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The power network reconfiguration algorithm with an R modeling approach evaluates its behavior in computing new reconfiguration topologies for the power grid in the context of the Smart Grid. The power distribution network modelling with the R language is used to represent the network and support computation of different algorithm configurations for the evaluation of new reconfiguration topologies. This work presents a reconfiguration solution of distribution networks, with a construction of an algorithm that receiving the network configuration data and the nodal measurements and from these data build a radial network, after this and using a branch exchange algorithm And verifying the best configuration of the network through artificial intelligence, so that there are no unnecessary changes during the operation, and applied an algorithm that analyses the load levels, to suggest changes in the network.
Wireless technologies can support a broad range of smart grid applications including advanced metering infrastructure (AMI) and demand response (DR). However, there are many formidable challenges when wireless technologies are applied to the smart gi
In the next few years, smart farming will reach each and every nook of the world. The prospects of using unmanned aerial vehicles (UAV) for smart farming are immense. However, the cost and the ease in controlling UAVs for smart farming might play an
We propose a novel neural network embedding approach to model power transmission grids, in which high voltage lines are disconnected and reconnected with one-another from time to time, either accidentally or willfully. We call our architeture LEAP ne
Frequency fluctuations in power grids, caused by unpredictable renewable energy sources, consumer behavior and trading, need to be balanced to ensure stable grid operation. Standard smart grid solutions to mitigate large frequency excursions are base
Recent advances in power system State Estimation (SE) have included equivalent circuit models for representing measurement data that allows incorporation of both PMU and RTU measurements within the state estimator. In this paper, we introduce a proba