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Harmonic risk estimation on LV networks by machine learning

The development of new appliances might have a significant impact on power quality in low voltage (LV) distribution networks. Enedis, the main French Distribution System Operator (DSO), aims at obtaining a mapping of the harmonic risks in all its LV networks depending on various development scenarios of new appliances. In this context, EDF R&D has designed and developed for Enedis a methodology and a tool based on Machine Learning (ML) techniques to predict the harmonic risk. The ML process is the core of the tool that we developed. It allows to accurately predict the harmonic voltage levels on any LV network following an integration rate of selected power electronics equipment and a selected year. This approach presents many advantages, the main one being that only one hour of calculation time is required to assess the harmonic risk for all LV networks in France. Performing this risk estimation through conventional methods, like frequency scan or EMT software, could not be envisioned in an operational way: it would have meant several years of calculation because of the significant number of LV networks and the large number of simulations to be carried out for each of them.