Optimized smart grid fault detection model using gradient boosting machines

Yirenkyi, George Nana Appiah and Asare, Emmanuel and Amakye, Dickson Ntoni and Tetteh, Lord Anertei and Mensah, Anastasia Akyamaa and Konglo, Alfred Elolo (2025) Optimized smart grid fault detection model using gradient boosting machines. World Journal of Advanced Engineering Technology and Sciences, 15 (1). pp. 1486-1495. ISSN 2582-8266

The evolution of traditional power grids into intelligent, resilient infrastructures has become imperative to address growing energy demands, climate-induced disruptions, and the integration of renewable energy sources. This study presents an AI-enhanced smart grid framework that employs machine learning models to optimize energy forecasting and fault detection, thereby improving grid reliability and operational efficiency. Specifically, the study implements a Gradient Boosting Regressor (GBR) for short-term load forecasting and a Gradient Boosting Classifier (GBC) for real-time fault detection. A balanced dataset, derived through oversampling techniques, ensures robust model training and classification reliability. Experimental results from simulated grid data demonstrate high performance, with the forecasting model achieving a coefficient of determination (R²) of 0.93 and low prediction errors (RMSE = 12.08, MAE = 9.37). The fault detection model attained 96.1% accuracy, 93% precision, and 100% recall for fault classification, resulting in an F1-score of 0.96, comparable or superior to benchmarks in the literature. These results validate the proposed system’s suitability for implementation in developing regions, particularly in Sub-Saharan Africa, where grid instability and outage frequency hinder socioeconomic development. By integrating real-time predictions with edge-level intelligence, this research contributes a scalable and context-aware solution to modernize energy systems in underserved environments. The study concludes by recommending policy and technological pathways for localized adoption of AI in power distribution networks.