Grid Distribution Fault Occurrence and Remedial Measures Prediction/Forecasting through Different Deep Learning Neural Networks by Using Real Time Data from Tabuk City Power Grid

Abstract

Modern societies need a constant and stable electrical supply. After relying primarily on formal mathematical modeling from operations research, control theory, and numerical analysis, power systems analysis has changed its attention toward AI prediction/forecasting tools. AI techniques have helped fix power system issues in generation, transmission, distribution, scheduling and forecasting, etc. These strategies may assist today’s large power systems which have added more interconnections to meet growing load demands. They make it simple for them to do difficult duties. Identification of problems and problem management have always necessitated the use of labor. These operations are made more sophisticated and data-intensive due to the variety and growth of the networks involved. In light of all of this, the automation of network administration is absolutely necessary. AI has the potential to improve the problem-solving and deductive reasoning approaches used in fault management. This study implements a variety of artificial intelligence and deep learning approaches in order to foresee and predict the corrective measures that will be conducted in response to faults that occur inside the power distribution network of the Grid station in Tabuk city with regard to users. The Tabuk grid station is the source of the data that was gathered for this purpose; it includes a list of defects categorization, actions and remedies that were implemented to overcome these faults, as well as the number of regular and VIP users from 2017 to 2022. Deep learning, the most advanced method of learning used by artificial intelligence, is continuing to make significant strides in a variety of domain areas, including prediction. This study found that the main predictors of remedial measures against the fault occurring in the power systems are the number of customers affected and the actual cause of the fault. Consequently, the deep learning regression model, i.e., Gated Recurrent Unit (GRU), achieved the best performance among the three, which yielded an accuracy of 92.13%, mean absolute error (MAE) loss of 0.37%, and root mean square error (RMSE) loss of 0.39% while the simple RNN model’s performance is not up to the mark with an accuracy of 89.21%, mean absolute error (MAE) loss of 0.45% and root mean square error (RMSE) loss of 0.34%. Significance of the research is to provide the maximum benefit to the customers and the company by using different AI techniques.