Identification Of Faults In Microgrid using Artificial Neural Networks
DOI:
https://doi.org/10.62643/Keywords:
Artificial Neural Networks (ANN), MATLAB, feedforward neural networkAbstract
This research offers a method to identify microgrid defects using Artificial Neural Networks (ANN). A diesel generator, a solar photovoltaic system, and a wind generator are all included in the microgrid model under consideration. Simulink is used to model both normal operation and fault scenarios for the microgrid. The faults encountered by a distribution line are represented by the simulated fault conditions. The ANN is trained using the fault voltages and currents. Using the Levenberg-Marquardt approach, the feedforward neural network in MATLAB trained effectively. Next, simulated data is used to assess the trained ANN's ability to identify different errors. Using both trained and untrained data, the ANN successfully detects defects in the microgrid.
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