Prediction of Smart Grid Stability Utilizing Machine Learning Algorithms

The smart grid encountered numerous issues when attempting to continually monitor participant usages, bidirectional data flow, and utility costs as a result of the rise in the use of renewable energy sources. Specialists must detect system failure before it occurs and ascertain whether the smart grid is stable in order to achieve a robust, dependable smart grid. Conventionally in a smart grid, data on consumer demand is gathered and centrally evaluated against current supply conditions where consumers are then given the resulting recommended pricing information so they can decide how much to use. Because the entire traditional process is time-sensitive, dynamically evaluating grid stability becomes not only a concern but also an absolute necessity. Power supply and demand need to be balanced in order to maintain grid stability. Thus, machine learning might significantly aid in classifying whether or not the smart grid is stable by applying contemporary data mining approaches. In this research, different machine learning algorithms will be applied to assess and predict grid stability. The algorithms used in this paper are XG-classifier, Random Forest, Neural Network, KNN, SVM. The experiment results show that the XG-classifier achieved the highest performance yielding Classification accuracy, AUC, F1 and Recall of 97.79%, 96.7%, 95.6%, 95.4%, 94.5% respectively.