Machine Learning-Powered Fault Detection System for Smart Grid Equipment with Predictive Analytics

As the backbone of modern business and society, a more sustainable and efficient electrical grid is necessary for efficient energy management. Evaluate and predict stability under different conditions since smart grid stabilization is one of the most crucial qualities that might be utilized to assess the effectiveness of smart grid architecture. For smart grid systems to remain stable, operate efficiently, and provide a steady supply of electricity, reliable defect detection is crucial. This study presents a data-driven approach using a neural network (NN) model for fault detection, leveraging the Smart Grid Stability dataset. The proposed NN model is implemented in a Python-based Jupyter Notebook environment and evaluated using standard performance metrics. The results of the experiment show good classification performance, with 98.02% accuracy, 98.92% precision, 98.03% recall, and 98.47% F1-score. Further analyses (confusion matrix, accuracy/loss curves, and ROC curves) support the model's generalizability and resilience. The NN model performs better than the existing state-of-the-art machine learning models (Logistic Regression, Random Forest, and Gradient Boosting) when compar