Advanced Strategies for Substation Asset Management: Leveraging Artificial Intelligence and Predictive Analytics

Abstract

The dependability and resilience of smart grids today are heavily dependent on effective substation asset management and accurate fault identification. Traditional techniques suffer from not being able to handle the complexity, nonlinearity, and high-dimensionality of smart grid data, and hence fault classification in real time is less than optimal. In this study, we propose a hybrid model that combines MLP, LightGBM, and LR to enhance the efficiency, robustness, and accuracy of substation fault detection. The model leverages the capability of deep learning to detect complex, nonlinear patterns and tree-based models to enable effective handling of high-dimensional, structured data. We evaluate the proposed model on the Smart Grid Asset Monitoring the dataset with an accuracy of 98.61%, precision of 99.00%, and recall of 98.66%, which is better than conventional ML and DL approaches. The hybrid system delivers an efficient and scalable real-time solution for substation monitoring towards predictive maintenance and wise decision-making. Our results confirm that integrating heterogeneous modeling techniques can significantly enhance fault classification and detection in smart grid networks, paving the way for even more smarter and trustworthy energy management systems.