Adaptive Evolutionary Algorithm for Dynamic Traveling Salesman Problem Optimization Leveraging Real-Time Gis Data Integration

Applying ML techniques into smart grid architectures has transformed the predictive maintenance to a level that can predict equipment failures before they occur. This approach reduces the power system downtimes and also results to a very low maintenance cost hence make the power systems more reliable and efficient. The paper examines a variety of the supervised, unsupervised, and reinforcement learning approaches appropriate for processing petabytes of data collected through smart grid sensors and monitoring systems. These algorithms enable the study to find out such patterns and abnormal signs that signify that a piece of equipment is likely to break down soon thus preventative measures are taken. They include: higher models that can predict the failures with higher accuracy and also the causes of the failures that can enable the right approach to be made. These studies reveal that for the improvement of the operational reliability of smart grids, it is indispensable to use ML, thereby signalling a paradigm shift from reliability-centred maintenance to reliability enhancement centring on smart maintenance. In this study, we provide the further understanding of how advanced analytical tools can enhance the practical implementation of smart grid, and general aspect of energy industry.