Artificial Neural Network (ANN)-Based Voltage Stability Prediction of Test Microgrid Grid

The Power Grid Initiative is currently engaged in persistent endeavors to convert the conventional power grid into a smart grid with the objective of enhancing the operation of the power system. As per the United States Department of Energy, the contemporary power grid comprises a range of constituents such as information management systems, communication technologies, field devices, and other related components. The optimal functioning of said components yields enhanced power quality, thereby reducing power losses and augmenting the dependability of the power provision. During the operational process, the interdependence of interconnected components can present various challenges, including load forecasting, voltage stability evaluation, and power grid security. The present investigation involves the utilization of an artificial intelligence network (ANN) to facilitate the training and prediction of the nodal voltage level pertaining to the IEEE 4-bus system. Four discrete models have been postulated to authenticate the efficacy of the Artificial Neural Network (ANN) methodology. The findings indicate that the ensemble of models exhibits superior performance compared to other models in forecasting the nodal voltage stability of the aforementioned electrical system. The ensemble model attains the highest accuracy rate of 98.73%. Additionally, the mean squared error and mean absolute error exhibit the best (lowest) values of 0.0095 and 0.0141, respectively. The outcomes obtained indicate the efficacy of the Artificial Neural Network (ANN) methodology, particularly in the ensemble configuration, for predicting the voltage stability of the electrical power grid.