Resumo:
The increased insertion of renewable generation, mainly wind and solar, brings new challenges
for the planning and operation of electric power systems due to its dependence on climatic
conditions. In this perspective, this dissertation aims to propose a methodology based on
Artificial Neural Networks (ANN) for pattern recognition and a self-organizing map to assist
in the planning and operation of the power system. In this context, the operational conditions
that can lead the system to breach of voltage limits can be identified, enabling corrective control
actions to be carried out. In addition, the proposed approach is able to identify the specific unit
responsible for driving the system to an unsatisfactory operating condition. For this, the
methodology is tested in a microgrid represented by the modified unbalanced three-phase IEEE
34-bus system, considering the use of wind and solar generation. The data set of satisfactory
and unsatisfactory operating conditions are obtained using the Monte Carlo simulation. For this
purpose, the backward-forward scan power flow is employed. These data are then provided to
ANNs for training, validation and testing. The results obtained indicate a robust methodology
capable of assisting in decision making and determining control actions during the operation of
the system with high insertion of renewables, thus avoiding overvoltages.
