect of the evolution of MV assets is the type of power supply, which is moving towards a hybrid concept: voltage can be DC or modulated AC, that includes voltage transients, ripple, and harmonics. As regards insulation systems, however, this trend can be fatal for life and reliability, since the type of stress to be withstood, mostly electrical and thermal (but also mechanical and environmental, for example in aerospace or electric vehicles), could be far beyond the reference used for decades for the design of electrical insulation, which has been based on AC sinusoidal electrical stress. The implication is that even good and conservative design could not work anymore to avoid premature breakdown, due both intrinsic and, especially, extrinsic accelerated aging. Hence, besides relying upon the best available design practice, monitoring the health conditions of MV asset components is becoming crucial to get grid/asset working effectively for the whole planned operation life. However, measuring/monitoring diagnostic quantities often relies upon expert interpretation of diagnostic monitoring results, which for matters of cost, data amount and timing, is not feasible for MV assets on a large scale. This paper has the purpose to discuss the feasibility of an innovative, automatic approach to PD monitoring which has the potentiality to allow MV asset components to self-diagnose their health conditions and interact smartly with asset and maintenance managers. The fundamentals of the algorithms developed for such a smart component and examples of its application on electrical and electronics asset components ( as PCB and spacers) are presented in the paper.
