Power system restoration based on intentional islanding of microgrids in disaster management using Artificial Neural Networks (ANN)

The Power outages that last for weeks at a time are a result of natural disasters with an extensive influence on electrical networks. To regain power, it is difficult to suppress these effects. Researchers presented an assortment of methods for power restoration using distributed energy resources (DERs). DERs can be produced by using power devices, batteries, diesel generators as well as different sources of renewable energy. The valued distribution system requires DC voltage with low voltage, direct current which also can be delivered by DERs. In proposed research, an effort is taken to restore electricity in the wake of disasters based on deliberate islanding in intelligent architecture, called Artificial Neural Networks (ANN). Traditional power system restoration methods encounter stability issues because renewable DERs operate in an unanticipated manner. Our study highlights the value of intentionally isolating microgrids as a strategy to speed up and stabilize the restoration process. Segmenting the grid allows for the systematic identification and repair of distressed areas while preserving intact portions. This technique reduces the likelihood of further system failures while streamlining the restoration procedure. In the final analysis, compared to an islanding algorithm, the use of ANNs significantly reduced model execution times throughout the restoration process and improved stability. The output of a MATLAB simulation shows the effectiveness of the DER and its control strategy to improve dynamical stability.