Nowadays Distribution System Operators (DSOs) typically struggle in having control and knowledge of their low voltage grids. They measures very few points of the power network, leaving most part of it unobserved. Measurement devices are rare and expensive.

ANNSE performs a low cost, low effort and (potentially) real time state estimation of the downstream LV grid with a low number of available measurements by means of an AI-powered algorithm trained on simulation-generated synthetic grid data.

It is well-known nowadays that DSOs typically struggle in having control and knowledge of their LV grids. Especially at distribution level, the DSO only measures very few points of the power network, leaving most part of its networks unobserved. Measurement devices are rare and expensive.

At the moment, most DSOs do not actively monitor the low-voltage parts of their grids. Usually, grid monitoring is in place only for medium-voltage grids. It is not an economically viable option to build up the “smart grid” by deploying a large quantities of measuring devices, therefore new different approaches must be found.

The proposed technology finds its application whenever:

There is the need to automate and monitor the power grid at low voltage level.

There is interest in developing the smart grid at low voltage level. Grid observability is the first step towards the realization of the smart grid.

ANNSE performs a low cost, low effort and (potentially) real time state estimation of the downstream LV grid with a low number of available measurements by means of an AI-powered algorithm trained on simulation-generated synthetic grid data.