Big Data for Low Voltage networks modernization
Odit-e exploits the amount of existing network data to identify potentially stressed areas. Odit-e then refines its diagnosis with a small number of field measurements and proposes the most accurate solutions to make the network evolve towards more efficiency and reliability of supply.
This approach is based on a cloud platform that hosts expert algorithms, collects data, and provides access to network operation, without requiring interfacing with existing IT systems.
Thanks to advanced data analysis methods, Odit-e develops innovative algorithms that radically transform the modeling of electrical networks: this data-driven approach takes into account the real behavior of the network. Our empirical models have a physical meaning, and the results obtained have much greater accuracy than those obtained in a conventional way.
This digital network is a very powerful tool, which opens many opportunities. In order to efficiently support network managers / owners in facing the energy transition, Odit-e has built three offers, entirely dedicated to Low Voltage networks: impact prediction, state estimation, and precise cartography.
Odit-e brings factual elements, based on the real state of the network, to target the constraint areas. This enables investment optimization and their reorientation toward other zones ignored by traditional tools.
The accurate modeling of the network also limits the uncertainties and thus enables better network exploitation while improving the reliability of supply for the subscribers.
Renewable production integration
Thanks to its innovative network modeling, our algorithms offer a large range of solutions such as optimal rebalancing of constraint feeders, network reconfiguration, or targeted and localized reinforcement.
Odit-e estimates the production capacity for the coming years as well as the optimal way to connect future production.
Non- technical losses reduction
Operating costs reduction
Knowledge of the actual state of the network helps prevent faults related to overloads and imbalances, and therefore to switch from curative maintenance to predictive maintenance. This also improves the reliability of supply and the lifetime of the assets.
Improving the network knowledge saves more than 50% on power restoration time by reducing fault location time and anticipating recovery patterns. This enables capitalization on field knowledge and experience of intervention crews.
The solutions implemented by Odit-e are non-intrusive and progressive.
They apply to specific portions of the network, and can be generalized to the entire network, if relevant.
They allow state estimation over time, evolving with new constraints, urban planning, and strategic investments.
Presentation from Philippe, the CEO
Odit-e sensors installation